code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common ... | 100 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
# flake8: noqa
# Lint as: python3
lowerCAmelCase__ : Optional[Any] =[
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_... | 101 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
"""simple docstring"""
from math import sqrt
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : D... | 102 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
"""simple docstring"""
from typing import Any
def snake_case ( lowerCAmelCase_ ) -> list[Any]:
if not input_list:
return []
_snake_case = [input_list.count(lowerCAmelCase_ ) for value in input_list]
_snake_case = max(lowerCAmelCase_ ) # Gets the ... | 103 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common im... | 104 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 105 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase_ ( lowerCAmelCase__ : NDArray[floataa] , lowerCAmelCase__ : NDArray[floataa] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int , ) -> list[float]... | 106 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
'''simple docstring'''
import random
def _SCREAMING_SNAKE_CASE ( __snake_case : int , __snake_case : float , __snake_case : bool = False ):
_A = {i: [] for i in range(__snake_case )}
# if probability is greater or equal than 1, then generate a compl... | 107 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
__a: List[Any] = [0, 2, 4, 6, 8]
__a: Union[str, Any] = [1, 3, 5, 7, 9]
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
if remaining_length == 0:
if digits[0] == 0 or di... | 108 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a = _LazyModule(_... | 109 |
from __future__ import annotations
UpperCAmelCase__ = list[tuple[int, int]]
UpperCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0,... | 117 | 0 |
def a ( A__ ) -> list:
'''simple docstring'''
if len(A__ ) < 2:
return collection
def circle_sort_util(A__ , A__ , A__ ) -> bool:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
if low == high... | 35 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class a_ ( a ):
def lowerCAmelCase( self : Tuple , UpperCAmelCase__ : float ):
"""simple docstring"""
... | 598 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
def lowerCAmelCase_ (lowercase__ : float , lowercase__ : int ) -> float:
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowercase__ ) , lowercase__ )
return number - int(lowercase__ )
if __name_... | 668 |
# 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 lowerc... | 117 | 0 |
'''simple docstring'''
from typing import Any
import numpy as np
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> bool:
'''simple docstring'''
return np.array_equal(SCREAMING_SNAKE_CASE__ , matrix.conjugate().T )
def _UpperCamelCase ( S... | 638 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
import numpy as np
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self ) -> Tuple:
UpperCamelCase :Optional[Any] = (0, 0)
UpperCamelCase :str = None
UpperCamelCase :List[str] = 0
... | 658 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 117 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/bit-50": "htt... | 11 |
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... | 117 | 0 |
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 importlib_metadata
SCREAMING_SNAKE_CASE__ ... | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
f... | 316 |
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_device
fr... | 117 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : Opti... | 656 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {"processing_layoutxlm": ["LayoutXLMPr... | 117 | 0 |
from functools import lru_cache
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Dict = 2
A_ : Any = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_lowerCAmelCase )
if n ... | 569 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
"""simple docstring"""
from string import ascii_uppercase
a = {str(ord(c) - 5_5): c for c in ascii_uppercase}
def lowercase (snake_case__ : int , snake_case__ : int ) -> str:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
... | 169 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca ... | 35 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add... | 598 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mo... | 668 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 638 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder imp... | 658 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# f... | 11 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( lowerCamelCase ):
def __init__( self , __lowerCAmelCase=None , **__lowerCAmelCase... | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase_ ( ):
__lowerCamelCase = [randint(-10_00 , 10_00 ) for i in range(10 )]
__lowerCamelCase = randint(... | 316 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
"""simple docstring"""
def lowercase ( UpperCamelCase : list , UpperCamelCase : int , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 ):
"""simple docstring"""
A__ : Dict =right or len(UpperCamelCase ) - 1
if left > r... | 656 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils im... | 569 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase (snake_case__ : str , snake_case__ : Union[s... | 169 |
from __future__ import annotations
UpperCAmelCase__ = list[tuple[int, int]]
UpperCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0,... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ :str = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxConfig',
],
}
... | 35 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
def a_ ( __magic_name__ = 10**9 ) -> int:
"""simple docstring"""
snake_case : int = 1
snake_case : List[Any] = 2
snake_case : List[str] = 0
snake_case : Optional[Any] = 0
... | 598 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_Upp... | 668 |
# 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 lowerc... | 117 | 0 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase__ = get_logger(__name__)
class snake_case__ ( enum.Enum ):
"""simple docstring... | 638 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableTran... | 658 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __A ( A ):
'''simple docstring'''
def __lt__(self , A ) -> Tuple:
"""simple docstring"""
... | 11 |
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... | 117 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowerCamelCase_ :
a__ = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} )
a__ = field(
de... | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
_UpperCamelCase : Optional[int] =[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_UpperCamelCase : Any =[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCamelCase_ ( A_ ):
__lower... | 316 |
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_device
fr... | 117 | 0 |
"""simple docstring"""
__A : Optional[Any] = {}
def lowercase ( UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
# if we are absent twice, or late 3 consecutive days,
# no further pr... | 656 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {"processing_layoutxlm": ["LayoutXLMPr... | 117 | 0 |
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : int = int(_lowerCAmelCase )
# Initialize Result
A_ : Any = []
# Traverse through all denomination
for denomination in reversed(_lowerCAmelCase ):
# Find de... | 569 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
"""simple docstring"""
import math
a = 1_0
a = 7
a = BALLS_PER_COLOUR * NUM_COLOURS
def lowercase (snake_case__ : int = 20 ) -> str:
'''simple docstring'''
lowerCAmelCase = math.comb(snake_case__ , snake_case__ )
lowerCAmelCase = ... | 169 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ :Optional[int] = logging.get_logger(__name__)
a_ :List[str] ... | 35 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
re... | 598 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
from __future__ import annotations
def lowerCAmelCase_ (lowercase__ : list[int] ) -> bool:
'''simple docstring'''
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 668 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
'''simple docstring'''
from typing import Any
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : Any ) -> List[str]:
"""simple docstring"""
snake_case : str ... | 638 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
from __future__ import annotations
