code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> str:
__lowerCAmelCase = len(lowercase )
for i in range(length - 1 ):
__lowerCAmelCase = i
for k in range(i + 1 , lowercase ):
if collection[k] < collection[least]:
... | 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Union[str, Any] = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""B... | 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
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, se... | 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = size
# approximate the overall size of segment tree with given value
... | 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> np.ndarray:
# prepare kern... | 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_a : str = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journ... | 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
a : int
a : TreeNode | None =None
a : TreeNode | None =None
_a : List[str] = nam... | 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'{price_plus_tax(1_0_0, 0.25) = }')
print(f'{price_plus_tax(125.50, 0.05) = }')
| 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> tuple[float, float, float]:
__lowerCAmelCase = point_y / 4 / point_x
__lowerCAmelCase = 2 * normal_gradient / (1 + normal_gradient * n... | 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = 0
while number > 0:
... | 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
_a : Tuple = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, ... | 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 1 |
'''simple docstring'''
from math import isqrt, loga
def _lowerCAmelCase ( lowercase ) -> list[int]:
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercas... | 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> list[int]:
__lowerCAmelCase = len(lowercase )
for i in range(lowercase ):
for j in range(i + 1 , lowercase ):
if numbers[j] < numbers[i]:
__lowerCAmelCase , __lowerCA... | 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 1 |
'''simple docstring'''
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
_a : str = logging.get_logger(... | 689 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 10**12 ) -> int:
__lowerCAmelCase = 1
__lowerCAmelCase = 0
__lowerCAmelCase = 1
__lowerCAmelCase = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
... | 689 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_a : List[Any] =... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 1 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_a : List[Any] = 0B10_11_00_11_11_10_11... | 689 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(lowercase ) * abs(lowercase )
if __name__ == "__main__":
import doctest
doct... | 689 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import nu... | 689 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : List[str] = {"""configuration_fnet""": ["""FNET_PRETRAINED... | 689 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _lowerCAmelCase ( ) -> int:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 1 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_a : Any = logging.get_logger(__name__)
class _UpperCAmelCase ( lowerCAmelCa... | 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
_a : int = get_logger(__name__)
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
_... | 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 1 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if dst_width < 0 or dst_height... | 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase ) -> int:
return 1 if input_a == input_a else 0
def _lowerCAmelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ... | 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 1 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_a : str = {
# 1536-bit
5: {
... | 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Tuple = {
"""configuration_whisper""": [""... | 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torc... | 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class _UpperCAmelCase ( low... | 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
defa... | 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 1 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
_a : Optional[int] = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def ... | 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_t... | 689 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( lowercase ) -> bool:
return np.array_equal(lowercase , matrix.conjugate().T )
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
__lowerCAmelCase = ... | 689 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_conf... | 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
_a : Any = """src/transformers"""
# Matches is_xxx_available()
_a : Any = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_st... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
__lowerCAmelCase = _modexpt(lowercase , exponent // 2 , lowercase ) % modulo_value
re... | 689 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = 88,__SCREAMING_SNAKE_CASE = N... | 689 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def _lowerCAmelCase ( lowercase ) -> np.ndarray:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 *... | 689 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> int | float:
if len(lowercase ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
left >= len(lowercase ... | 689 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 1 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = 9
__lowerCAmelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
... | 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> bool:
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def _lowerCAmelCase ( lowercase ) -> bool:
__lowerCAmelCase = credit_card_number
__lowerC... | 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_a : str = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_a : List[Any] ... | 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 1 |
'''simple docstring'''
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.pipelin... | 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> list[list[float]]:
__lowerCAmelCase = []
for data in source_data:
for i, el in enumerate(lowercase ):
if len(lowercase ) < i + 1:
data_lists.append([] )
data_lists[i].... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
... | 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> list:
if len(lowercase ) < 2:
return collection
def circle_sort_util(lowercase , lowercase , lowercase ) -> bool:
__lowerCAmelCase = False
if low == high:
return sw... | 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _lowerCAmelCase ( lowercase = 100_0000 , lowercase = 10 ) -> int:
__lowerCAmelCase = defaultdict(lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ... | 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 1 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_a : Dict = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def _lowerC... | 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
_a : List[Any] = {
... | 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 1 |
'''simple docstring'''
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.imag... | 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class _UpperCAmelCase ( torch.nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(__SCREAMING_SNAKE_CASE,self ).__init__()
... | 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_a : List[str] = 1_0
def _lowerCAmelCase ( lowercase , lowercase ... | 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.confi... | 689 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Any:
__lowerCAmelCase = f'{file}_{class_name}_{test_name}'
done_test[_id] += 1
wi... | 689 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 1 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def _lowerCAmelCase ( lowercase = 100_0000 ) -> int:
__lowerCAmelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCAmelCase = max(ceil(sqrt(o... | 689 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Tuple ="""SpeechT5FeatureExtractor"""
a : str ="""SpeechT5Tokenizer"""
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
... | 689 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase , lowercase )}
if len(set(lists_lengths.value... | 689 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, requi... | 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : Tuple = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFI... | 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 1 |
'''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 PegasusTokeniz... | 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 1 |
'''simple docstring'''
import string
import numpy
def _lowerCAmelCase ( lowercase , lowercase ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , lowercase )
class _UpperCAmelCase :
a : Tuple =string.ascii_uppercase + string.dig... | 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_a : Tuple = r"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used... | 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_a : str = logging.get_logger("""transformers.models.speecht5""")
def _lowerCAmelCase ( lowercase ... | 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
... | 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> Tuple:
__lowerCAmelCase = ArgumentParser(
description=(
... | 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
... | 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowercase ) -> None:
create_state_space_tree(lowercase , [] , 0 , [0 for i in range(len(lowercase ) )] )
def _lowerCAmelCase ( lowercase , lowercase , ... | 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 1 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
Charac... | 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test... | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''junnyu/roformer_chinese_smal... | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Tuple , __lowerCAmelCase : ... | 2 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase : List[Any] = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Alt... | 3 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
return str(_UpperCAmelCase ) == str(_UpperCAmelCase )[::-1]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
return int(_UpperCAmelCase ) + int(str(_UpperCAmelCase )[::-1] )
def _SCREAMING_SNAKE... | 4 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_lowercase = logging.get_logger(__name__)
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = r"... | 5 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = (DDIMParallelScheduler,)
lowerCamelCase_ = (("eta", 0.0), ("num_inference_steps"... | 6 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( _snake_case : Optional[Any] , _snake_case : Any ) -> Dict:
'''simple docstring'''
_A = a.name
_A = b.name
_A = ''
_A = ''
_A = ... | 7 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...te... | 8 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 9 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "upernet"
def __... | 10 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, 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... | 11 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def UpperCamelCase ( ... | 12 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require... | 13 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
ble... | 14 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( A__ : List[str] , A__ : Optional[Any] , A__ : Optional[int] ):
# Init... | 16 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> typing.Counter[int]:
__A : typing.Counter[int] = Counter()
for base in range(1 ,max_perimeter + 1 ):
for perpendicular in range(a__ ,max_p... | 17 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : Any ):
'''simple docstring'''
_lowerCAmelCase = [0] * len(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = 0
for valu... | 18 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'AutoImagePro... | 19 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 0 |
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