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 ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''', '''torchsde''']
def __init__( self : int , *lowercase_ : Optional[Any] , **lowerca... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 30 | 1 |
'''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase ( UpperCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase__ , U... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
... | 30 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNA... | 30 | 1 |
'''simple docstring'''
import operator as op
_lowercase : List[Any] = "scaler.pt"
_lowercase : int = "pytorch_model"
_lowercase : str = "random_states"
_lowercase : int = "optimizer"
_lowercase : List[Any] = "scheduler"
_lowercase : Tuple = "... | 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = fiel... | 30 | 1 |
'''simple docstring'''
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 ... | 30 | '''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : in... | 30 | 1 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_... | 30 | '''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if start is None:
lowercase_ : Any = 0
... | 30 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 30 | '''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add... | 30 | 1 |
'''simple docstring'''
from __future__ import annotations
_lowercase : Optional[Any] = tuple[int, int, int]
_lowercase : List[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_lowercase : List[str] = "ABCDEFGHIJKLMNOPQRSTUVWX... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConf... | 30 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fr... | 30 | '''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray | None = None , ) -> np.ndarray:
lowercase_ ... | 30 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase ( UpperCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase__ , U... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 10 , UpperCAmelCase__ : int = 22 ) -> int:
lowercase_ : List[Any] = range(1 , UpperCAmelCase__ )
lowercase_ : Tuple = range(1 , UpperCAmelCase__ )
... | 30 | '''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lower... | 30 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_lowercase : Any = True
except (ImportError, ModuleNotFoundError):
_lowercase : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("pun... | 30 | '''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 BackboneTest... | 30 | 1 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : bool , ) -> tuple[float | int, list... | 30 | '''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase : Optional[Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say th... | 30 | 1 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : in... | 30 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small... | 30 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase : List[str] = logging.get_logger(__name__)
_lowercase : int = {
"shi-l... | 30 | '''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ... | 30 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[Any] , lowercase_ : Tuple , lowercase_ : Dict , lowercase_ : Dict , ... | 30 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docst... | 30 | 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/LICENSE-2.0
... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 30 | 1 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str ):
debug_launcher(... | 30 | '''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 30 | 1 |
'''simple docstring'''
from functools import reduce
_lowercase : List[str] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"125406987471585238630507156932909632952274430435... | 30 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer,... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str ) -> str:
if not (isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )):
raise ValueError("""longest_commo... | 30 | '''simple docstring'''
from collections.abc import Callable
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : Callable , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> np.array:
... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 ... | 30 | '''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase_ : str ... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
lowercase_ : Optional[int] = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups... | 30 | '''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import ... | 30 | 1 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowercase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--checkpo... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 30 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
_lowercase , _lowercase : List[Any] = shutil.get_terminal_size()
_lowercase : Any = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class __magic_name__ ( enum.Enum):
UpperCamelCase__ = 0
Uppe... | 30 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNA... | 30 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ... | 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = fiel... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str = " " ) -> list:
lowercase_ : List[Any] = []
lowercase_ : int = 0
for index, char in enumerate(UpperCAmelCase__ ):
if ch... | 30 | '''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : in... | 30 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_lowercase : Union[str, Any] = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert... | 30 | '''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if start is None:
lowercase_ : Any = 0
... | 30 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_lowercase : Optional[int] ... | 30 | '''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add... | 30 | 1 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConf... | 30 | 1 |
'''simple docstring'''
import argparse
import os
import re
_lowercase : List[Any] = "src/transformers"
