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 |
|---|---|---|---|---|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 332 |
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 OptionalDependencyNotAvailable()... | 332 | 1 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : list , UpperCAmelCase_ : int , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : int = 0 ):
lowerCamelCase_ = right or len(UpperCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[l... | 718 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Con... | 445 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
UpperCAmelCase = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh... | 666 | from typing import Dict, List, Optional, Tuple, 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_dimensio... | 666 | 1 |
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
lowerCAmelCase = logging.getLogge... | 429 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenization_... | 429 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 501 |
'''simple docstring'''
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 __snake_case (__UpperCAmelCase , __Uppe... | 501 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__a : Tuple = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action=... | 522 | 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 : Any = logging.get_logger(__name__)
__a : Dict = {
"""hustvl/... | 522 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_snake_case : Optional[int] = logging.get_logger(__name__)
class a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : Union[str, Any] ... | 81 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...t... | 102 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _A ( ... | 624 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 624 | 1 |
from numpy import exp, pi, sqrt
def UpperCamelCase ( snake_case__ , snake_case__ = 0.0 , snake_case__ = 1.0):
return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 659 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __... | 659 | 1 |
'''simple docstring'''
from math import pow, sqrt
def __snake_case (*__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = len(__UpperCAmelCase ) > 0 and all(value > 0.0 for value in values )
return result
def __snake_case (__Upp... | 418 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowerCamelCase_ : Union[str, Any] = sorted(string.lower() ... | 418 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 41 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common impor... | 675 | 0 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name_... | 149 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def snake_case_ ( lowercase__ : Optional[Any] , lowercase__ : List[Any]=False ):
'''simple docstring'''
_lowerCAmelCase =OmegaConf.load(l... | 149 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=lowerCamelCase ):
a__ = ['''note_seq''']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends... | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCamelCase_ ( lowerCamelCase ... | 0 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git... | 302 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_... | 302 | 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 snake_case_ ( nn.... | 351 |
def a_ (__A , __A , __A , __A ) -> int:
"""simple docstring"""
__a , __a : Any = len(__A ), len(grid[0] )
if (
min(__A , __A ) < 0
or row == row_length
or col == col_length
... | 351 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def _A ( A ,A ,A ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
... | 708 |
'''simple docstring'''
import qiskit
def _A ( A ,A ) -> qiskit.result.counts.Counts:
lowercase : Tuple = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
lowercase : List[Any] = qiskit.QuantumCircuit(A ... | 425 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __a :
'''simple docstring'''
_lowerCamelCase : List[str] = ... | 118 | """simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase :
@property
def _UpperCAmelCa... | 425 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCamelCase__ : int = 2_0_0 ) -> int:
_SCREAMING_SNAKE_CASE : List[str] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
_SCREAMING_SNAKE_CASE : Tuple = [0] * (pence + 1)
_SCREAMING_SNAKE_CASE : List[Any] ... | 701 |
"""simple docstring"""
import operator as op
def _lowerCAmelCase ( lowerCamelCase__ : Tuple ) -> List[str]:
_SCREAMING_SNAKE_CASE : Optional[int] = []
_SCREAMING_SNAKE_CASE : str = lambda lowerCamelCase__, lowerCamelCase__ : int(x / ... | 295 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__snake_case : Union[str, Any] = str(bin(__lowerCamelCase ) )[2:] # remove the leading "0... | 81 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 378 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase__(__snake_case ) -> Dict:
'''simple docstring'''
for param in module.parameters():
lowerCamelCase__ = False
def lowerCAmelCase__() -> Union[str, Any]:
... | 29 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : List[str] = logging.get_logger(__name__)
_UpperCamelCase : Tuple ... | 396 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
fr... | 459 | 0 |
"""simple docstring"""
import math
class __lowerCamelCase :
'''simple docstring'''
def __init__( self , __UpperCAmelCase=0 ) -> Tuple: # a graph with Node 0,1,...,N-1
_a = n
_a = [
[math.inf for j in range(0 , ... | 285 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_... | 285 | 1 |
def UpperCAmelCase_ ( _A , _A , _A , _A , _A ):
'''simple docstring'''
if index == number_of_items:
return 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = knapsack(_A , ... | 493 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_SCREAMING_SNAKE_CASE : Optional[Any] = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.par... | 493 | 1 |
"""simple docstring"""
import functools
from typing import Any
def lowerCamelCase_ ( _lowerCamelCase : str , _lowerCamelCase : list[str] ):
# Validation
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
... | 718 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
