code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
SCREAMING_SNAKE_CASE_: Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n ... | 1 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Any = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
rais... | 327 | 0 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCamelCase : Union[str, Any] = '.'
if __name__ == "__main__":
lowerCamelCase : Dict = os.path.join(RE... | 2 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
if len(snake_case__ ) < k or k < 0:
raise ValueError('''Invalid Input''' )
A : Any =... | 3 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 327 | 0 |
'''simple docstring'''
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_tens... | 4 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : list[list[int]] = []
snake_case_ : list[int] = []
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = sum(__a )
create_... | 327 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 5 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import... | 327 | 0 |
import unittest
import numpy as np
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = None , ) -> np.ndarray:
__a = np.shape(a__ )
__a = np.shape(a__ )
__a = np.shape(a__ )
if shape_a[0] != shape_b[0]:
__a ... | 6 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, Tens... | 7 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 327 | 0 |
import math
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(SCREAMING_SNAKE_CASE__ )... | 8 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCAmelCase : Dict ... | 9 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 327 | 0 |
from __future__ import annotations
__A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
low... | 10 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig... | 327 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxC... | 11 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 327 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core... | 12 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Union[str, Any] , _A : Any , _A : Dict ) -> Union[str, Any]... | 327 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase : str = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT... | 13 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
while b:
snake_case_ ,snake_case_ : Any = b, a % b
return a
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return a if b == 0 else euclidean_gcd_recursive(__a , a % b )
def... | 327 | 0 |
from __future__ import annotations
_lowerCamelCase : Union[str, Any] = 10
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
"""simple docstring"""
A__ = 1
A__ = max(lowercase_ )
while placement <= max_digit:
... | 14 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.dis... | 327 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCAmelCase ( a_ ) -> Optional[Any]:
... | 15 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 327 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
... | 16 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE ... | 327 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch... | 17 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_SCREAMING_SNAKE_CASE = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE__ ( __a = "https://www.worldometers.info/coronavirus/" ):
snake_case_ ... | 327 | 0 |
class a__ :
def __init__( self : List[str],_A : list ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE_ : List[Any] = max(_A )
SCREAMING_SNAKE_CASE_ : str ... | 18 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_SCREAMING_SNAKE_CASE = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ""... | 327 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A =TypeVar('''T''')
def lowerCamelCase_ ( lowerCamelCase__ ):
return (position - 1) // 2
def lowerCamelCase_ ( lowerCamelCase__ ):
return (2 * position) + 1
def lowerCamelCa... | 19 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
... | 327 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
if index == r:
for j in range(SCREAMING_SNAKE_CASE__ ):
prin... | 20 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
""... | 327 | 0 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[Any]:
_lowercase : Optional[Any] = [1]
for i in range(2 , lowerCamelCase_ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_lowerca... | 21 |
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def UpperCAmelCase_ ( self : str , _A : Optional[Any]=None , _A : List[str]=None , _A : Optional[Any]... | 327 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
... | 22 |
from itertools import permutations
def SCREAMING_SNAKE_CASE__ ( __a ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case_ : Any = [7, 11, 13, 17]
for i, test in enumerate(_... | 327 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Optional[int] = logging.get_logger(__name__)
UpperCamelCase__: str = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class ... | 23 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Any = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
rais... | 327 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case_ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trap... | 24 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 | 0 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, ... | 25 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 327 | 0 |
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(... | 26 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : list[list[int]] = []
snake_case_ : list[int] = []
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = sum(__a )
create_... | 327 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase ():
__a , __a : Union[str, Any] = 9, 14 # noqa: F841
__a : Optional[Any] = [
[0, 1, 4],
... | 27 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import... | 327 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trainin... | 28 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = os.path.dirname(os.pa... | 29 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 327 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging.getLogger(__name__)
... | 30 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import Au... | 31 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 327 | 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_ : str = logging.get_logger(__name__)
UpperCAmelCase_ :... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig... | 327 | 0 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint ... | 33 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 327 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( __a , unittest.TestCase ):
__a : Dict = CTR... | 34 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Union[str, Any] , _A : Any , _A : Dict ) -> Union[str, Any]... | 327 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a ... | 35 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
while b:
snake_case_ ,snake_case_ : Any = b, a % b
return a
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return a if b == 0 else euclidean_gcd_recursive(__a , a % b )