from cmath import sqrt
def _A ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' )
UpperCamelCase ... | 658 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase :... | 11 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffus... | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , _snake_case ):
"""simple docstring"""
__lowerCamelCa... | 316 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
A__ : str =str(bin(UpperCamelCase ... | 656 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
A_ : Union[str, Any] = name.repla... | 569 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_m... | 169 |
from __future__ import annotations
UpperCAmelCase__ = list[tuple[int, int]]
UpperCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0,... | 117 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
a_ :Optional[Any] = datasets.logging.get_logger(__name__)
a_ :Dict = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel\'s P... | 35 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
class a_ :
def __init__( self : Tuple , UpperCAmelCase__ : int ):
"""simple docstring"""
snake_case : Union[str, Any] = size
snake_case : List[Any] = [0] * size
snake_case : Any = [0] *... | 598 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, ... | 668 |
# 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 lowerc... | 117 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPr... | 638 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
UpperCamelCase :Union[str, Any] = ArgumentParser(
description=(
'''PyTorch TPU distributed trai... | 658 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 117 | 0 |
'''simple docstring'''
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
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__... | 11 |
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... | 117 | 0 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
SCREAMING_SNAKE_CASE__ : str ... | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_co... | 316 |
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_device
fr... | 117 | 0 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
return number | (1 << position)
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstrin... | 656 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {"processing_layoutxlm": ["LayoutXLMPr... | 117 | 0 |
from ... import PretrainedConfig
_lowerCAmelCase = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class _UpperCAmelCase ( _lowerCamelCase ):
a = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a = '''nezh... | 569 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
a = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
a = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
a = [2, 4, 1, 5]
a = len(train_data)
a... | 169 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class lowercase :
def __init__( self : Tuple , _lowercase : str , _lowercase : List[Any] , _lowercase : Union[str, Any] , _lowercase : Tuple , _lowercase : Dict , _lo... | 35 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
import os
from datetime import datetime as dt
from github import Github
_a : Optional[int] = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def a_ ( ... | 598 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
def lowerCAmelCase_ (lowercase__ : int = 60_08_51_47_51_43 ) -> int:
'''simple docstring'''
try:
lowerCAmelCase__ = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' ... | 668 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
'''simple docstring'''
# 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 ... | 638 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT models at https://huggingface... | 658 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCAmelCase (__A , __A , __A = "x" , __A = 10**-10 , __A = 1 , ):
"""simple docstring"""
_a = symbols(__A)
_a ... | 11 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
impo... | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase_ ( A_ ):
__lowerCamelCase = int(number**0.5 )
return number == sq * sq
def lowerCamelCase_ ( A_ , A_ , A_ , A_ ... | 316 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 656 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
from math import isqrt, loga
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Dict = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,_lowerCAmelCase ,_lowerCAmelCase ):
... | 569 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
"""simple docstring"""
import warnings
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 impor... | 169 |
from __future__ import annotations
UpperCAmelCase__ = list[tuple[int, int]]
UpperCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0,... | 117 | 0 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a_ :Tuple = False
class lowercase ( unittest.TestCase ):
pass
@slow
@requir... | 35 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_model... | 598 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers impor... | 668 |
# 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 lowerc... | 117 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
snake_case : Optional[Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case ... | 638 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder imp... | 658 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 117 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
lowercase_ = [
["attention", "attn"],
... | 11 |
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... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : Tuple = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CO... | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , _snake_case ):
"""simple docstring"""
__lowerCamelCase = []
self.adli... | 316 |
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_device
fr... | 117 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : List[str] = {
"facebook/xmod-b... | 656 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {"processing_layoutxlm": ["LayoutXLMPr... | 117 | 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 = logging.get_logger(__name__)
_lowerCAmelCase = {
"""facebook/data2... | 569 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : int = 3 , snake_case__ : int = 7 , snake_case__ : int = 1_000_000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 1
for current_denominator in range(1 , ... | 169 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def a ( A__ , A__=1_0_0_0 ) -> List[str]:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
SCREAMING_SNAKE_CASE__ : O... | 35 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
import numpy as np
def a_ ( __magic_name__ , __magic_name__ ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , __magic_name__ , (alpha * (np.exp(__magic_name__ ) - 1)) )
if __name__ == "__main__":
import doctest
... | 598 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import P... | 668 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class snake_case__ ( __SCREAMING_SNAKE_CASE ... | 638 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogpt""": ["""BioGptTo... | 658 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A = 1_000):
"""simple docstring"""
_a = 3
_a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return... | 11 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n titl... | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 316 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
"""simple docstring"""
from __future__ import annotations
__A : Optional[Any] = list[tuple[int, int]]
__A : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, ... | 656 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
from manim import *
class _UpperCAmelCase ( _lowerCamelCase ):
def _lowerCamelCase ( self ):
A_ : Optional[Any] = Rectangle(height=0.5 , width=0.5 )
A_ : int = Rectangle(height=0.46 , width=0.46 ).set_stroke(... | 569 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( _a ):
de... | 169 |
from __future__ import annotations
UpperCAmelCase__ = list[tuple[int, int]]
UpperCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0,... | 117 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ :List[str] = logging.get_logger(__name__)
a_ :List[str] = {
'google/bigbird-roberta-base': 'https://huggingface... | 35 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from d... | 598 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
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