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 |
|---|---|---|---|---|
"""simple docstring"""
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(a__ , int(b / 2 ) ) * actual_power(a__ , int(b / 2 ) )
else:
return ... | 196 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ... | 665 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transforme... | 573 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 0 |
from math import sqrt
def _UpperCAmelCase ( a : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 654 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils imp... | 448 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ ={
'''configuration_electra''': ['''ELE... | 616 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 0 |
def lowercase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def lowercase_ ( ):
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
... | 381 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_id... | 531 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 0 |
'''simple docstring'''
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 OptionalDependencyN... | 533 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ) -> bool:
if len(a__ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('A... | 366 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 0 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREA... | 297 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 618 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, On... | 196 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 | 0 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def lowerCamelCase ( lowerCamelCase : str):
if not sentence:
return ""
A_ : int = dict(zip(a__ , a__))
return lower_to_upper.get(sentence[0] , sentence[0]) + sente... | 665 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ :List[Any] = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 618 | 0 |
"""simple docstring"""
lowercase = 8.314_462 # Unit - J mol-1 K-1
def UpperCAmelCase ( A : float , A : float , A : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid i... | 573 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=Fals... | 654 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 0 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict', [
SplitDict(),
SplitDict({'train': SplitInfo(name='train', num_bytes=1337, num... | 448 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 618 | 0 |
"""simple docstring"""
from __future__ import annotations
class lowerCamelCase__ :
def __init__( self : Union[str, Any] , A_ : Tuple , A_ : int ):
'''simple docstring'''
__lowercase , __lowercase = ... | 616 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 0 |
import numpy
# List of input, output pairs
lowerCamelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCamelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase__ = [2, 4, ... | 381 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.ut... | 618 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is... | 531 |
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_simplify,
require_tf,
... | 618 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
if len(a__ ) == 0:
return False
_SCREAMING_SNAKE_CASE : Optional[Any] = len(a__ ) // 2
if a_list[m... | 533 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 0 |
'''simple docstring'''
import os
from math import logaa
def lowerCamelCase( SCREAMING_SNAKE_CASE_ = "base_exp.txt" ) -> int:
A_ = 0
A_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a__ ) ,a__ ) ) ):
A_ , A_ = list(ma... | 366 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 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
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : Optional[A... | 297 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 0 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as impo... | 196 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int):
if p < 2:
raise ValueError("""p should not be less than 2!""")
elif p == 2:
return True
A_ : Optional[Any] = 4
A_ : Tuple = (1 << p) - 1
for _ in ra... | 665 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 0 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class ... | 573 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 0 |
def _UpperCAmelCase ( a : int , a : int ):
while a != 0:
snake_case__ , snake_case__ = b % a, a
return b
def _UpperCAmelCase ( a : int , a : int ):
if gcd(a__ , a__ ) != 1:
... | 654 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCamelCase : Optional[int] = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 448 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 0 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
... | 616 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTowe... | 381 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extra... | 531 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 0 |
'''simple docstring'''
from __future__ import annotations
class lowercase__ :
'''simple docstring'''
def __init__( self , __snake_case = 0 ):
_SCREAMING_SNAKE_CASE : List[str] = key
def UpperCAmelCase_ ( self ... | 533 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 0 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] , _lowercase : Tuple , _lowercase : List[Any] ... | 366 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import Generatio... | 297 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 618 | 0 |
"""simple docstring"""
import argparse
import json
import subprocess
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = (
f"""curl -H \"Accept: application/vnd.... | 196 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spec... | 665 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ :List[Any] = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 618 | 0 |
"""simple docstring"""
def UpperCAmelCase ( A : list[int] , A : list[int] , A : int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(a__ ) )
... | 573 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmR... | 654 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import... | 448 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 618 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
... | 616 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 0 |
def lowercase_ ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
snake_case__ : List[Any] =int(a__ )
if n_element < 1:
snake_case__ : Dict =ValueError('''a should be a positive number''' )
raise my_error
snake_case__ : Tuple =[1]
sna... | 381 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.ut... | 618 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case( metaclass=UpperCAmelCase ):
__snake_case: str = ['speech']
def __init__(self : List[Any] , *a : List[str] , **a : Optional[Any] ) -> Optional[i... | 531 |
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_simplify,
require_tf,
... | 618 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 533 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = False ,) -> tuple[int, float, str]:
