code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
_snake_case = get_logger(__name__)
class _a :
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ ... | 659 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_perceiver": ["PERCEIVER_PRETRAIN... | 659 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def snake_case ( _a: list[float] )-> Optional[Any]:
'''simple docstring'''
return np.maximum(0 , _a )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) #... | 659 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependenc... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def snake_case ( _a: Callable[[int | float], int | float] , _a: int | float , _a: int | float , _a: int = 100 , )-> float:
'''simple docstring'''
l... | 659 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 1 |
"""simple docstring"""
import argparse
import struct
import unittest
class _a :
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : bytes ):
lowerCamelCase__ = data
# Initialize hash values
lowerCame... | 659 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 1 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 659 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 1 |
"""simple docstring"""
from PIL import Image
def snake_case ( _a: Image , _a: float )-> Image:
'''simple docstring'''
def brightness(_a: int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
... | 659 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int , _a: int , _a: list[list[int]] )-> int:
'''simple docstring'''
def update_area_of_max_square(_a: int , _a: int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
... | 659 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 1 |
"""simple docstring"""
import qiskit
def snake_case ( _a: int , _a: int )-> qiskit.result.counts.Counts:
'''simple docstring'''
lowerCamelCase__ = qiskit.Aer.get_backend('aer_simulator' )
lowerCamelCase__ = qiskit.Quant... | 659 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
_snake_case = TypeVar("T")
_snake_case = Union[List[T], Tuple[T, ...]]
_snake_case = Union[T, List[T], Dict[str, T]]
_snake_case = Union[str, bytes, os.... | 659 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 1 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _a ( SCREAMING_SNAKE_CASE_ ):
def _UpperCamelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ... | 659 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 1 |
"""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 _a ( unitt... | 659 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 659 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
Sta... | 659 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 659 | 1 |
"""simple docstring"""
class _a :
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str ):
lowerCamelCase__ = name
lowe... | 659 |
"""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
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 1 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 659 | 1 |
"""simple docstring"""
from math import factorial
def snake_case ( _a: int = 20 )-> int:
'''simple docstring'''
lowerCamelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCamelCase__ ... | 659 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 | 1 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def snake_case ( _a: str )-> int:
'''simple docstring'''
lo... | 659 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
... | 659 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TF... | 659 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_snake_case = logging.get_lo... | 659 | 1 |
"""simple docstring"""
_snake_case = {str(digit): digit**5 for digit in range(10)}
def snake_case ( _a: int )-> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_a ) )
def snake_case ( )... | 659 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 1 |
"""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 l... | 659 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 1 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_sna... | 659 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, C... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 1 |
"""simple docstring"""
class _a :
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : str = "" , SCREAMING_SNAKE_CASE__ : bool = False ):
# Mapping from the first character of the prefix of the node
lowerCamelCase__ ... | 659 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 659 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transform... | 659 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( _a: int , _a: int )-> list[list[int]]:
'''simple docstring'''
lowerCamelCase__ = []
create_all_state(1 , _a , _a , [] , _a )
return result... | 659 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 1 |
"""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 diffu... | 659 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 1 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.co... | 659 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_snake_case = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_snake_case = _Laz... | 659 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 1 |
"""simple docstring"""
import operator
def snake_case ( _a: list , _a: bool = False , _a: list | None = None )-> list:
'''simple docstring'''
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = ... | 659 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def snake_case ( _a: ... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def snake_case ( _a: Optional[int] , _a: Any , _a: Optional[int] , _a: Tuple )-> Any:
'''simple docstring'''
... | 659 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 659 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def... | 659 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 1 |
"""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 diffuse... | 659 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPrio... | 659 |
"""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
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as j... | 659 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 659 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE_ )
class _a ( SCREAMING_SNAKE_CASE_ ):
# `task` is not a Class... | 659 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 | 1 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_snake_case = loggi... | 659 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 659 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 659 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_snake_case = logging.get_lo... | 659 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_snake_case = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Tra... | 659 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int = 1 , _a: int = 1000 )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
lowerCamelCase__ = 0
for divide_by_number in range(_a , digit + 1 ):
lowerCamelCa... | 659 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 1 |
"""simple docstring"""
from torch import nn
def snake_case ( _a: Dict )-> List[str]:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
... | 659 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 1 |
"""simple docstring"""
from math import factorial
def snake_case ( _a: int , _a: int )-> int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('Please enter positive integers for n and k where n >= k' )
return factorial(... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 659 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: list[int] , _a: list[int] )-> None:
'''simple docstring'''
lowerCamelCase__ = len(_a )
print('The following activities are selected:' )
# The first activity is always selected
... | 659 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
_snake_case = tuple[int, int]
class _a :
def __init__( self : List[Any] ):
lowerCamelCase__ = []
lowerCamelCase__ = set()
def _UpperCam... | 659 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses... | 659 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common imp... | 659 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 1 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _a ( SCREAMING_SNAKE_CASE_ )... | 659 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: str )-> list:
'''simple docstring'''
if n_term == "":
return []
lowerCamelCase__ = []
for temp in range(int(_a ) ):
series.append(F'1/{temp + 1}' if series else '1... | 659 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE... | 659 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 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.
