code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKI... | 60 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = ["image_processor", "tokenizer"]
snake_case__ = "Aut... | 61 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
snake_case = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
... | 62 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCamelCase__ ( __lowerCamelCase : Any ): ... | 63 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
def A__ ( snake_case_ : list ):
if len(snake_case_ ) <= 1:
return [tuple(snake_case_ )]
SCREAMING_SNAKE_CASE__: str= []
def generate(snake_case_ : int , snake_case_ : list ):
SCREAMING_SNAKE_CASE__: int= [0] * n
res.append(tuple(snake_case_ ) )
SCREAMING_SN... | 64 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 65 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> List[An... | 66 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def SCREAMING_SNAKE_CASE__ ( snake_case__ :str ) ->... | 67 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@r... | 68 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
'''simple docstring'''
import enum
import shutil
import sys
a , a : List[str] = shutil.get_terminal_size()
a : str = {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}
class SCREAMING_SNAKE_CASE__ ( enum.Enum... | 69 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class A( ... | 70 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> float:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = (num_of_terms / 2) * (2 * first_term + (n... | 71 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def _A( snake_case_ ):
raise NotImplementedError()
@abstractmethod
def _A( self ... | 72 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
def a__ ( snake_case = 10 , snake_case = 22 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = range(1 , snake_case )
__SCREAMING_SNAKE_CASE : List[Any] = range(1 , snake_case )
return sum(
1 for power in powe... | 74 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCamelCase__ = 2_0_4_8
UpperCamelCase__ = 4_0_9_6
UpperCamelCase__ = 4_2
UpperCamelCase__ = os.environ.pop('''PROCESS_TRAIN''', '''false''')
UpperCamelCase__ =... | 75 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
"""simple docstring"""
from __future__ import annotations
a_ = [True] * 1_0_0_0_0_0_1
a_ = 2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
a_ = False
i += 1
def __UpperCAmelC... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 0 |
"""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 ... | 77 |
import argparse
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, Distribu... | 73 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
SCREAMING_SNAKE_CASE_: Dict =[
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embeddi... | 78 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
... | 79 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__UpperCamelCase : Optional[int] = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS ... | 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
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.pipe... | 81 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCamelCase = numpy.array([0, 0])
lowerCamelCase = numpy.array([0.5, 0.8_660_254])
lowerCamelCase = numpy.array([1, 0... | 82 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
... | 83 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import... | 84 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
from __future__ import annotations
import bisect
def _a ( lowercase__ : list[int] , lowercase__ : int , lowercase__ : int = 0 , lowercase__ : int = -1 ):
'''simple docstring'''
if hi < 0:
SCREAMING_SNAKE_CASE__ : str = len(lowercase_... | 85 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['torch', 'torchsde']
def __init__( self : Any , *UpperCAmelCase : int ... | 86 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_lowerCamelCase : List[str] = logging.ge... | 87 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
"""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 _snake_case ( __snake_case : Optional[Any] , __snake_case ... | 88 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCamelCase( _a ):
lowercase_ : Dict = """"""
lowercase_ : str = (
None # p... | 89 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_comm... | 90 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _snake_case ( snake_case__ : List[Any] , snake_case__ : str , snake_case__ : str , snake_case__ : ... | 91 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : int , __magic_name__ : int , __magic_name__... | 92 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE = 1000 ) ->int:
"""simple docstring"""
lowerCAmelCase__ :List[str] = 3
lowerCAmelCase__ :str = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
resul... | 93 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-e... | 94 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
"""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 impor... | 95 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffu... | 96 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft... | 97 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ : int = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argu... | 98 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 0 |
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 Ac... | 99 |
import argparse
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, Distribu... | 73 | 0 |
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,
)
_A : str = {
"""configuration_albert""": ["""ALBERT_PRE... | 100 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = """Speech2TextFeatureExtractor"""
_UpperCAmel... | 101 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy ... | 102 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
fro... | 103 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
assert (
isinstance(UpperCAmelCase_, UpperCAmelCase_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, yo... | 104 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( lowerCamelCase_ , unittest.Test... | 105 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipe... | 106 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _SCREAMING_SNAKE_CASE ( ):
_A , _A = 9, 1_4 # noqa: F841
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, ... | 107 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
_lowerCamelCase = 42
_lowerCamelCase = None
_lowerCamelCase = None
__a: Optional[int] = namedtu... | 108 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
a = ""
a = ""
a = ""
a = ""
def __magic_name__ ( __UpperCAmelCase ) -> None:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = tweepy.OAuthHandler(__UpperCAmelCase ... | 109 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def lowerCamelCase ( ):
from torch.utils.cpp_extension import load
UpperCAmelCase__ : str = Path(_snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
UpperCAmelCase__ : List[Any] ... | 110 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowercase ( A__):
"""simple docstring"""
_A : Tuple = '''EncodecFeatureExtractor'''
_A : ... | 480 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : List[Any] ):
