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 |
|---|---|---|---|---|
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
Uppe... | 117 |
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 | 1 |
def _a ( a :int , a :int ) -> int:
a = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
a = n - k
# Calculate C(n,k)
for i in range(a ):
result *= n - i
result //= i + 1
return result
def _a ... | 117 |
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 | 1 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is... | 117 |
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 | 1 |
UpperCAmelCase__ = 8.31_4462 # Unit - J mol-1 K-1
def _a ( a :float , a :float , a :float ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UN... | 117 |
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 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( a :int ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def _a ( a :int , a :int , a :int , a :int ... | 117 |
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 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependen... | 117 |
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 | 1 |
from __future__ import annotations
import math
def _a ( a :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return Fa... | 117 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
UpperCAmelCase__ = logging.g... | 117 |
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 | 1 |
from collections import deque
def _a ( a :str ) -> List[Any]:
a = len(a )
a = deque()
a = [False for _ in range(a )]
a = [-1 for _ in range(a )]
a = index_of[:]
def strong_connect(a :str , a :Tup... | 117 |
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 | 1 |
def _a ( a :list ) -> list:
a = len(a )
for _ in range(a ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
a , a = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
UpperCAmelC... | 117 |
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 | 1 |
from __future__ import annotations
from cmath import sqrt
def _a ( a :int , a :int , a :int ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' )
a = b * b - 4 * a * c
a = ... | 117 |
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 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
sl... | 117 |
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 | 1 |
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 ... | 117 |
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 | 1 |
import sys
import turtle
def _a ( a :tuple[float, float] , a :tuple[float, float] ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _a ( a :tuple[float, float] , a :tuple[float, float] , a :tuple[float,... | 117 |
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 | 1 |
from math import pow
def _a ( a :int , a :int , a :int , a :int , a :int , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += ... | 117 |
# 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 | 1 |
def _a ( a :str ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(a ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 117 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_... | 117 |
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 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
... | 117 |
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 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consid... | 117 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
... | 117 |
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 | 1 |
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,
... | 117 |
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 | 1 |
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 AutoProcessor, BertT... | 117 |
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 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xln... | 117 |
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 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowercase_ :
'''simple docstring'''
def __init__( self : Optional[int] , __UpperCAmelCase : Tuple ) ->str:
"""simple do... | 117 |
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 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lt__( self : str , __UpperCA... | 117 |
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 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _a ( ) -> Union[str, Any]:
a = ArgumentParser(
description=(
'''PyTorch TPU distributed training ... | 117 |
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 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProc... | 117 |
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 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 117 |
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 | 1 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 1 |
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... | 117 |
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 | 1 |
# 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
#
# U... | 117 |
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 | 1 |
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
UpperCAmelCase__ ... | 117 |
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 | 1 |
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 lowercase_ ( lowercase ):
'''simple do... | 117 |
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 | 1 |
def _a ( a :int , a :Any , a :str ) -> int:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a , n - 1 , a ) * a) % mod
else:
a = binary_exponentiation(a , n / 2 , a )
re... | 117 |
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 | 1 |
def _a ( a :int = 600_851_475_143 ) -> int:
try:
a = int(a )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' ... | 117 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "h... | 117 |
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 | 1 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
... | 117 |
# 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 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if... | 117 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 1 |
def _a ( ) -> List[str]:
for n in range(1 , 1_000_000 ):
yield n * (n + 1) // 2
def _a ( a :List[Any] ) -> List[Any]:
a = 1
a = 2
while i * i <= n:
a = 0
while n % i == 0:
n //= i
multiplicity += 1
di... | 117 |
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 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {name: getattr(transformers, n... | 117 |
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 | 1 |
def _a ( a :float , a :int ) -> float:
if digit_amount > 0:
return round(number - int(a ) , a )
return number - int(a )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(de... | 117 |
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 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docst... | 117 |
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 | 1 |
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
UpperCAmelCa... | 117 |
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 | 1 |
def _a ( a :int = 3 , a :int = 7 , a :int = 1_000_000 ) -> int:
a = 0
a = 1
for current_denominator in range(1 , limit + 1 ):
a = current_denominator * numerator // denominator
if current_denominator % deno... | 117 |
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 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowercase_ :
'''simple docstring'''
__snake_case = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or pat... | 117 |
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 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
loggi... | 117 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_distilbert": [
"DIST... | 117 |
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 | 1 |
def _a ( a :list ) -> 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 = grid[0]
for row_n... | 117 |
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 | 1 |
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... | 117 |
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 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPas... | 117 |
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 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase__ = "src/diffusers"
# Matches is_xxx_available()
UpperCAmelCase__ = re.compile... | 117 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 117 |
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 | 1 |
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
from ...utils... | 117 |
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 | 1 |
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 lowercase_ :
'''simple docstring'''
... | 117 |
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 | 1 |
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.pipel... | 117 |
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 | 1 |
from torch import nn
def _a ( a :List[str] ) -> Any:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""" )
... | 117 |
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 | 1 |
def _a ( a :str ) -> int:
assert column_title.isupper()
a = 0
a = len(a ) - 1
a = 0
while index >= 0:
a = (ord(column_title[index] ) - 64) * pow(26 , a )
answer += value
power += 1
index -= 1
return ... | 117 |
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 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 117 |
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 | 1 |
import os
def _a ( ) -> Dict:
with open(os.path.dirname(a ) + '''/grid.txt''' ) as f:
a = [] # noqa: E741
for _ in range(20 ):
l.append([int(a ) for x in f.readline().split()] )
a = 0
# right
for i in range(20 ):
for j... | 117 |
# 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 | 1 |
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 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 1 |
def _a ( a :int , a :str , a :Dict , a :List[str] ) -> Union[str, Any]:
