code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
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_torch_available, is_vision_available
... | 21 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class A_ ( lowerCAmelCase_ ):
def __init_... | 22 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.... | 23 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 0 |
def lowerCamelCase__ ( snake_case_ : int ) -> str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
__snake_case = len(bin(snake_case_ )[3:] )
__snake_case = bin(abs(snake_case_ ) - (1 <<... | 24 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_c... | 25 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( snake_case_ ):
_A : Optional[int] = []... | 26 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 0 |
'''simple docstring'''
from string import ascii_uppercase
__lowercase : List[Any] = {str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if isinstance(_SCREAMING_SNAKE_CASE , ... | 27 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 28 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageProcessingSavi... | 29 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def a ( *snake_case__: Optional[Any] ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = ... | 30 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__S... | 31 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-n... | 32 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 0 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
log... | 33 | '''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : str = logging.get_logger(__name__)
_A : str = [
['''a... | 229 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _a ( __a ):
__a : List[Any] = CustomTokenizer
pass
| 34 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( ... | 229 | 0 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = "sshleifer/bart-tiny-rand... | 35 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_snake_case = "."
if __name__ == "__main__":
_snake_case = os.path.join(REPO_PATH, "utils/documentation_tests.txt")
_snake_case = []
_s... | 36 | '''simple docstring'''
import argparse
import gc
import json
import os
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
... | 229 | 0 |
'''simple docstring'''
# Copyright 2022 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
... | 37 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
fo... | 229 | 0 |
UpperCAmelCase_ : Optional[int] = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader impor... | 38 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 0 |
# using dfs for finding eulerian path traversal
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None )-> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = (path or []) + [u]
for v in graph[u]:
... | 39 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
... | 40 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNA... | 229 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_A : List[str] ='''Usage of script: script_name <size_of_canvas:int>'''
_A : List[str] =[0] * 100 + [1] * 10
... | 41 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A : int = True
except (ImportError, ModuleNotFoundError):
_A : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quie... | 229 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __A ) -> 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 ) ):
matrix... | 42 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase =... | 229 | 0 |
import argparse
import json
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 import Accele... | 43 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 0 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ,_lowerCamelCase : int ) -> list[list[int]]:
_lowerCAmelCase : list[list[int]] = []
create_all_state(1 ,_lowerCamelCase ,_lowerCa... | 44 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ ... | 45 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 0 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_ar... | 46 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diff... | 47 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 0 |
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
from t... | 48 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__snake_case :Optional[Any] = logging.get_logg... | 49 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from trans... | 50 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 0 |
snake_case_ : Optional[int] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def A (__A : bytes ) -> bytes:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F... | 51 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 0 |
import sys
from collections import defaultdict
class A__ :
def __init__( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = []
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return self.... | 52 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 0 |
'''simple docstring'''
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Optional[int] , __A : list[list[int]] ):
__UpperCamelCase = TypeError(
'Matrices must be formed from a list of ... | 53 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 0 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class UpperCamelCase_ ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def UpperCAmelCase_ ( UpperCAmelCase__ : Tuple ) -> str:
__SCREAMING_SNAKE_CASE = PartialStat... | 54 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 0 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __snake_case ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str]=1 ):
if n_shave_prefix_segments >= 0:
retur... | 55 | '''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : str = logging.get_logger(__name__)
_A : str = [
['''a... | 229 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spe... | 56 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( ... | 229 | 0 |
"""simple docstring"""
from math import sqrt
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 0
for i in range(1 , int(sqrt(_UpperCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(_UpperCamelCase ):
total += i + n... | 57 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 0 |
'''simple docstring'''
lowercase_ = 65_521
def lowerCamelCase ( __lowerCamelCase : str ) ->int:
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 0
for plain_chr in plain_text:
_SCREAMING_SNAKE_CASE = (a + ord(__lowerCamelCase )) %... | 58 | '''simple docstring'''
import argparse
import gc
import json
import os
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
... | 229 | 0 |
def UpperCamelCase ( __lowerCamelCase : list ):
if any(not isinstance(__lowerCamelCase , __lowerCamelCase ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__lowerCamelCase ) ):
... | 59 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
fo... | 229 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
snake_case__ : List[Any] = '''examples/'''
snake_case__ : int = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init... | 60 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 0 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CON... | 61 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 62 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNA... | 229 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ : Tuple = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'toke... | 63 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A : int = True
except (ImportError, ModuleNotFoundError):
_A : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quie... | 229 | 0 |
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
if not sentence:
return ""
_snake_case : str = dict(zip(snake_case__ , snake_case__ ) )
ret... | 64 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase =... | 229 | 0 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
UpperCamelCase__ = namedtuple('covid_data', 'cases deaths recovered')
def lowerCAmelCase_ ( __A = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
... | 65 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
__a = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation ... | 66 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"vocab_file": "vocab.json",
"tokenizer_... | 67 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_toke... | 68 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 0 |
"""simple docstring"""
import socket
def UpperCAmelCase ( ) -> List[str]:
snake_case_ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case_ = socket.gethostname()
snake_case_ = 12312
sock.connect((host, ... | 69 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
