code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
__lowerCamelCase : Dict = {}
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] ) -> int:
"""simple docstring"""
if late == 3 or absent == 2:
... | 219 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""shi-labs/nat-mini-in1k-224""": "... | 68 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 39 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 121 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 0 |
"""simple docstring"""
class _UpperCAmelCase:
def __init__( self , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = val
_UpperCamelCase = None
_UpperCamelCase = None
def UpperCAmelCa... | 194 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
raise RuntimeError('''CUDA out of memo... | 279 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 0 |
from dataclasses import dataclass
from typing import 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 .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, ... | 216 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 218 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 0 |
def _UpperCamelCase ( snake_case__ ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
__UpperCAmelCase : Tuple = sorted(string.lower() )
return len(__l... | 157 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __a():
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION... | 158 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import... | 212 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( snake_case__):
... | 39 | 0 |
# flake8: noqa
# Lint as: python3
SCREAMING_SNAKE_CASE :str = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_... | 15 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,... | 219 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_i... | 68 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
... | 39 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple d... | 121 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 0 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase( snake_case__ ):
... | 194 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 | 0 |
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = len(__lowerCAmelCase )
for i in range(__lowerCAmelCase ):
for j in range(i + 1 , __lowerCAmelCase ):
if numbers[j] < numbers[i]:
... | 279 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 1_0, '''max_num_jobs''': 1}, [range(1_0 )]),... | 216 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 218 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
... | 157 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : Optional[Any] = 10**12 ):
'''simple docstring'''
_lowerCAmelCase = 1
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = 1
while numerator <= 2 * min_total - 1:
prev_numerato... | 158 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 39 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():... | 212 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 0 |
import random
from typing import Any
def UpperCAmelCase ( a_ ) -> list[Any]:
"""simple docstring"""
for _ in range(len(__lowerCAmelCase ) ):
__A = random.randint(0 , len(__lowerCAmelCase ) - 1 )
__A = random.randint(0 , len(__lowerCA... | 15 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__lowerCamelCase : Any = datasets.logging.get_logger(__name__)
__lowerCamelCase : Any = '''\
@InProceedings{moosavi2... | 219 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # ... | 68 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 39 | 0 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAm... | 121 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> List[str]:
"""simple docstring"""
if not head:
return True
# split the list to two parts
_UpperCamelCase , _UpperCamelCase = head.next, head
while fast and fast.ne... | 194 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 0 |
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : List[str] = abs(__lowerCAmelCase )
snake_case_ : Any = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowerCamelCase_ ( ... | 279 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 0 |
from __future__ import annotations
class UpperCamelCase__ :
def __init__(self : Any , snake_case_ : List[str] , snake_case_ : Union[str, Any] ):
__a , __a : int = text, pattern
__a , __a : Dict = len(snake_case_ ), len(snake_case_ )
de... | 216 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase_( _snake_case : Optional[int] ):
"""... | 218 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,... | 157 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Any ):
'''simple docstring'''
return base * power(__lowerCAmelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of... | 158 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 0 |
from sklearn.metrics import recall_score
import datasets
lowerCamelCase__ = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negat... | 212 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( snake_case__):
... | 39 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] ,A : Union[str, Any] ):
__A = data
__A = None
class UpperCAmelCase... | 15 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 0 |
import os
import sys
__lowerCamelCase : Union[str, Any] = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModel... | 219 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> bool:
'''simple docstring'''
A__ = [int(__lowerCAmelCase ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(__lowerCAmelCase ) == 4 and all(0 <= int(__lowerCAmelCase ) <= 2_5_4 for ... | 68 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
... | 39 | 0 |
def lowerCamelCase__ ( a ) -> int:
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_A: List[Any] = grid[... | 121 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 0 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_t... | 194 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 | 0 |
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Optional[Any] = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCam... | 279 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 0 |
import os
import pytest
from attr import dataclass
lowercase__ ='us-east-1' # defaults region
@dataclass
class UpperCamelCase__ :
_SCREAMING_SNAKE_CASE : str = 42
_SCREAMING_SNAKE_CASE : List[str] = "arn:aws:iam::558105141721:role/sagemaker_execution_... | 216 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_model... | 218 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class _snake_case ( snake_case__ ):
def __init__( self: List[str] , *__lowerCamelCase: Optional[int] , **__lowerCamelCase: int ) -> List[str]:
super().__init__(*__lowerCamelCase , **__lowerCamel... | 157 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 0 |
'''simple docstring'''
# 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 Ba... | 158 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 39 | 0 |
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_extraction_common import Seq... | 212 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 0 |
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
__A = len(bin(__lowerCAmelCase )[3:] )
__A = bin(abs(__lowerCAmelCase ) - (1 << binary_number_length) )[3:]
... | 15 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 | 0 |
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 : Tuple = {
'''junnyu/roformer_... | 219 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 0 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dime... | 68 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 39 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=snake_case__ ):
'''simple docstring'''
__UpperCamelCase : Any = ['''sentencepiece''']
def __init__( self : Optional[Any] , *lowerCAmelCase_ : Optional[int] , **l... | 121 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( __snake_case, ... | 194 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 279 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 216 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
