code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils impor... | 292 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 335 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCamelCase__ : Dict = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
... | 486 |
from __future__ import annotations
UpperCamelCase__ : str = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A_( A , A , A , A , A , ):
UpperCAmelCase_ = [
[0 for col in range(len... | 486 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase__ ) )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = ... | 82 |
'''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_torch_available():
... | 667 | 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 UpperCamelCase_( __magic_name__ : Union[dict, list, tuple, torch... | 703 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common impor... | 382 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_A ... | 505 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case :... | 505 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( _UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_Up... | 718 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( _a , _a , _a ):
"""simple docstring"""
_lowerCamelCase ... | 297 | 0 |
"""simple docstring"""
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,
DPMSolverMultiste... | 586 | """simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Optional[An... | 586 | 1 |
'''simple docstring'''
import math
import random
def _lowerCamelCase ( lowercase : float , lowercase : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase_ : Any ... | 521 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ : List[Any] = [
'good first issue',
'feature request',
'wip',
]
def _lowerCamelCase ( ) -> Dict:
_a = Github(os.environ["GITH... | 521 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase : Optional[int] = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "Bl... | 501 |
'''simple docstring'''
__lowerCAmelCase : List[str] ="Alexander Joslin"
import operator as op
from .stack import Stack
def UpperCamelCase ( _lowerCamelCase : str ):
A__ = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
A__ = Stack()... | 440 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( __UpperCAmelCase: Namespace ) -> Tuple:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_d... | 711 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
UpperCAmelCase_ = logg... | 369 | 0 |
_lowerCamelCase : Union[str, Any] = """
# 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
"""
_lowerCa... | 87 | """simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils... | 213 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__a: Dict = logging.getLog... | 711 |
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> list[int]:
_UpperCAmelCase = [0 for i in range(len(__snake_case ) )]
# initialize interval's left pointer and right pointer
_UpperCAmelCase , _UpperCAmelCase = 0, 0
for i in range(1 , len(__sn... | 402 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils impo... | 34 |
'''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... | 533 | 0 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('T')
class A__ ( Generic[T] ):
"""simple docstring"""
def __init... | 503 |
from __future__ import annotations
def _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(SCREAMING_SNAKE_CASE ):
print... | 503 | 1 |
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 jnp
from jax import jit
... | 413 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableTransfor... | 413 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_vision_availab... | 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_devic... | 408 | 0 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext... | 65 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__UpperCAmelCase = '\nArgs... | 65 | 1 |
def __lowerCAmelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] = 0 , UpperCAmelCase__ : int = 0 ) -> Optional[int]:
lowerCamelCase_ = right or len(_snake_case ) - 1
i... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
... | 103 | 0 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lo... | 522 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""distilbert-base... | 314 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( __lowerCamelCase:Any ):
'''simple docstring'''
__magic_name__ = 1
__magic_name__ = 2
while i * i <= n:
__magic_name__ = 0
while n % i == 0:
... | 468 |
"""simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from pa... | 468 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __a( nn.Module ):
"""si... | 30 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 1 |
# Lint as: python3
import itertools
import os
import re
A__ : List[str] = re.compile(r'([A-Z]+)([A-Z][a-z])')
A__ : Optional[Any] = re.compile(r'([a-z\d])([A-Z])')
A__ : int = re.compile(r'(?<!_)_(?!_)')
A__ : int = re.compile(r'(_{2,})')
A__ ... | 703 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : str = "", lowerCamelCase : bool = False ):
'''simple docstring'''
# Mapping from the first character of the prefix of the node
lowercase... | 671 | 0 |
"""simple docstring"""
import math
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return math.pow(SCREAMING_SNAKE_CASE , 2 ) - a
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return 2 * x
def UpperCamelC... | 102 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class snake_case_ ( __lowercase... | 504 | 0 |
'''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, CLIPTokenizer
... | 720 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCAmelCase : List[str] = logging.get_logger('transformers.models.speecht5')
def __UpperCamelCase ( _A : Any... | 646 | 0 |
def lowerCamelCase__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any ):
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j... | 290 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _lowerCAmelCase ( __a ):
_lowercase =4... | 290 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
lowercase : List[str] = logging.get_logger(__name__)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lowercase , ... | 392 |
import collections
import os
import re
from pathlib import Path
lowercase : int = """src/transformers"""
# Matches is_xxx_available()
lowercase : List[str] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowercase : str = re.compil... | 392 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 4 |
"""simple docstring"""
from __future__ import annotations
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_UpperCAmelCase ).json()
def _SCREAMING_SNAKE_C... | 4 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.util... | 456 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowercase_ = logging.get_logger(__name__)
class __a ( SCREAMING_SNAKE_CASE ):
def __init__( self : Optional[Any] , *snake_case_ : List[str] ,... | 456 | 1 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __SCREAMING_SNAKE_CASE ( a__ : Optional[int] ,a__ : Any ) ... | 17 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import F... | 31 | 0 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42
snake_case_ = None
snake_case_ = None
def _a( UpperCamelCase__ ... | 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_... | 665 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1_024 , SCREAMING_SNAKE_CASE=... | 66 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.ut... | 383 | 0 |
import functools
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# Validation
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not all(isinstance(_lowerCamelCase , _lowerCamelCase ) for day in days ):