A_ = cipher_alphabet or [chr(a__ ) for ... | 366 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .mode... | 297 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 0 |
"""simple docstring"""
from __future__ import annotations
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAK... | 196 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ = logging.getLogger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
''... | 665 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = 'Encod... | 573 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from .... | 654 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: int ) -> list[int]:
"""simple docstring"""
__a = [True] * limit
__a = False
__a = False
__a = True
... | 448 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 0 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
UpperCAmelCase__ =datasets.logging.get_logger(__name__)
UpperCAmelCase__ ='''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},... | 616 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}
... | 381 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 0 |
'''simple docstring'''
def _A ( UpperCAmelCase ,UpperCAmelCase ):
'''simple docstring'''
return base * power(a__ ,(exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
low... | 531 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 0 |
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_devi... | 533 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
A_ =... | 366 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : int = 50 ):
snake_case__ : int = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
r... | 297 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 618 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = Counter()
for base in range(1 , max_perimeter + 1 ):
for... | 196 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 | 0 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttentio... | 665 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ :List[Any] = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 618 | 0 |
"""simple docstring"""
lowercase = '''
# 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
'''
... | 573 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from ... | 654 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 0 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase : Union[str, Any] ... | 448 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 618 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class __SCR... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
class __SCREAMING_SNAKE_CASE :
def __init__( self , __lowerCAmelCase ):
# we need a list not a string, so do something to change the type
UpperCamelCase__ = arr.split(""",""" )
def _lowerCamelCase ( self ):
... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...ut... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
def is_in_circle(a__ :float , a__ :float ) -> bool:
UpperCamelCase__ ... | 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
import math
import qiskit
def _UpperCamelCase (a__ :int = 1 , a__ :int = 1 , a__ :int = 1 ):
"""simple docstring"""
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
or isinstance(a__ , a__ )... | 619 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird i... | 619 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _UpperCamelCase (a__ :int , a__ :Optional[Any]... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 1 |
import re
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(a__ , a__ ):
return match.string == phone
return Fals... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
... | 619 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
from __future__ import annotations
import math
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives,... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 619 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 1 |
def _UpperCamelCase (a__ :int = 200_0000 ):
"""simple docstring"""
UpperCamelCase__ = [0 for i in range(n + 1 )]
UpperCamelCase__ = 1
UpperCamelCase__ = 1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 619 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 |
# Copyright 2023 The HuggingFace Inc. 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 required b... | 619 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hug... | 619 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, req... | 619 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra... | 619 |
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_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
raise Optiona... | 619 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase__ = False
class __SCREAMING_SNAKE_CASE ( unittest.T... | 619 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _UpperCamelCase (a__ :Tuple , a__ :Dict ):
"""simple docstring"""
UpperCamelCase__ = k_s... | 619 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 619 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils im... | 619 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CA... | 619 | 1 |
from itertools import permutations
def _UpperCamelCase (a__ :tuple ):
"""simple docstring"""
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
... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = "▁"
UpperCamelC... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
from ... import PretrainedConfig
UpperCamelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Tuple = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird i... | 619 | 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 FlaxAutoModel
... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
f... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 1 |
from math import sqrt
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
assert isinstance(a__ , a__ ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase__ = True
# 0 and 1 are none pri... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
def ... | 619 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"xlm-mlm-en-2048": "https://huggingfac... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.js... | 619 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRET... | 619 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase (a... | 619 |
# Copyright 2023 The HuggingFace Inc. 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 required b... | 619 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParse... | 619 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 | 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_configuration_common impor... | 619 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 | 1 |
import os
import sys
import unittest
UpperCamelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backe... | 619 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 | 1 |
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