... | 218 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slo... | 157 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 0 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models... | 158 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 0 |
# 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 requir... | 212 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( snake_case__):
... | 39 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def UpperCAmelCase ( a_ ) -> list[str]:
"""simple docs... | 15 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 0 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_... | 219 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class a__ ( snake_case__ ):
"""simple docstring"""
__lowerCamelCase = 'MCTCTFeatureExtractor'
__lowerCamelCase = 'AutoTokenizer'
def __init__( self , ... | 68 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
... | 39 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigM... | 121 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""... | 194 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 | 0 |
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 (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
S... | 279 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 0 |
from collections import Counter
from timeit import timeit
def __UpperCamelCase ( lowerCAmelCase__ : List[str] = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def __UpperCamelCase ( lowerCAmelCase__ : List[... | 216 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 | 0 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 218 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 0 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _UpperCamelCase ( ) -> Tuple:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 157 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den... | 158 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 39 | 0 |
from functools import reduce
lowerCamelCase__ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
""... | 212 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 0 |
from __future__ import annotations
def UpperCAmelCase ( a_ ) -> list[int]:
"""simple docstring"""
__A = 2
__A = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowerCAmelCase )
if n > 1... | 15 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class __snake_case :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {}
def __a ( ... | 219 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 0 |
from collections.abc import Generator
from math import sin
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> bytes:
'''simple docstring'''
if len(__lowerCAmelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
A__ = b""
for i... | 68 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 39 | 0 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER... | 121 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 0 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
_a = datasets.logging.get_logger(__name__)
_a = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
... | 194 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 0 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __lowerCA... | 279 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler... | 216 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 0 |
import numpy as np
_lowerCAmelCase : Optional[Any] = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class __magic_name__ :
def __init__( self ) ... | 218 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 0 |
from math import pi, sqrt, tan
def _UpperCamelCase ( snake_case__ ) -> float:
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def _UpperCamelCase ( snake_c... | 157 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 0 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 158 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 0 |
from __future__ import annotations
lowerCamelCase__ = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}... | 212 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( snake_case__):
... | 39 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase ( snake_c... | 15 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 0 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple = 60_08_51_47_51_43 ) -> int:
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE__ = int(__lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or ... | 219 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 0 |
# 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 by ap... | 68 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
... | 39 | 0 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 121 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( __snake_case, __snake_case = None, __snake_case = None, __snake_case = False, ) -> tuple[int, float, str]:
"""simple docstring"""
_UpperCamelCase = ciphe... | 194 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import... | 279 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 0 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowercase__ =yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: "Dataset Card for X" # Fi... | 216 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 | 0 |
from ... import PretrainedConfig
_lowerCAmelCase : Any = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class __magic_name__ ( snake_case__ ):
SCREAMING_SNAKE_CASE = NEZHA_PRETRAINED_CONFIG_ARCHI... | 218 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 0 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _snake_case ( snake_case__ , unittest.TestCase ):
lowerCamelCase__: Tuple = ... | 157 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRL... | 158 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 39 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = "... | 212 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 0 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 15 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 | 0 |
from __future__ import annotations
import unittest
from transformers import 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, random_attention_mask
from ...test_... | 219 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMS... | 68 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 39 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
UpperCAmelCase__ : List[str] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj'... | 121 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/... | 194 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class ... | 279 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 216 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = 4_2
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
_lowerCAmelCas... | 218 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 0 |
from __future__ import annotations
from typing import Any
def _UpperCamelCase ( snake_case__ ) -> int:
if not postfix_notation:
return 0
__UpperCAmelCase : Union[str, Any] = {"+", "-", "*", "/"}
__UpperCAmelCase : Optional[int]... | 157 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_SCREAMING... | 158 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> list:
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
lowerCAmelCase__ : List[Any] = gray_code_sequence_string(__lowerCAmelCase )
#
# ... | 212 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( snake_case__):
... | 39 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.key", "attention.se... | 15 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to ha... | 219 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 0 |
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