# Return True if there is node that has not iterated.
a = [False] * len(a )
a = []
queue.append(a )
a = True
while queue:
a... | 117 |
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 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
UpperCAmelCase__ = datasets.logging.get_logger(__name__)
UpperCAmelCase__ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon... | 117 |
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 | 1 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase__ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def _a ( ) -> ... | 117 |
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 | 1 |
def _a ( a :list[int] ) -> list[list[int]]:
a = []
if len(a ) == 1:
return [nums.copy()]
for _ in range(len(a ) ):
a = nums.pop(0 )
a = permute(a )
for perm in permutations:
perm.append(a )
result.extend(a ... | 117 |
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 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate i... | 117 |
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 | 1 |
import re
def _a ( a :str ) -> bool:
a = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(a , a ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("+918827897... | 117 |
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 | 1 |
def _a ( a :int , a :int ) -> int:
while second != 0:
a = first & second
first ^= second
a = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ = int(inpu... | 117 |
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 | 1 |
def _a ( a :list , a :int , a :int = 0 , a :int = 0 ) -> int:
a = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return s... | 117 |
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 | 1 |
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 |
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 | 1 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime a... | 117 |
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 | 1 |
UpperCAmelCase__ = {}
def _a ( a :int , a :int , a :int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and h... | 117 |
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 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
UpperCAmel... | 117 |
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 | 1 |
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 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 117 |
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 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _a ( a :str , a :complex , a :str = "x" , a :float = 10**-10 , a :int = 1 , ) -> complex:
a = symbols(a )
a = lam... | 117 |
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 | 1 |
from __future__ import annotations
def _a ( a :list[int] ) -> bool:
return len(set(a ) ) == len(a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 117 |
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 | 1 |
from math import factorial
UpperCAmelCase__ = {str(digit): factorial(digit) for digit in range(10)}
def _a ( a :int ) -> int:
if not isinstance(a , a ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
raise ValueError(... | 117 |
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 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 117 |
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 | 1 |
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_modeling_tf... | 117 |
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 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_... | 117 |
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 | 1 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCAmelCase__ = {
"facebook/maskformer-swin-base-ad... | 117 |
# 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 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 117 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 1 |
def _a ( a :int = 1_000 ) -> int:
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 result
if __name__ == "__main__":
print(f"""{solution() = }""")
| 117 |
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 | 1 |
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 import compute_effe... | 117 |
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 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 |
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 | 1 |
def _a ( a :int , a :int ) -> int:
return abs(a ) if a == 0 else greatest_common_divisor(b % a , a )
def _a ( a :int , a :int ) -> int:
while y: # --> when y=0 then loop will terminate and return x as final GCD.
... | 117 |
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 | 1 |
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,
... | 117 |
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 | 1 |
import numpy as np
def _a ( a :np.ndarray , a :float ) -> np.ndarray:
return np.where(vector > 0 , a , (alpha * (np.exp(a ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 117 |
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 | 1 |
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... | 117 |
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 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 117 |
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 | 1 |
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 |
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 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 117 |
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 | 1 |
def _a ( a :float ) -> float:
if edge <= 0 or not isinstance(a , a ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _a ( a :float ) -> float:
if edge <= 0 or... | 117 |
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 | 1 |
from __future__ import annotations
def _a ( a :list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(a ) )... | 117 |
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 | 1 |
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... | 117 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 1 |
# 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
#
# Unless... | 117 |
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 | 1 |
import os
from pathlib import Path
def _a ( ) -> Dict:
from torch.utils.cpp_extension import load
a = Path(a ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
a = [
root / filename
for filename in [
'''vision.cpp''... | 117 |
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 | 1 |
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, a... | 117 |
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 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _a ( a :dict ) -> tuple:
return (data["data"]... | 117 |
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 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( a :str , a :Union[str, Any] , a :str ) -> ... | 117 |
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 | 1 |
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