code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from maths.prime_check import is_prime
def __A ( __lowerCamelCase ) -> int:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
a = f'Input value of [number={number}] must be an integer'
raise TypeError(__lowerCamelCase )
if is_prime(__lowerC... | 347 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 1 |
__UpperCamelCase : int = "Alexander Joslin"
import operator as op
from .stack import Stack
def __A ( __lowerCamelCase ) -> int:
a = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
a = Stack()
a = S... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase :... | 347 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 347 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self :List[Any] ):
'''simple do... | 347 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 1 |
def __A ( __lowerCamelCase ) -> "list[int]":
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
a = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
a = 1
if upper_limit > 0:
a = 1
# Rec... | 347 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 1 |
import os
import sys
import unittest
__UpperCamelCase : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy... | 347 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_c... | 347 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transf... | 347 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, ... | 347 | 1 |
def __A ( __lowerCamelCase = 200_0000 ) -> int:
a = [0 for i in range(n + 1 )]
a = 1
a = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __lowerCa... | 347 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
UpperCamelCase__ = None
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
... | 347 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 347 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 1 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .d... | 347 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
f... | 347 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(che... | 347 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 1 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __magic_name__ , unitte... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 1 |
from maths.prime_factors import prime_factors
def __A ( __lowerCamelCase ) -> int:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
a = f'Input value of [number={number}] must be an integer'
raise TypeError(__lowerCamelCase )
if number < 1... | 347 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2... | 347 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterM... | 347 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__UpperCamelCase : list[int] = [ord(letter) for letter in string.... | 347 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__UpperCamelCase : List[str] = "__DUMMY_TRANSFORMERS_USER__"
__UpperCamelCase : Optional[int] = "Dummy User"
__UpperCamelCase : ... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 1 |
import os
from collections.abc import Iterator
def __A ( __lowerCamelCase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(__lowerCamelCase ):
a = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filen... | 347 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__UpperCamelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
__UpperCamelCase : List[Any] = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metri... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__UpperCamelCase : Optional[Any] = "src/diffusers"
# Matches is_xxx_available()
__UpperCamelCase : Optional[int] =... | 347 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 1 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
a = str(bin(__lowerCamelCase ) )
binary_number += "0" * shift_amount
return binary_number
... | 347 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 1 |
from PIL import Image
def __A ( __lowerCamelCase ) -> Image:
a , a = image.size
a = 0
a = image.load()
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
a = pixels[j, i]
mean += p... | 347 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.m... | 347 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 347 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets i... | 347 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 1 |
def __A ( __lowerCamelCase = 50 ) -> int:
a = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_le... | 347 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, ... | 347 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 347 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 1 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 1 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
a = (boundary[1] - boundary[0]) / steps
a = boundary[0]
a = boundary[1]
a = mak... | 347 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Optional[int] = {
"t5-small": "https://huggingface.... | 347 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
... | 347 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_uti... | 347 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Union[str, Any] = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERN... | 347 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As co... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 1 |
import cmath
import math
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> complex:
a = math.radians(__lowerCamelCase )
a = math.radians(__lowerCamelCase )
# Convert voltag... | 347 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2... | 347 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ... | 347 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__... | 347 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCame... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__UpperCamelCase : List[str] = 4
__UpperCamelCase : List[Any] = 3
class __lowerCAme... | 347 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 1 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> int:
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(__lowerCamelCase ):
print(f'{i}\t\t{d}' )
def __A ( __lowerCamelCase , ... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requ... | 347 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
def... | 347 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Op... | 347 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __lowerCAmelCase :
UpperCamelCase__ = 42
UpperCamelCase__ ... | 347 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
UpperCamelCase__ = ['''sentencepiece''']
def __init__( self :Tuple , *__magic_name__ :List[str] , **__magic_name__ :List[Any] ):
... | 347 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, ... | 347 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__UpperCamelCase : Union[str, Any] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHI... | 347 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 1 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets... | 347 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 1 |
from typing import Any
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> list:
_validation(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCa... | 347 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[int] = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XL... | 347 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
def __A ( __lowerCamelCase=None , __lowerCamel... | 347 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionCo... | 347 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available(... | 347 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__UpperCamelCase : str = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerC... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2... | 347 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers... | 347 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 1 |
class __lowerCAmelCase :
def __init__( self :str , __magic_name__ :str = "" , __magic_name__ :bool = False ):
'''simple docstring'''
a = {}
# A node will be a leaf if the tree contains its word... | 347 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import ... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __A ( __lowerCamelCase ) -> None:
a , a = analyze_text(__lowerCamelCase )
a = list(""" """ + ascii_lowercase )
# what i... | 347 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to incl... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def __A ( __lowerCamelCase="ro" , __lowerCamelCase="en" , __lowerCamelCase="wmt16" , __lowerCamelCase=None ) -> None:
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 347 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
"junnyu... | 347 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 1 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__UpperCamelCase : List[Any] = "src/transf... | 347 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 1 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = ''''''
UpperCamelCase__ = (
None # pr... | 347 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __A ( *__lowerCamelCase ) -> Tuple:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
a = list(__lowerCamelCase )
for i ... | 347 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weig... | 347 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, ... | 347 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__( ... | 347 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 1 |
def __A ( __lowerCamelCase = 1000 ) -> int:
return sum(e for e in range(3 , __lowerCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'{solution() = }')
| 347 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 1 |
from functools import reduce
__UpperCamelCase : Union[str, Any] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
... | 347 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import ... | 347 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerCAmelCase (... | 347 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_a... | 347 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 1 |
__UpperCamelCase : Optional[int] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ... | 347 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class __lowerCAmelCase :
def __init__( self :int ):
'''simple docstring'''
a = []
a = 0
a ... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
f... | 347 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2... | 347 | 1 |
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> float:
a = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise... | 347 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 1 |
__UpperCamelCase : Tuple = range(2, 20 + 1)
__UpperCamelCase : Tuple = [10**k for k in range(ks[-1] + 1)]
__UpperCamelCase : dict[int, dict[int, list[list[int]]]] = {}
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lo... | 347 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__UpperCamelCase : int = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
def __init__( self :Optional[Any] , *__magic_name__ ... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
... | 347 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCamelCase : List[str] = log... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Di... | 347 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__UpperCamelCase : Optional[int] = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targete... | 350 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 0 |
import math
import sys
def __A ( __lowerCamelCase ) -> int:
if number != int(lowercase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input must not be a negative number""" )
if numbe... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Union[str, Any] = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():
... | 352 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Any... | 353 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 0 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_co... | 354 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sql... | 355 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, ... | 347 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def __A ( __lowerCamelCase ) -> list[int]:
if num <= 0:
a = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(__snake_case )
a = [True... | 356 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 0 |
from pathlib import Path
import fire
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
a = Path(__A )
a = Path(__A )
dest_dir.mkdir(exist_ok=__A )
for path in src_dir.iterdir():
a = ... | 357 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 0 |
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
if exponent == 1:
return base
if exponent % 2 == 0:
a = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
ret... | 358 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 0 |
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