super... | 66 | 0 |
"""simple docstring"""
def _snake_case ( ):
return 1
def _snake_case ( snake_case__ : int ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _snake_case ( snake_case__ : int ):
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(snake_case__ )
def _... | 91 |
from __future__ import annotations
from collections.abc import Generator
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = {}
UpperCamelCase__ = 2
while True:
UpperCamelCase__ = factor_map.pop(__a... | 513 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float , ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise Valu... | 461 | '''simple docstring'''
def __a ( __lowerCamelCase : int = 600_851_475_143 ) -> int:
'''simple docstring'''
try:
lowercase_ = int(__lowerCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
... | 461 | 1 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
"""simple docstring"""
_lowercase = ['transformers', 'torch', 'note_seq']
def __init__( self : Optional[int] , *lowerCamelCase__ : int , **lowerCamelCase__ : int ):
require... | 37 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 92 | 0 |
"""simple docstring"""
import math
def a ( __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[int] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
... | 213 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDM... | 213 | 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 __SCREAMING_SNAKE_CASE ( _Uppe... | 624 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 624 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__lowercase ... | 701 | '''simple docstring'''
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 a__( lowerCAmelCase__ ):
'''simple docstring'''
... | 605 | 0 |
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 ... | 32 |
"""simple docstring"""
def A_ ( lowercase ) -> str:
"""simple docstring"""
UpperCAmelCase_ : Any = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase_ : Union[str, Any] = """"""
UpperCAmel... | 470 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Ten... | 718 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowerCamelCase : Dict = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def lowercase__ ( __A: List[Any] ... | 501 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__Upp... | 80 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">... | 695 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(lowerCamelCase_ , x % y )
def UpperCAmelCase ( UpperCamelCase__ ... | 702 | """simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> str:
A__ = get_activation('swis... | 536 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_... | 436 |
'''simple docstring'''
import argparse
import struct
import unittest
class _snake_case :
'''simple docstring'''
def __init__( self: Optional[int] , __UpperCamelCase: bytes ) -> None:
__magic_name__ :... | 436 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffusio... | 718 | '''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 packaging import version
... | 415 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( A_ : Callable[[int | float], int | float], A_ : int | float, A_ : int | float, A_ : int = 1_00, ):
'''simple docstring'''
... | 83 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.p... | 504 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_UpperCamelCase : Union[s... | 708 |
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,
)
_UpperCamelCase : Any ... | 341 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def UpperCAmelCase_ ( __lowerCamelCase : bytes ):
if len(__lowerCamelCase ) != 32:
raise ValueError("Input must be of length 32" )
lowercase_ :str = B""
fo... | 172 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip ins... | 172 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_pa... | 145 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase : Tuple = False
class __magic_name__ ( unittest.TestCase ):
... | 145 | 1 |
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.kandinsky.text_encoder impo... | 569 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowerCAmelCase = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except Opt... | 569 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __UpperCAmelCase ( A : str , A : Tuple , A : List[str]=None , **A : List[str] ) -> Optional[int]:
UpperCAmelCase_ : Optional[Any] = [x.strip() for x in open(A ... | 216 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( A : np.ndarray , A : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
UpperCAmelCase_ : int = math.sqrt(A )
UpperCAm... | 216 | 1 |
'''simple docstring'''
import os
def _UpperCamelCase (_lowerCamelCase : str = "matrix.txt" )-> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(_lowerCamelCase ) , _lowerCamelCase ) ) as in_file:
__snake_case =... | 24 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 0 |
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common im... | 706 | '''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCAmelCase_ : List[Any] = [{"""type""": ""... | 204 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Any , UpperCamelCase : List[Any] , UpperCamelCase : Any , UpperCamelCase : str ... | 322 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__... | 634 | 0 |
class SCREAMING_SNAKE_CASE_ :
def __init__( self : int ) -> Tuple:
"""simple docstring"""
snake_case_ : Tuple = {}
def UpperCAmelCase_ ( self : str ) -> None:
... | 715 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_SCREAMING_SNAKE_CASE = (7_20, 12_80) # Height, Width
_SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
_SCREAMING_SNA... | 534 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNe... | 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 36 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import ... | 525 | def __A ( _A ):
"""simple docstring"""
__a = []
for data in source_data:
for i, el in enumerate(_A ):
if len(_A ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(_A ) )
return data_lists
def __A ( _A , _A ):