"""simple docstring"""
UpperCamelCase = [0] * len(_UpperCAmelCase )
UpperCamelCase = []
UpperCamelCase = [1] * len(_UpperCAmelCase )
for values in graph.val... | 386 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization... | 87 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__SCREAMING_SNAKE_CAS... | 388 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
'''simple docstring'''
import re
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
... | 331 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
"""simple docstring"""
def _lowercase ( __snake_case = "The quick brown fox jumps over the lazy dog" ,) -> Optional[Any]:
__lowerCAmelCase : Dict = set()
# Replace all the whitespace in our sentence
__lowerCAmelCase : Union[str, Any] = input_str.repla... | 293 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_lowerCAmelCase :Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase :Tuple = {
'Intel/dpt-large': 'https://huggingface.... | 251 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:
if... | 204 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase : Any = {
'configuratio... | 241 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
"""simple docstring"""
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Dict = n
UpperCAmelCase__ : str = [None] * self.n
UpperCAmelCase__ : str = 0 # index of the first element
... | 110 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 0 |
def __UpperCAmelCase ( a_ , a_):
print('\nThe shortest path matrix using Floyd Warshall algorithm\n')
for i in range(_UpperCAmelCase):
for j in range(_UpperCAmelCase):
if dist[i][j] != float('inf'):
print(int(dist[i][j]) , e... | 198 |
import argparse
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, Distribu... | 73 | 0 |
"""simple docstring"""
from math import sqrt
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Dict ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 480 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def lowerCamelCase_ ( self : Dict , __magic_name__ : List[str] ):
"""si... | 386 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
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 SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"... | 87 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
"""simple docstring"""
def __a ( a, a ):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ), _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_is... | 388 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionM... | 331 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | 293 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCAmelCase ( datasets.BeamBasedBuilder ):
... | 251 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torc... | 204 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowercase_ ( __snake_case : List[str] , __snake_case : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case__ :... | 241 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
"""simple docstring"""
def lowerCamelCase ( _snake_case = 100 ):
UpperCAmelCase__ : Optional[Any] = 0
UpperCAmelCase__ : List[Any] = 0
for i in range(1 ,n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_... | 110 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from... | 198 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ ... | 480 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : Optional[Any] ): # noqa: E741
"""simple docstring"""
UpperCamelCase = len(_UpperCAmelCase )
UpperCamelCase = 0
UpperCamelCase = [0] * n
UpperCamelCase = [False] *... | 386 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ = 50_000_000 ) -> str:
"""simple docstring"""
A__ = set()
A__ = int((limit - 24) ** (1 / 2) )
A__ = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
f... | 87 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
"""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... | 388 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 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,
DDIMSchedule... | 331 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
"""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, ... | 293 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cl... | 251 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, B... | 204 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Dict = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbee... | 241 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case = None ):
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
... | 110 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 0 |
from __future__ import annotations
def __UpperCAmelCase ( a_ , a_ , a_):
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0')
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must be >= 0')
if princ... | 198 |
import argparse
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, Distribu... | 73 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( A__):
"""simple docstring"""
_A : Tuple = ['''image_processor''', '''tokenizer''']
_A : L... | 480 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from .... | 386 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
_lowerCamelCase : str = 'Alexander Joslin'
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
... | 87 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class __s... | 388 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
a__ = mf_... | 331 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def _lowercase ( __snake_case ) -> Optional[int]:
__lowerCAmelCase : Optional[Any] = min(_UpperCAmelCase ) # min() finds the minimum value
__lowerCAmelCase : str = m... | 293 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowerCAmelCase :Optional[Any] = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
_lowerCAmelCase :List[str] = None
def __lowerCAmelC... | 251 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.fu... | 204 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( A__ ):
_A = '''ClapFeatureExtractor'''
_A = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self ,UpperCa... | 241 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCamelCase__ = '<<<<<<< This should probably be modified because it mentions: '
UpperCamelCase__... | 110 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..m... | 198 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else... | 480 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
from __future__ import annotations
def _lowercase ( SCREAMING_SNAKE_CASE_ : List[str] ):
"""simple docstring"""
UpperCamelCase = 2
UpperCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 386 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase : Any = logging.getLogger()
def SCREAMING_SNAKE_CASE ... | 87 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_pr... | 388 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
a__ = 4
a__ = (1 << p) - 1
for... | 331 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case=None ) -> Union[str, Any]:
__lowerCAmelCase : Tuple = (path or []) + [u]
for v in graph[u]:
... | 293 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
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