raise ValueError("The parameter days shou... | 718 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 37 | 0 |
import math
from collections.abc import Callable
def UpperCamelCase ( __magic_name__ : Callable[[float], float] , __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
lowercase__ = xa
lowercase_... | 15 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"facebook/deit-base-distilled-patch16-22... | 475 | 0 |
"""simple docstring"""
import sys
import turtle
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : tuple[float, float] , SCREAMING_SNAKE_CASE : tuple[float, float] ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def UpperCAmelCa... | 393 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"kss... | 393 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 64 |
from __future__ import annotations
from typing import Any
class _snake_case ( snake_case ):
pass
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Any = data
__magic_name__ : Node | None = None
def __it... | 124 | 0 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__SCREAMING_SNAKE_CASE : Any = '''src/trans... | 623 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_vision_encoder_decoder''':... | 623 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""YituTech/conv-bert-base""": """https://huggingface.co/YituTech/c... | 74 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_di... | 483 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatu... | 280 |
'''simple docstring'''
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ = "" , UpperCamelCase_ = False ):
'''simple docstring'''
UpperCamelCase__ :dict[str, RadixNode] = {}
# A node will be a leaf if the tree ... | 280 | 1 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.t... | 27 |
'''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() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependen... | 507 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Dict ... | 299 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normaliz... | 299 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 688 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = 3
class low... | 688 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
#... | 452 | import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vis... | 452 | 1 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 207 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 0 |
from ...configuration_utils import PretrainedConfig
__A : str = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetun... | 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
... | 397 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Confi... | 397 | 1 |
'''simple docstring'''
from collections.abc import Generator
def SCREAMING_SNAKE_CASE( ) -> Generator[int, None, None]:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = 0, 1
while True:
UpperCAmelCase_ , UpperCAmelCase_ : str = b, a ... | 471 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebo... | 471 | 1 |
# 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 and v to U. We can also say that... | 105 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a__ : Optional[Any] = None
try:
import msvcrt
except ImportError:
a__ : List[str] = None
try:
import fcntl
except ImportError:
a__ : O... | 188 | 0 |
def __UpperCAmelCase ( UpperCAmelCase )-> float:
"""simple docstring"""
if edge <= 0 or not isinstance(UpperCAmelCase, UpperCAmelCase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) ... | 707 | from __future__ import annotations
from collections.abc import MutableSequence
class __lowercase :
def __init__( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : MutableSequence[float] ) -> None:
'... | 479 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( snake_case):
assert isinstance(lowerCamelCase_, lowerCamelCase_), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
__snake_case = f"The input value of [n={numb... | 564 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
def __init__(self ... | 647 | 0 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dat... | 198 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a__ = """src/diffusers"""
# Matches is_xxx_available()
a__ = re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches ... | 198 | 1 |
def SCREAMING_SNAKE_CASE ( snake_case_ : list[list[int | float]] ):
snake_case__ : Optional[int] = len(lowerCAmelCase__ )
snake_case__ : str = len(matrix[0] )
snake_case__ : Union[str, Any] = min(lowerCAmelCase__ , lowerCAmelCase__ )
for row in range(lowerCAmel... | 297 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCAmelCase ( _UpperCamelCase ):
# to overwrite a... | 642 | 0 |
'''simple docstring'''
import os
import re
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 ={'voca... | 358 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/ma... | 358 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCamelCase :
'''simple docstring'''
lowercase : int
lowercase : int
... | 257 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( _a ) -> List[str]:
'''simple docstri... | 257 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 630 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
... | 630 | 1 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_... | 430 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
if num < 0:
return False
UpperCamelCase = num
UpperCamelCase = 0
while num > 0:
UpperCamelCase = rev_nu... | 430 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase__ ( _UpperC... | 713 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclas... | 609 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_... | 329 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbo... | 329 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'c... | 432 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
... | 432 | 1 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calcul... | 420 | """simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase ( a__ : dict , a__ : str , a__ : set , a__ : set , a__ : dict , a__ : dict , a__ : ... | 420 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=lowerCAmelCase__):
__UpperCamelCase = ["torch", "torchsde"]
def __init__( self : Optional[Any] , *a__ : str , **a__ : str ... | 709 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 0 |
a__ : Optional[int] = """Tobias Carryer"""
from time import time
class lowercase :
"""simple docstring"""
def __init__( self : int , a_ : Union[str, Any] , a_ : int , a_ : List[Any] , a_ : List[str]=in... | 165 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : Any = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Conf... | 165 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 222 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __a ( lowerCAmelCase__ , unitte... | 222 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return np.dot(UpperCAmelCase__ , UpperCAmelCase__ )
class UpperCamelCase__ :
def __init__( self : Any , *,
... | 412 |
import re
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(UpperCAmelCase__ , UpperCAmelCase__ ):
return match.string == phone
return False
if __name__ == "__main__":
pri... | 412 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Any = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not... | 712 |
'''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 class... | 126 | 0 |
'''simple docstring'''
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def snake_case_ ( lowe... | 199 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from trans... | 199 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _inter... | 467 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: Tuple , lowerCAmelCase: Any=None , **lowerCAmelCase: Any ) -> Dict:
_UpperCAmelCase : Optional[Any] = [x.strip() for x in open(low... | 467 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Refor... | 42 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : int = logging.get_logger(__name__)
__a : Optional[int] = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at htt... | 534 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSched... | 711 | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase ... | 164 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Optional[Any] = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/mi... | 63 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetV... | 448 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = 1, 1
UpperCAmelCase_ : List[str] = []
for i in range(1, n + 1 ):
... | 644 |
'''simple docstring'''
snake_case_ : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0... | 644 | 1 |
'''simple docstring'''
# 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
cla... | 119 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( lowercase__ : str = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowercase__ ):
__lowercase =[d for d in dir_names if d != 'scripts' a... | 119 | 1 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 1 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils i... | 26 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 327 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 719 | '''simple docstring'''
import sys
from collections import defaultdict
class __UpperCAmelCase :
def __init__( self ):
lowerCAmelCase_ = []
def UpperCAmelCase_ ( self , _lowerCamelCase ):
return self.node_position[vertex]
def Uppe... | 606 | 0 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 331 |
'''simple docstring'''
from math import pi
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 331 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase = logging.getLogger(__name__)
class __snake_case:
'''simple docstring'... | 344 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Im... | 344 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import To... | 575 |
from typing import Dict, Optional
import numpy as np
import datasets
a__ : int = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-cla... | 165 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax... | 718 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 0 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
... | 26 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 0 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowercase__ : str = input('''Enter image url: ''').strip()
print(f'Downloading image from {url} ...')
lowercase__ : Dict = Beaut... | 711 |
"""simple docstring"""
from statistics import mean, stdev
def __lowercase ( _a , _a = 3 ):
snake_case_ : Optional[int] = min(_a )
snake_case_ : str = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) , _a ) for x in data... | 485 | 0 |
'''simple docstring'''
from __future__ import annotations
__magic_name__ = 1.60_21e-19 # units = C
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float , ):
if (conductivity, electron_conc, mobility).count(0) != 1:
... | 665 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 704 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_UpperCamelCase : Optional[int] = datasets.logging.get_logger(__name__)
_UpperCamelCase : Dict = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust ... | 341 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
f... | 587 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_u... | 587 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 125 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_... | 125 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
snake_case : List[str] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action=... | 124 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 124 | 1 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : Dict ) -> List[Any]:
'''simple docstring'''
return math.pow(UpperCAmelCase__ , 2 ) - a
def lowerCAmelCase_ ( snake_case_ : Union[st... | 714 | '''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 415 | 0 |
'''simple docstring'''
__UpperCAmelCase ="\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__UpperCAmelCase... | 546 | '''simple docstring'''
from manim import *
class a__ ( UpperCAmelCase__ ):
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
__lowerCamelCase = Rectangle(height=0.5 , width=0.5 )
_... | 546 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
UpperCAmelCase_ = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
im... | 707 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowerCamelCase__( ... | 80 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if nth_term == "":
return [""]
__SCREAMING_SNAKE_CASE = int(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = ... | 682 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 682 | 1 |
"""simple docstring"""
def __a ( A ) -> int:
'''simple docstring'''
if not isinstance(A , A ):
A__ = f"""Input value of [number={number}] must be an integer"""
raise TypeError(A )
if number < 1:
A__ = f"""Input value of [number={number}] mus... | 261 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase =... | 261 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase = 1.6_021E-19 # units = C
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ) ->str:
if (conductivity, e... | 174 |
"""simple docstring"""
from itertools import permutations
def lowercase ( _SCREAMING_SNAKE_CASE : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
... | 602 | 0 |
"""simple docstring"""
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation"... | 22 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Dict ) -> int:
A = {}
def _SCREAMING_SNAKE_... | 22 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_E... | 285 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate... | 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''Can... | 336 |
import math
import random
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase = False ) ->float:
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase... | 336 | 1 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not ... | 364 |
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__=2_81_23 ):
lowerCamelCase_ : Any = [1] * (limit + 1)
for i in range(2 ,int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 ,limit // i + 1 ):
sum_divs[k * i] += k + i
... | 364 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCamelCase ( _UpperCamelCase : int = 3 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance(_UpperCamelCase , _UpperCamelCase ... | 717 |
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterM... | 299 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __snake_case ( __UpperCamelCase : Optional[int] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
A_ ... | 86 | '''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__SCREAMING_SN... | 244 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a_ = False
class __lowercase ( unittest.TestCase):
... | 48 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 1 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_... | 633 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
a : List[Any] = '''\
'''
a : Optional[int] = '''
Perpl... | 633 | 1 |
import math
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
return math.pow(UpperCAmelCase__ , 2 ) - a
def __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
return 2 * x
def __lowercase... | 710 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datas... | 102 | 0 |
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