... | 525 | 1 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Union[str, Any] ):
return 1 / (1 + np.exp(-z ))
... | 6 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
c... | 149 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffuser... | 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.... | 24 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSe... | 44 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class __lowercase ( _lowercase ):
def __init__(self , *A , **A ):
super().__init__(*A , **A )
lowerCamelCase_ : Optional[int] = {}
def UpperCAmelCase__ ... | 422 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, 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():
impor... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See al... | 426 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__... | 665 |
'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 119 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.d... | 457 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.uti... | 457 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProc... | 67 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf... | 67 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 ModelT... | 708 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _a ( __SCREAMING_SNAKE_CASE : Optional[Any] ... | 585 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAM... | 305 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[int] = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_A... | 265 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gp... | 390 | import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UN... | 390 | 1 |
from math import isqrt
def UpperCamelCase ( snake_case__ : int ) -> list[int]:
UpperCamelCase : Union[str, Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_cas... | 40 |
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 SequenceFeatureExtractio... | 40 | 1 |
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
from ..auto import CONFIG_MAPPING
lowerCAmelCase__: Any = logging.get_logger(__n... | 311 |
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> bool:
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 primes
return False
#... | 311 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class UpperCamelCase_ ( __a ):
'''simple... | 441 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_snake_case : Tuple = models.Sequentia... | 441 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 29 |
_a = "\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"
_a = [{"type": "code", "content": INSTALL_CONTENT}]
_a = {
"{processor_class}": "FakeProcessorC... | 29 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=snake_case_ ):
__UpperCAmelCase : Any = ['''keras_nlp''']
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
... | 178 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__snake_case = logging.get_logger(__name__)
class _lowerCAmelCase ( snake_case_ ):
def __init__( self , *UpperCamelCase__ , ... | 178 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class ... | 243 |
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, slow
from .test_pipelines... | 243 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...t... | 93 |
"""simple docstring"""
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_... | 480 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 381 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ ={
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
... | 381 | 1 |
'''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,
B... | 51 |
import os
def __magic_name__ ( lowerCAmelCase_ = "input.txt"):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase_) , lowerCAmelCase_)) as input_file:
lowerCamelCase_ : Dict = [
[int(lowerCAmelCase_) for element in l... | 250 | 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,
DDIMSchedu... | 700 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : List[str] ): # noqa: E741
'''simple docstring'''
lowercase__ = len(_snake_case )
lowercase__ = 0
lowercase__ = [0] * n
lowercase__ ... | 539 | 0 |
'''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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 94 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : Union[str, Any] ... | 429 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _snake_case :
pass
| 323 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_te... | 323 | 1 |
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( lowercase__ ):
A__ = ['''image_processor''', '''feature_extractor''']
A__ = '''TvltImageProcessor'''
A__ = '''TvltFeatureExtractor'''
def __init__( self , __UpperCAmelCase , __Uppe... | 678 |
import math
from numpy import inf
from scipy.integrate import quad
def SCREAMING_SNAKE_CASE_ ( __A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
return quad(__A ... | 570 | 0 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""kakaobrain/align-... | 700 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
if numbers[j] < numbers[i]:
lowercase__ , lowercase__ = numbers[j]... | 37 | 0 |
'''simple docstring'''
from math import factorial
_snake_case : Optional[int] = {str(d): factorial(d) for d in range(10)}
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase ... | 22 |
'''simple docstring'''
import argparse
import os
import re
UpperCAmelCase_ : List[str] = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_ : Tuple = re.... | 24 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( a_ : str , a_ : int ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(a_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod() | 599 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class __UpperCAmelCase ( UpperCamelCase__ ):
'''simple docstring'''
def __... | 599 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def lowerCamelCase ( self : str , lowerCamelCase : str ) -> Optional[int]:
... | 108 |
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase = psutil.Process()
_UpperCAmelCase ... | 108 | 1 |
"""simple docstring"""
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