# Pattern that looks at the indentation in a line.
_lowercase : str = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : str = re... | 30 | '''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray | None = None , ) -> np.ndarray:
lowercase_ ... | 30 | 1 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> str:
... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase ( UpperCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase__ , U... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modu... | 30 | '''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lower... | 30 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer,... | 30 | '''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 BackboneTest... | 30 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : Tuple = [
"word_emb... | 30 | '''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase : Optional[Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say th... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase : List[str] = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLToken... | 30 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small... | 30 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
Vil... | 30 | '''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ... | 30 | 1 |
'''simple docstring'''
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> list[Any]:
if not input_list:
return []
lowercase_ : Dict = [input_list.count(UpperCAmelCase__ ) for value in input_list]
lowercase_ ... | 30 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docst... | 30 | 1 |
'''simple docstring'''
import os
import sys
import unittest
_lowercase : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, crea... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 30 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slo... | 30 | '''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 30 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : Optional[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kin... | 30 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer,... | 30 | 1 |
'''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | '''simple docstring'''
from collections.abc import Callable
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : Callable , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> np.array:
... | 30 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ... | 30 | '''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase_ : str ... | 30 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
f... | 30 | '''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import ... | 30 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/re... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 30 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int ) -> list[int]:
lowercase_ : str = 0
lowercase_ : int = len(UpperCAmelCase__ ) - 1
while ... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
_lowercase : Union[str, Any] = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n ... | 30 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNA... | 30 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import Fla... | 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = fiel... | 30 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : str = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP... | 30 | '''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : in... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowercase : Optional[Any] = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available()... | 30 | '''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if start is None:
lowercase_ : Any = 0
... | 30 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data i... | 30 | '''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add... | 30 | 1 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
fro... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConf... | 30 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class ... | 30 | '''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray | None = None , ) -> np.ndarray:
lowercase_ ... | 30 | 1 |
'''simple docstring'''
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGE... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase ( UpperCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase__ , U... | 30 | 1 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Optional[int] , lowercase_ : str="" , lowercase_ : Dict="train... | 30 | '''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lower... | 30 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( ... | 30 | '''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 BackboneTest... | 30 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lo... | 30 | '''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase : Optional[Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say th... | 30 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Tuple = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __ma... | 30 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowercase : str = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 30 | '''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ... | 30 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ... | 30 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docst... | 30 | 1 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = JukeboxTokenizer
UpperCamelCase__ = {
'''artist''': '''Zac Brown Band''',
... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 30 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Optional[Any] ... | 30 | '''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHI... | 30 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer,... | 30 | 1 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
class __magic_name__ ( _Uppe... | 30 | '''simple docstring'''
from collections.abc import Callable
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : Callable , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> np.array:
... | 30 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import S... | 30 | '''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase_ : str ... | 30 | 1 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[Any]:
lowercase_ ... | 30 | '''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import ... | 30 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 30 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | 1 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_tor... | 30 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNA... | 30 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers... | 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = fiel... | 30 | 1 |
'''simple docstring'''
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_visio... | 30 | '''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : in... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 30 | '''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if start is None:
lowercase_ : Any = 0
... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Any = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTex... | 30 | '''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : float ) -> float:
if edge <= 0 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2))... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConf... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
... | 30 | '''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray | None = None , ) -> np.ndarray:
lowercase_ ... | 30 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokenizer... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase ( UpperCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase__ , U... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 30 | '''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lower... | 30 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import ... | 30 | '''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 BackboneTest... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : str = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"featu... | 30 | '''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase : Optional[Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say th... | 30 | 1 |
'''simple docstring'''
import os
from math import logaa
def lowerCamelCase ( UpperCAmelCase__ : str = "base_exp.txt" ) -> int:
lowercase_ : float = 0
lowercase_ : Any = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(Upper... | 30 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small... | 30 | 1 |
'''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 and v to U... | 30 | '''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ... | 30 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,... | 30 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docst... | 30 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common i... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 30 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNA... | 30 | '''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 30 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fro... | 30 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer,... | 30 | 1 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase : Dict = "\\n@misc{chen2021evaluating,\n title={Ev... | 30 | '''simple docstring'''
from collections.abc import Callable
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : Callable , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> np.array:
... | 30 | 1 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 30 | '''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase_ : str ... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
_l... | 30 | '''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import ... | 30 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : list[int] , UpperCAmelCase__ : list[int] ) -> None:
lowercase_ : Optional[Any] = len(UpperCAmelCase__ )
print("""The following activities are selected:""" )
# The first activit... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 30 | 1 |
'''simple docstring'''
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 __magic... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | 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,
)
_lowercase : Dict = {
"configuration_rembert": ["REMBERT_... | 30 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNA... | 30 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : int , UpperCAmelCase__ : int=1024 ... | 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = fiel... | 30 | 1 |
'''simple docstring'''
_lowercase : List[Any] = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
... | 30 | '''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : in... | 30 | 1 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = (CMStochasticIterativeScheduler,)
UpperCamelCase__ = 10
def SCREAMING_... | 30 | '''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if start is None:
lowercase_ : Any = 0
... | 30 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : str = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM m... | 30 | '''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add... | 30 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_lowercase : Optional[Any] = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matt... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConf... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Any = {
"configuration_distilbert": [
"DISTIL... | 30 | '''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray | None = None , ) -> np.ndarray:
lowercase_ ... | 30 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase : List[Any] = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_A... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase ( UpperCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase__ , U... | 30 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowercase : List[str] = logging.get_logg... | 30 | '''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lower... | 30 | 1 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
_lowercase : Tuple = False
class __magic_name__ (... | 30 | '''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 BackboneTest... | 30 | 1 |
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