_snake_case = 10
def snake_case ( _a: int , _a: int , _a:... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def snake_case ( )-> Dict:
'''simple docstring'''
lowerCamelCase__ = HfArgumentParser(_a )
lowerCamelCase__ = parser.parse... | 659 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 659 | 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
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
... | 659 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _a :
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : ... | 659 |
"""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
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils ... | 659 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 659 | 1 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_tor... | 659 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny... | 659 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 659 | 1 |
"""simple docstring"""
import math
class _a :
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
lowerCamelCase__ = n
lowerCamelCase__ = [
... | 659 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_snake_case = logging.get_lo... | 659 | 1 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _a ( SCREAMING_SNAKE_CASE_ ):
a_ : Tuple = CustomTokenizer
pass
| 659 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int , _a: Any )-> Dict:
'''simple docstring'''
lowerCamelCase__ = (boundary[1] - boundary[0]) / steps
lowerCamelCase__ = boundary[0]
lowerCamelCase__ = boundary[1]
l... | 700 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_snake_case = "Usage of script: script_name <size_of_canvas:int>"
_snake_case = [0] * 100 + [1] * 10
random.shuffle(choice)
def snake_ca... | 701 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( snake_case... | 702 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 0 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, Squ... | 703 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 0 |
"""simple docstring"""
class _a :
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ):
lowerCamelCase__ = val
lowerCamelCase__ = None
lowerCamelCase__ = None
def _Upper... | 704 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__ )
class _a ( lowercase__ ):
a_ : List[Any] = field(default='... | 705 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 0 |
"""simple docstring"""
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.na... | 706 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 0 |
from collections.abc import Sequence
def snake_case ( _a: Sequence[float] , _a: bool = False )-> float:
'''simple docstring'''
if not arr:
return 0
lowerCamelCase__ = 0 if allow_empty_subarrays else float('-inf' )
l... | 707 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _a ( __A ):
@require_torch
def _UpperCamelCase ( self : T... | 708 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 0 |
"""simple docstring"""
import os
import string
import sys
_snake_case = 1 << 8
_snake_case = {
'tab': ord("\t"),
'newline': ord("\r"),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY... | 709 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_snake_case = datasets.utils.logging.get_logger(__name__)
class _a ( folder_based_builder.FolderB... | 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_snake_case = logging.ge... | 711 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 659 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 712 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 0 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def snake_case ( _a: str )-> int:
'''simple docstring'''
lowerCamelCase__ = FileLock(str(tmpdir / 'foo.lock' ) )
lowerCamelCase_... | 713 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 659 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
... | 714 |
"""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
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ..... | 715 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 659 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJ... | 716 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 | 0 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def snake_case ( _a: List[Any] )-> Any:
'''simple docstring'''
lowerCamelCase__ = ... | 717 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 | 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, UNetaDCond... | 718 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 659 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, P... | 719 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_snake_case = logging.get_lo... | 659 | 0 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
_snake_case = get_logger(__name__)
class _a :
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_... | 720 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 0 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( _a: int , _a: int )-> Dict:
'''simple docstring'''
if b == 0:
return (1, 0)
(lowerCamelCase__) = extended_euclid(__UpperCamelCase , a % b )
... | 721 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _a ( _lowercase ):
a_ : Dict = '''MCTCTFeatureExtractor'''
a_ : Optional[int] = '''AutoTokenizer'''
... | 700 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common imp... | 701 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 0 |
"""simple docstring"""
import os
import sys
import transformers
_snake_case = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", t... | 702 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 0 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_snake_case = logging.get_logger(__name__)
class _a :
a_ : int = None
@experiment... | 703 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import... | 704 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 0 |
"""simple docstring"""
def snake_case ( _a: int )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [[0 for _ in range(_a )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase__ = 1
fo... | 705 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 0 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( _a: list[int | str] )-> int:
'''simple docstring'''
create_state_space_tree(snake_case__ , [] , 0 , [0 for i in range(len(snake_case__ ) )] )
def snake_... | 706 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 0 |
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.warn(e)
warnings.warn(
"The conv... | 707 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 0 |
"""simple docstring"""
import sys
def snake_case ( _a: int )-> Optional[int]:
'''simple docstring'''
lowerCamelCase__ = len(UpperCAmelCase__ )
lowerCamelCase__ = [[0 for x in range(UpperCAmelCase__ )] for x in range(UpperC... | 708 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 0 |
"""simple docstring"""
import string
from math import logaa
def snake_case ( _a: str , _a: str )-> int:
'''simple docstring'''
lowerCamelCase__ = document.translate(
str.maketrans('' , '' , string.punctuation ) ).replace('\n' , ... | 709 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 0 |
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