def... | 327 | 0 |
from math import pow
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a soluti... | 36 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.dis... | 327 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_lowerCAmelCase = TypeVar('''T''')
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return (position - 1) // 2
def _SCREAMING_SNA... | 37 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 327 | 0 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( __magic_name... | 38 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE ... | 327 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )-> float | int:
... | 39 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_SCREAMING_SNAKE_CASE = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE__ ( __a = "https://www.worldometers.info/coronavirus/" ):
snake_case_ ... | 327 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common imp... | 40 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_SCREAMING_SNAKE_CASE = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ""... | 327 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Tuple:
lowerCamelCase... | 41 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
... | 327 | 0 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCAmelCase ( _lowerCamelCase ):
# to overwrite ... | 42 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
""... | 327 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomS... | 43 |
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def UpperCAmelCase_ ( self : str , _A : Optional[Any]=None , _A : List[str]=None , _A : Optional[Any]... | 327 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a : Tuple = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'W... | 44 |
from itertools import permutations
def SCREAMING_SNAKE_CASE__ ( __a ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case_ : Any = [7, 11, 13, 17]
for i, test in enumerate(_... | 327 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
... | 45 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Any = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
rais... | 327 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=_UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *lowercase , **lowercase ) -> int:
requires_backen... | 46 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A__ ( A__ ):
def A ( self : Optional[Any] ) -> str:
'''simple docstring'''
return [
{"col_1": 3... | 47 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 327 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAM... | 48 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : list[list[int]] = []
snake_case_ : list[int] = []
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = sum(__a )
create_... | 327 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _A ( unittest.TestCase ):
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_mo... | 49 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import... | 327 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def SCREAMING_SNAKE_CASE ( ) -> List[... | 50 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
... | 51 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 327 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
# Initialise PyTorch model
UpperCame... | 52 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__ : Any =1.054571817E-34 # unit of ℏ : J * s
a__ : List[Any] =3E8 # unit of c : m * s^-1
def lowercase__... | 53 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 327 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGener... | 54 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig... | 327 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( UpperCAmelCase_ : list[float] , UpperCAmelCase_ : list[float] ):
lowerCamelCase_ = sorted(numsa + numsa )
lowerCamelCase_ ,lowerCamelCase_ = divmod(len(UpperCAmelCase_ ... | 55 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 327 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import requi... | 56 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Union[str, Any] , _A : Any , _A : Dict ) -> Union[str, Any]... | 327 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 57 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
while b:
snake_case_ ,snake_case_ : Any = b, a % b
return a
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return a if b == 0 else euclidean_gcd_recursive(__a , a % b )
def... | 327 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 58 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.dis... | 327 | 0 |
def UpperCamelCase ( __lowerCamelCase : int ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("Input value must be a 'int' type" )
return... | 59 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 327 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case__ : str = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''... | 60 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE ... | 327 | 0 |
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 61 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_SCREAMING_SNAKE_CASE = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE__ ( __a = "https://www.worldometers.info/coronavirus/" ):
snake_case_ ... | 327 | 0 |
from collections.abc import Generator
def _UpperCAmelCase ( ):
__UpperCamelCase , __UpperCamelCase =0, 1
while True:
__UpperCamelCase , __UpperCamelCase =b, a + b
yield b
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : ... | 62 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_SCREAMING_SNAKE_CASE = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ""... | 327 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if... | 63 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
... | 327 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 64 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
""... | 327 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class A ( UpperCAmelCase_ ):
def __init__(self : List[str] , *__UpperCAmelCase : List[... | 65 |
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def UpperCAmelCase_ ( self : str , _A : Optional[Any]=None , _A : List[str]=None , _A : Optional[Any]... | 327 | 0 |
"""simple docstring"""
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 ( _lowerCAmelCase , ... | 66 |
from itertools import permutations
def SCREAMING_SNAKE_CASE__ ( __a ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case_ : Any = [7, 11, 13, 17]
for i, test in enumerate(_... | 327 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a__ ( UpperCAmelCase__ ):
... | 67 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Any = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
rais... | 327 | 0 |
from PIL import Image
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Image ) -> Image:
'''simple docstring'''
A__ , A__ = image.size
A__ = 0
A__ = image.load()
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(SCREAMING_SNAKE_CA... | 68 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 | 0 |