A__ : int =logging.get_logger(__name__)
class UpperCAmelCase ( snake_case_ ):
def __init__( self : List[str... | 70 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 0 |
def A ( a_ ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
__UpperCamelCase : Any =sorted(string.lower() )
return len(a_ ) == len(set(a_ ... | 71 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 0 |
"""simple docstring"""
lowerCAmelCase__ = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def snake_case_ ( A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = {'''*''': op.mul, '''/''': op.truediv, '''+... | 72 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a =logging.get_logger(__name__)
a ={"""vocab_file""": """spiece.mo... | 73 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_lowercase = 3
def _snake_case ( snake_case__ : int ):
print('Generating primitive root of p' )
while True:
A = random.randrange(... | 74 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 0 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a_ : int = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:... | 75 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 0 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Optional[int] = []
lowercase__ : Optional[Any] = set({'(', '[', '{'} )
lowercase__ : List[str] = set({')', ']', '}'} )
lowercas... | 77 | '''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : str = logging.get_logger(__name__)
_A : str = [
['''a... | 229 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class ... | 78 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( ... | 229 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ =... | 79 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
UpperCamelCase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _UpperCamelCase ( __A = 100 ) ... | 80 | '''simple docstring'''
import argparse
import gc
import json
import os
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
... | 229 | 0 |
"""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/lic... | 81 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
fo... | 229 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case_ : Optional[Any] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try... | 83 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embe... | 84 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNA... | 229 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : List[Any] = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not i... | 85 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A : int = True
except (ImportError, ModuleNotFoundError):
_A : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quie... | 229 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configurat... | 86 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase =... | 229 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCamelCase = False
class snake_case_ ( unittest.TestCa... | 87 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 0 |
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
__lowerCAmelCase : Tuple = 0b101_100_111_110_110_010_010_000_011_110_11... | 88 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 0 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
... | 89 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_a... | 90 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCAmelCase_ : Optional[Any] = logging.getLogger(__name__)
U... | 91 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 0 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
UpperCamelCase__ = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class a__ :
... | 92 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slo... | 93 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 94 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __lowerCAmelCase :
_lowercase : Optional[Union[str, Path]] = None
_lowercase : bool = False
_lowercas... | 95 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 0 |
"""simple docstring"""
import numpy as np
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : List[str] = int(np.ceil((x_end - xa) / h ) )
_lowerCamelCase ... | 96 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__snake_case ... | 97 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 0 |
"""simple docstring"""
lowerCAmelCase__ : Tuple = 'Tobias Carryer'
from time import time
class snake_case :
"""simple docstring"""
def __init__( self : Dict ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : Union[str, Any] ,lower... | 98 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExt... | 99 | '''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : str = logging.get_logger(__name__)
_A : str = [
['''a... | 229 | 0 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 100 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( ... | 229 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase__... | 101 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """vocab.txt""", """tokenize... | 102 | '''simple docstring'''
import argparse
import gc
import json
import os
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
... | 229 | 0 |
def UpperCamelCase( __UpperCamelCase : float ,__UpperCamelCase : int ):
if digit_amount > 0:
return round(number - int(__UpperCamelCase ) ,__UpperCamelCase )
return number - int(__UpperCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decim... | 103 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
fo... | 229 | 0 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/l... | 104 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a : Dict = 0
a : Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, ... | 105 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studi... | 106 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNA... | 229 | 0 |
from math import sqrt
def __magic_name__ ( A : int ):
'''simple docstring'''
a = 0
for i in range(1, int(sqrt(A ) + 1 ) ):
if n % i == 0 and i != sqrt(A ):
total += i + n // i
elif i == sqrt(A ):
total += i
return total - n
... | 107 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A : int = True
except (ImportError, ModuleNotFoundError):
_A : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quie... | 229 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCAmelCase__ = (3, 9, -11, 0, 7, 5, 1, -1)
lowerCAmelCase__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class SCREAMING_SNAKE_CASE__ :
... | 108 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase =... | 229 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
... | 109 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 0 |
'''simple docstring'''
__snake_case = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__snake_c... | 97 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase (UpperCAmelCase__ ):
"""simple docstring"""
def _snake_case ( self ):
return [
{"col_1": 3, "col_2": "a"},
... | 177 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niel... | 146 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNe... | 186 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ = list[tuple[int, int]]
SCREAMING_SNAKE_CASE__ = [
[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, 0, 0],
[0, 0,... | 325 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__lowerCamelCase : List[str] = logging.get_logger(__name__)
class __snake_case ( UpperCAmelCase__ ):
def __init__( self : List[Any] , *... | 219 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : Any ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = min(snake_case_ ) # min() finds the minimum value
lowerCAmelCase_ :List[str] = max(snake_case_ ) # max() fin... | 84 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 0 |
"""simple docstring"""
def _lowerCamelCase( a = 1_0_0_0 ):
__a = 2**power
__a = str(snake_case_ )
__a = list(snake_case_ )
__a = 0
for i in list_num:
sum_of_num += int(snake_case_ )
return sum_of_nu... | 261 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _a ( UpperCAmelCase__ ):
@require_torch
def A ( self : Tuple ):
... | 34 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def UpperCamelCase__ ( lowercase__ : int = 100_0000 ):
snake_case : Optional[Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
snake_case : Optio... | 148 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 0 |
class __magic_name__ :
'''simple docstring'''
def __init__( self, lowercase_ ) -> List[str]:
"""simple docstring"""
a__ =arr.split(''',''' )
def _UpperCAmelCase ( self ) -> str:
"""simple docstring"""
a... | 188 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 0 |
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