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 |
|---|---|---|---|---|
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from ... | 115 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils ... | 115 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 115 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_avai... | 115 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 1 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availa... | 115 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCamelCase_ : List[str] = logging.get_logger(__name__)
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
def __init__( ... | 115 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm mu... | 115 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 1 |
"""simple docstring"""
import math
def A_ (__a , __a = 0 , __a = 0 ):
'''simple docstring'''
A_ = end or len(__a )
for i in range(__a , __a ):
A_ = i
A_ = array[i]
while temp_index != start and temp_index... | 115 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 1 |
"""simple docstring"""
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def A_ (__a , __a ):
'''simple docstring'''
A_ = k_size // 2
A_ , A_ =... | 115 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 1 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
snake_case = (EulerDiscreteSchedule... | 115 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 115 | 1 |
"""simple docstring"""
def A_ (__a , __a ):
'''simple docstring'''
A_ = ""
for word_or_phrase in separated:
if not isinstance(__a , __a ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phras... | 115 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransfor... | 115 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCamelCase_ : Dict = logging.get_logg... | 115 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 1 |
"""simple docstring"""
def A_ (__a , __a ):
'''simple docstring'''
_validate_point(__a )
_validate_point(__a )
if len(__a ) != len(__a ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ... | 115 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 1 |
"""simple docstring"""
import pprint
import requests
UpperCamelCase_ : Tuple = '''https://zenquotes.io/api'''
def A_ ():
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def A_ ():
'''simple docstring'''
return re... | 115 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPrio... | 115 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 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_tens... | 115 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def A_ (__a , __a , __a , __a , ):
'''simple docstring'''
A_ , A_ = grid.shape
A_ = [-1, 1, 0, 0]
A_ = [0, 0, -1, 1]
if allow_diagonal:
d... | 115 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 1 |
"""simple docstring"""
import math
import qiskit
def A_ (__a = 1 , __a = 1 , __a = 1 ):
'''simple docstring'''
if (
isinstance(__a , __a )
or isinstance(__a , __a )
or isinstance(__a , __a )
):
raise TypeError("inp... | 115 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 1 |
import argparse
import datetime
def __lowercase ( snake_case ):
"""simple docstring"""
__magic_name__ :Dict = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thursday''',
... | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_ca... | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDe... | 2 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Dict = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfi... | 3 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
"""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,
Ber... | 4 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class UpperCAmelCase_ :
'''simple doc... | 5 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
_lowerCamelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Any , UpperCamelCase__: Optional[Any] , UpperCa... | 6 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
"""simple docstring"""
import baseaa
def _snake_case ( _snake_case : str ) -> bytes:
'''simple docstring'''
return baseaa.aaaencode(string.encode('utf-8' ) )
def _snake_case ( _snake_case : bytes ) -> str:
'''simple docstrin... | 7 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self):
... | 8 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@requ... | 9 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = (PNDMScheduler,)
UpperCAmelCase = (("num_inference_steps", 50),)
def UpperCamelCase_... | 10 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-p... | 11 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 115 | 0 |
import os
lowerCamelCase__ : int = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
def UpperCamelCase ( lowercase_ ) -> int:
'''simple docstring'''
lowercase__ : Optional[Any] = 0
lowercase__ : L... | 12 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 0 |
'''simple docstring'''
A__ : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
A__ : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
"knot": 0.5_3_9_9_5_6_8_0_3,
}
def ... | 13 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path im... | 15 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ..... | 16 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_mu... | 17 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_SCREAMING_SNAKE_CASE = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __a(SCREAMING_SNAKE_CASE_ : dict[int, list[int]] , SCREAMING_SNAKE_CASE_ : int , S... | 18 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_a = HUGGINGFACE_HUB_CACHE
_a = """config.json"""
_a = """diffusion_pytorch_model.bin"""
_a = """diffusion_flax_model.msgpack"""
_a ... | 19 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
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 _lowercase( __a : List[Any] ):
a__ ... | 20 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requ... | 21 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class A ( _a ):
lowercase_ = 'WhisperFeatureExtractor'
lowercase_ = 'WhisperTokenizer'
def __init__( self : Union[str, Any] , ... | 22 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
