code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
from collections import defaultdict
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , __lowercase):
UpperCamelCase_ = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__lowercase ... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
snake_case__ : List[str] = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@tota... | 23 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 23 | 1 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=UpperCAmelCase__ ):
"""simple docstring"""
A_ = ["""flax"""]
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Optional[Any]:
requi... | 23 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 1 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 1 |
# Function to print upper half of diamond (pyramid)
def _snake_case (__lowercase):
for i in range(0 , __lowercase):
for _ in range(0 , n - i - 1): # printing spaces
print(' ' , end='')
for _ in range(0 , i + 1): # printing stars
... | 23 |
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 _a (... | 23 | 1 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 23 |
import argparse
import os
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 i... | 23 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class _a :
"""simple docstring"""
def __init__( self , _UpperCAmelCase ) -> Optional[Any]:
UpperCamelCase_ = str(id_ )
UpperCamelCase_ = None
UpperCa... | 23 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 1 |
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, TrainingJobAnalytics
from sag... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Tuple = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransfo... | 23 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : List[str] = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-st... | 23 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils impo... | 23 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 1 |
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 transformers.utils import cached_propert... | 23 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 1 |
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.distributed... | 23 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 1 |
from __future__ import annotations
def _snake_case (__lowercase):
UpperCamelCase_ = 2
UpperCamelCase_ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowercase)
if n > 1:
factors.app... | 23 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _a :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=0.2 , _UpperC... | 23 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 1 |
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ ... | 23 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 1 |
snake_case__ : Any = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
snake_ca... | 23 |
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.d... | 23 | 1 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : An... | 23 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 23 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging... | 23 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case__ : Optional[int] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConf... | 23 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 1 |
import numpy as np
class _a :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
UpperCamelCase_ = (0, 0)
UpperCamelCase_ = None
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ ... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_sof... | 23 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 23 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 23 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/... | 23 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robe... | 23 |
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 _a (... | 23 | 1 |
import torch
from torch import nn
class _a ( nn.Module ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=1 , _UpperCAmelCase=False ) -> int:
supe... | 23 |
import argparse
import os
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 i... | 23 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class _a :
"""simple docstring"""
def __init__( self ) -> None:
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = 0
... | 23 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : str = {
... | 23 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case__ : Tuple = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CO... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobe... | 23 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_se... | 23 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAme... | 23 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 1 |
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__ : Optional[Any] = logging.get_logger(__name__)
snake_c... | 23 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch... | 23 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _snake_case (__lowercase = 3):
if isinstance(__lowercase , __lowercase):
raise TypeError('number of qubits must be a integer.')
... | 23 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configurat... | 23 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> List[str]:
... | 23 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case (__lowercase):
def decorator(__lowercase):
UpperCamelCase_ = getattr(__lowercase , 'handle_key' , [])
handle += [key]
setattr(__lowercase , 'handle_... | 23 |
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.d... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Tuple = {"""configuration_plbart""": ["""PLBART_PRETRAINED_C... | 23 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 23 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 1 |
def _snake_case (__lowercase):
UpperCamelCase_ = [1]
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 0, 0, 0
UpperCamelCase_ = ugly_nums[ia] * 2
UpperCamelCase_ = ugly_nums[ia] * 3
UpperCamelCase_ = ugly_nums[ia] * 5
for _ in r... | 23 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fro... | 23 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeniza... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 1 |
def _snake_case (__lowercase = 1000):
UpperCamelCase_ , UpperCamelCase_ = 1, 1
UpperCamelCase_ = 2
while True:
UpperCamelCase_ = 0
UpperCamelCase_ = fa + fa
UpperCamelCase_ , UpperCamelCase_ = fa, f
index += 1
for ... | 23 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 23 | 1 |
import numpy
class _a :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ) -> None:
UpperCamelCase_ = input_array
# Random initial weights are assigned where first argument is the
# number of no... | 23 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : List[str] = logging.get_logger(__name__)... | 23 |
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 _a (... | 23 | 1 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 |
import argparse
import os
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 i... | 23 | 1 |
import gc
import threading
import time
import psutil
import torch
class _a :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
UpperCamelCase_ = psutil.Process()