lowercase : Optional[Any] = logging.get_log... | 708 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
... | 468 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 151 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if not is_torch_av... | 151 | 1 |
from __future__ import annotations
import math
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
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... | 712 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verb... | 481 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
'''... | 403 | from __future__ import annotations
def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : list[str] | None = None , UpperCAmelCase__ : dict[str, float] | None = None , UpperCAmelCase__ : bool = False , ):
SCREAMING_SNAKE_CASE = cipher_... | 403 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIF... | 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : List[Any] = {
... | 293 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 312 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] ... | 613 | 0 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE : Tuple = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted ... | 137 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePro... | 137 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[Any] = logging.get_logger(__name__)
_a : Tuple = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretraine... | 213 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __a ):
snake_case : Dict = (UnCLIPScheduler,)
def snake_case_ (self , **lowerCA... | 414 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCAmelCase__ ( snake_case , unittest.Tes... | 714 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?f... | 286 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features impor... | 484 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class ... | 484 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 675 |
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,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
... | 675 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
UpperCAmelCase__ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCAmelCase__ : Tuple = BASE... | 410 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 410 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCAmelCase : List[str] = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
... | 364 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowerCAmelCase : List[Any] = ["small", "medium", "large"]
_lowerCAmelCase : List[Any] = "lm_head.decoder.weight"
_lowerCAmelCase : Optional[int] = "lm_head.weight"
def lowerCAmelCase ( ... | 364 | 1 |
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... | 611 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_0... | 80 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class UpperCamelCase ( snake_case__ ):
"""simple docstring"""
def __init__( sel... | 110 |
from ...configuration_utils import PretrainedConfig
class UpperCamelCase ( snake_case__ ):
"""simple docstring"""
snake_case = "bert-generation"
def __init__( self : Tuple ,_SCREAMING_SNAKE_CASE : Tuple=5_0_3_5_8 ,_SCREAMING_SNAKE_CASE : str=1_0_2_4 ... | 110 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Any = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingf... | 287 |
from ...processing_utils import ProcessorMixin
class a_ ( _a ):
a : Optional[int] = '''SpeechT5FeatureExtractor'''
a : List[Any] = '''SpeechT5Tokenizer'''
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
super().__init__(__Uppe... | 287 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.j... | 376 |
import fire
from utils import calculate_rouge, save_json
def _UpperCAmelCase ( UpperCamelCase: Any , UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any]=None , **UpperCamelCase: Optional[int] ):
"""simple docstring"""
__lowerCAmelCase = ... | 376 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCAmelCase ( __a : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(__a ,__a ):
... | 14 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase)
class __SCREAMING_SNAKE_CASE ( lowercase):
# `task` is not a ClassVar since we wan... | 717 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__lowerCAmelCase = 5_0_0_0_0_0
__lowerCAmelCase , __lowerCAmelCase = os.path.split(__file__)
__lowerCAmelCase = os.path.join(RESU... | 129 | 0 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
lowercase ='src/transformers'
# Matches is_xxx_available()
lowercase =re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowercase =re.compile(r'^_import_structure\s+=\s+\{([^\}... | 446 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow... | 446 | 1 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_SCREAMING_SNAKE_CASE = (
"""This metric will be removed fro... | 614 |
"""simple docstring"""
from __future__ import annotations
_SCREAMING_SNAKE_CASE = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],... | 614 | 1 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = 0.0 ,UpperCAmelCase__ = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
lowerCamelCase__ = {
"""joule""": 1.0,
"""kilojoule""": 1000,
"""megajoule""": 100_0000,
"""gigajoule""": 10_0000_0000,
"""wattsecond""": 1.0,
"""watthour""": 3600,
"""kilowatthour""": 360_0000,
"""newtonmeter""": 1.0,
"""calorie_nutr""": 4186.8,
"""kilocalorie... | 700 |
import warnings
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 A__ ( __magic_name__ ):
... | 69 | 0 |
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 = {
'junnyu/roformer_chinese_small': 'https://hug... | 43 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: int = 600_851_475_143 ):
try:
SCREAMING_SNAKE_CASE__ = int(UpperCamelCase__ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""P... | 6 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampling... | 297 |
def _lowerCamelCase ( _a , _a ):
"""simple docstring"""
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
_UpperCAmelCase = int(input("Enter the base: ").strip())
... | 297 | 1 |
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