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__UpperCame... | 69 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 327 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transf... | 70 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : list[list[int]] = []
snake_case_ : list[int] = []
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = sum(__a )
create_... | 327 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :str = logging.get_logger(__name__)
A_ :Tuple = {
'''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/r... | 71 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import... | 327 | 0 |
"""simple docstring"""
from math import isqrt, loga
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is... | 72 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
import argparse
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
__lowerCamelCase : List[str] = F"{file}_{class_name}_{test_name}"
done... | 73 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 327 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 74 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def a_ ( __snake_case : Union[str, Any] ) -> int:
"""simple docstring"""
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitt... | 75 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 327 | 0 |
from __future__ import annotations
import math
def lowerCamelCase__ ( _a , _a):
if len(_a) != 2 or len(a[0]) != 2 or len(_a) != 2 or len(b[0]) != 2:
raise Exception("Matrices are not 2x2")
SCREAMING_SNAKE_CASE : str = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * ... | 76 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig... | 327 | 0 |
"""simple docstring"""
from math import loga
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
ra... | 77 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 327 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 78 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Union[str, Any] , _A : Any , _A : Dict ) -> Union[str, Any]... | 327 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowercase ( __lowercase , __lowercase = None , __lowercase = None , __lowercase = False , ) -> tuple[int, float, str]:
'''simple docstring'''
_A = cipher_alphabet or [chr(__lowercase ... | 79 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
while b:
snake_case_ ,snake_case_ : Any = b, a % b
return a
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return a if b == 0 else euclidean_gcd_recursive(__a , a % b )
def... | 327 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionP... | 80 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.dis... | 327 | 0 |
"""simple docstring"""
class __A :
"""simple docstring"""
def __init__( self , __A ) -> None:
a =len(__A )
a =[0] * len_array
if len_array > 0:
a =array[0]
for i in range(1 , __A ):
... | 81 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 327 | 0 |
A__ = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
A__ ... | 82 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE ... | 327 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : Any = ... | 83 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_SCREAMING_SNAKE_CASE = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE__ ( __a = "https://www.worldometers.info/coronavirus/" ):
snake_case_ ... | 327 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
... | 84 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_SCREAMING_SNAKE_CASE = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ""... | 327 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_SCREAMING_SNAKE_CASE : Tuple = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 85 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
... | 327 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_C... | 86 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
""... | 327 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCamelCase = pd.read_csv('''sample_data.csv''', header=None)
UpperCamelCas... | 87 |
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def UpperCAmelCase_ ( self : str , _A : Optional[Any]=None , _A : List[str]=None , _A : Optional[Any]... | 327 | 0 |
import math
import os
import sys
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = """"""
try:
with open(A_, """rb""" ) as binary_file:
__magic_name__ = binary_file.read()
for dat in data:
__magic_name__... | 88 |
from itertools import permutations
def SCREAMING_SNAKE_CASE__ ( __a ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case_ : Any = [7, 11, 13, 17]
for i, test in enumerate(_... | 327 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
... | 89 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Any = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
rais... | 327 | 0 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 90 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( Up... | 91 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 327 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
UpperCamelCase__ = logging.getLogger(__nam... | 92 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : list[list[int]] = []
snake_case_ : list[int] = []
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = sum(__a )
create_... | 327 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowercase : Optional[Any] = ["small", "medium", "large"]
_lowercase : Any = "lm_head.decoder.weight"
_lowercase : List[str] = "lm... | 93 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import... | 327 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __lowerCamelCase ( UpperCAmelCase_ : ndarray ):
"""simple docstring"""
return np.dot(UpperCAmelCase_ , UpperCAmelCase_ )
class _snake_case :
def... | 94 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase : str = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
... | 95 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 327 | 0 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Tuple = data
... | 96 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def a ( __a , __a ) -> np.ndarray:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = math.sqrt(__a )
UpperCamelCase__ :Tuple = 1 / (sigma * math.sqrt(2 * math.pi ... | 97 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 327 | 0 |
"""simple docstring"""
from PIL import Image
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(lowerCamelCase ) -> int:
return int(1_2_8 + factor * (c - 1_2_8) )... | 98 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig... | 327 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase : Union[str, Any] = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
lowercase : Union[str, Any] = None
def A_ ( ) -> Dict:
a__ : Dict... | 99 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 327 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
# Checks if the entire collection has been sorted
if len(UpperCamelCase_ ) <= 1 or n <= 1:
return
insert_next(UpperCamelCase_ , n - 1 )
rec_insertion_so... | 100 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Union[str, Any] , _A : Any , _A : Dict ) -> Union[str, Any]... | 327 | 0 |
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