UpperCamelCase_ = False
def _UpperCAm... | 23 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
snake_case__ : Optional[int] = logging.getLogger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
A_ = """masked_bert"""
... | 23 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 1 |
from ...configuration_utils import PretrainedConfig
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
A_ = """bert-generation"""
def __init__( self , _UpperCAmelCase=50358 , _UpperCAmelCase=1024 , _UpperCAmelCase=24 , _Uppe... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routi... | 23 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Dict = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
... | 23 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : List[Any] = {
"""configuration_roberta""":... | 23 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 1 |
def _snake_case (__lowercase , __lowercase):
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 doctest
... | 23 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 1 |
def _snake_case (__lowercase = 1000000):
UpperCamelCase_ = [i - 1 for i in range(limit + 1)]
for i in range(2 , limit + 1):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , __lowercase):
phi[j] -= phi[j] // i
return sum(p... | 23 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 1 |
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
snake_case__ : Optional[Any] = logging.get_logger(__name__)
... | 23 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class _a ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=1 , _UpperCAmelC... | 23 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ):
"""simple docstring... | 23 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 1 |
import os
def _snake_case ():
UpperCamelCase_ = os.path.dirname(os.path.realpath(__lowercase))
UpperCamelCase_ = os.path.join(__lowercase , 'triangle.txt')
with open(__lowercase) as f:
UpperCamelCase_ = f.readlines()
UpperCamelCase_ ... | 23 |
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.d... | 23 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
# TODO: upload to AWS
snake_case__ : List[str] = {
"""yjernite/retribert-base-uncased""": (
"""https:... | 23 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, 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.num... | 23 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 1 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
A_ = """EncodecFeatureExtractor"""
A_ = (... | 23 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ = ('dense.weight', 'attention.self.query', 'attention.self.ke... | 23 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Dict = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise Opt... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 1 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 23 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRU... | 23 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, Re... | 23 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _a ( enum.Enum )... | 23 |
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 _a (... | 23 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 |
import argparse
import os
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 i... | 23 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
snake_case__ : Union[str, Any] = """__DUMMY_TRANSFORMERS_USER__"""
snake_case__ : Optional[int] =... | 23 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _UpperCAmelCase ( _UpperCAmelCase ) -> Union[str, Any]:
raise NotImplement... | 23 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 1 |
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = [1]
for i in range(2 , __lowercase):
factorials.append(factorials[-1] * i)
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase_ = []
UpperCamelCase_ = list(ra... | 23 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import ... | 23 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 1 |
def _snake_case (__lowercase):
UpperCamelCase_ = 0
for ch in input_str:
UpperCamelCase_ = ord(__lowercase)
UpperCamelCase_ = pow(2 , __lowercase)
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1... | 23 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
snake_case__ : Union[str, Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
snake_case__ : Any = typing.Union[np.floata... | 23 |
import numpy as np
def _snake_case (__lowercase):
return 1 / (1 + np.exp(-vector))
def _snake_case (__lowercase):
return vector * sigmoid(__lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, l... | 23 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 23 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMH... | 23 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Dict:
with open(_UpperC... | 23 | 1 |
from bisect import bisect
from itertools import accumulate
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase):
UpperCamelCase_ = sorted(zip(__lowercase , __lowercase) , key=lambda __lowercase: x[0] / x[1] , reverse=_... | 23 |
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.d... | 23 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _snake_case (__lowercase , __lowercase=() , __lowercase=None , __lowercase="no"... | 23 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 | 1 |
# Copyright 2021 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... | 23 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAu... | 23 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 23 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case (__lowercase):
UpperCamelCase_ = SwinConfig(image_size=192)
if "base" in model_name:
Up... | 23 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 23 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
snake_case__ : int = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Syste... | 23 |
import datasets
from .evaluate import evaluate
snake_case__ : int = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 23 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Optional[A... | 23 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGen... | 23 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = f"""{sampling_rate}"""
UpperCamelCase_ = '1'
UpperCamelCase_ = 'f32l... | 23 |
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 _a (... | 23 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Tuple = """1"""
snake_case__ : Optional[int] = """0"""
snake_case__ : Any = """1"""
snake_case__ : List[str] = ort.SessionOpt... | 23 |
import argparse
import os
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 i... | 23 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_... | 23 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common impor... | 23 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> int:
debug_la... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = iter(__lowercase)
while True:
UpperCamelCase_ = tuple(itertools.islice(__lowercase , __lowercase))
... | 23 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | 1 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UN... | 23 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
... | 23 | 1 |
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