snake_case__ : Optional[Any] = logging.get_logger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def ... | 23 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 24 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise Value... | 25 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( _lowerCamelCase , _lowerCamelCase , ... | 26 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler... | 27 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase__( __UpperCamelCase: Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] ... | 28 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceCl... | 29 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Tuple = len(_lowercase ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 30 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 31 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase_ = "1"
UpperCAmelCase_ = "0"
UpperCAmelCase_ = "1"
UpperCAmelCase_ = ort.SessionOptions()
UpperCAmelCase_ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference ses... | 32 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase__ : Optional[int] = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggin... | 33 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 115 | 0 |
"""simple docstring"""
# Copyright 2021 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/LICE... | 34 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( A__ ) -> List[Any]:
'''simple docstring'''
for param in module.parameters():
SCREAMING_SNAKE_CASE__ : Optional[int] = False
def a ( ) -> Dict:
... | 35 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availa... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A__ :
"""simple docstring"""
pass
| 37 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : Dict = logging.get_logger(__name__)
class __snake_case ( __SCREAMING_SNAKE_CAS... | 38 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 39 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Tuple = n
UpperCamelCase : List[Any] = [None] * self.n
UpperCamelCase : str = 0 # ind... | 40 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __lt__( self : Any ,lowercase__ : List[Any] ... | 41 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_... | 42 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tour... | 43 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
... | 44 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
from math import isqrt
def A ( lowercase__ : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase__ ) + 1 ) )
def A ( lowercase__ : int = 10**6 ) -> int:
UpperCamelCase__ :Tuple = 0
UpperCamelCase__ :List[Any] = 1
U... | 45 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
"""simple docstring"""
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_tok... | 46 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
t... | 47 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class A :
def __init__( self : Dict , __magic_name__ : Collection[float] | None = None ):
"""simple docstring"... | 48 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int = 4_000_000 ):
__UpperCAmelCase = [0, 1]
__UpperCAmelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
__UpperCAmelCase = 0
for j in rang... | 49 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Ba... | 50 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def __snake_case ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any=1_000 ) -> Any:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
re... | 51 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
fro... | 52 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def a_ ( lowerCAmelCase_ : str=N... | 53 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
__A = abs(a_ )
__A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
__... | 55 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _lowercase :
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : int ) -> None:
__snake_case = num_of_nodes
__snake_case = [... | 56 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 0 |
def snake_case (UpperCAmelCase__ ) -> str:
UpperCamelCase_: Dict = 1
UpperCamelCase_: List[Any] = 2
while i * i <= n:
UpperCamelCase_: Dict = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= mult... | 57 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCAmelCase ( __UpperCamelCase :... | 58 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
e... | 59 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = '''▁'''
lowerCA... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import sys
UpperCamelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403... | 61 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ): # noqa: E741
"""simple docstring"""
while r - l > 1:
SCREAMING_SNAKE_CASE : List[str] = (l + r) // 2
if v[m] >= key:
SCREAMING_SNAKE_C... | 62 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transforme... | 63 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_ava... | 64 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
"""simple docstring"""
class __lowercase :
def __init__( self : List[Any] ,A : List[Any] ):
'''simple docstring'''
# we need a list not a string, so do something to change the type
UpperCAmelCase__ : Any = arr.split(""",""" )
... | 65 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 66 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def SCRE... | 67 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowercase__ ( A_: int , A_: int , A_: int , A_: int , A_: int , A_: int ) -> np.ndarray:
"""simple docstring""... | 68 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a : List[Any] = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pr... | 69 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_t... | 70 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECK... | 71 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
'''simple docstring'''
import math
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase =input('''Enter message: ''' )
lowercase =int(input(f'Enter key [2-{len(lowercase_ ) - 1}]: ' ) )
lowercase =input('''Encryption/Decryption [e/d]: ''' )
if mode.l... | 72 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
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
a_ : Any = logging.get_logg... | 73 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowercase_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __UpperCamelCase ( datasets.Bui... | 74 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 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 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 76 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A = logging.get_logg... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common i... | 78 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_o... | 79 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.