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 random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor,... | 664 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 0 |
'''simple docstring'''
import re
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
if len(re.findall("[ATCG]" , lowercase__ ) ) != len(lowercase__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maket... | 331 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 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, logging
... | 350 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeniz... | 131 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_snake_case = TypeVar('''KT''')
_snake_case = TypeVar('''VT''')
class _SCREAMING_SNAKE_CASE ( Generic[KT, VT] ):
'''simple docstring'''... | 580 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase_ = {
"configuration_trocr": ["TROCR_PRETRAINED_CON... | 539 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config i... | 329 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 0 |
"""simple docstring"""
A_ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_S... | 29 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : Tuple ) ->Optional[Any]:
'''simple docstring'''
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _SCREAMING_SNAKE_CASE ( _... | 633 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 0 |
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 sagem... | 246 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class snake_case__ :
'''simple docstring'''
pass
| 121 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 664 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 0 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
a__ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _lowerCAmelCase (_lowercase = 50_00 ):
"""simple docstring"""
a__ = [(... | 331 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""... | 630 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_availa... | 350 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ : Any ):
if not isinstance(lowercase__, lowercase__ ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_a = 0
while number:
# This way we arrive at next set bit... | 131 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: Tuple = 400_0000 ):
"""simple docstring"""
_lowerCAmelCase = [0, 1]
_lowerCAmelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] ... | 580 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_availab... | 539 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torc... | 630 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 329 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 630 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class __lowerCamelCase ( lowerCAmelCase ):
a__... | 29 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ... | 630 | 0 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
a... | 633 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase : Union[str, Any] = '\\n\n'
_lowerCAmelCase : Dict = '\nPerplexity (PPL) is... | 246 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase : Any = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCamelCase : Optional[Any] = _La... | 121 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_rem... | 664 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 0 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
def get_matched_characters(_lowercase , _lowercase ) -> str:
a__ = []
a__ = min(len(_stra ) , len(_stra ) ) // 2... | 331 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 0 |
'''simple docstring'''
a = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggin... | 350 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ : Dict ):
_a = int(lowercase__ )
if n_element < 1:
_a = ValueError("a should be a positive number" )
raise my_error
_a = [1]
_a = (0, 0, 0)
_a ... | 131 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''facebook/dat... | 580 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
def __init__( self , UpperCamelCase ) -> Optional[int]:
__a = data
__a = ... | 539 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 0 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = arr.split(',' )
def _UpperCamelCase ( self ... | 329 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 0 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 29 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 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 diffusers... | 633 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 0 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_lowerCAmelCase : List[Any] = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def a_ ( UpperCamelCase_ : List[str] = "mumbai" ) ->... | 246 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 | 0 |
def _lowerCAmelCase ( ):
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
_lowerCamelCase : Union[str, Any] = generate_large_matrix()
_lowerCamelCase : Dict = (
[[4, 3, 2, -1], [3, 2, 1, -... | 121 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 | 0 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__magic_name__ : List[str] =object()
# For specifying empty leaf dict `{}`
__magic_name__ : Dict =object()
... | 664 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 0 |
'''simple docstring'''
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class lowerCamelCase__ :
"""simple docstring"""
def __init_... | 331 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""... | 630 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingT... | 350 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_comm... | 131 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=UpperCAmelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] = ["transformers", "torch", "note_seq"]
def __init__( ... | 580 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 0 |
'''simple docstring'''
class __lowercase :
def __init__( self , UpperCamelCase ) -> int:
__a = len(UpperCamelCase )
__a = [0] * len_array
if len_array > 0:
__a = array[0]
for i in range(... | 539 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torc... | 630 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
... | 329 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 630 | 0 |
def lowercase ( a , a , a ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(a ) )
def lowercase ( a , a , a , a ):
'''simple docstring'''
if index == len(a ):
return True
... | 631 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = "examples/"
SCREAMING_SNAKE_CASE__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\... | 631 | 1 |
import math
def lowercase ( a , a ):
'''simple docstring'''
if (
not isinstance(a , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid float value between -1 and 1." )
return apparent_power * power_factor
d... | 631 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def lowercase ( a , a , a , a , a , a , a ):
'''simple docstring'''
global process_lock
# we perfor... | 631 | 1 |
def lowercase ( a ):
'''simple docstring'''
if not isinstance(a , a ):
SCREAMING_SNAKE_CASE_ :List[Any] = F"Input value of [number={number}] must be an integer"
raise TypeError(a )
if number < 1:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = F"Input val... | 631 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE__ = (720, 1_280) # Height, Width
SCREAMING_SNAKE_CASE__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__ ... | 631 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..... | 631 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transforme... | 631 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class ... | 631 |
def lowercase ( a , a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [False] * len(a )
SCREAMING_SNAKE_CASE_ :List[Any] = []
queue.append(a )
SCREAMING_SNAKE_CASE_ :int = True
while queue:
SCREAMING_SNAKE_CASE_... | 631 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
SCREAMING_SNAKE_CASE__ = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-large-v1": "https://huggingfac... | 631 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
SCREAMING_SNAKE_CASE__ = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\... | 631 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, ... | 631 |
import qiskit
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
SCREAMING_SNAKE_CASE_ :Union[str, Any] = qiskit.QuantumCircuit(a , a... | 631 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE__ = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76... | 631 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( s... | 631 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 631 |
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,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_owlvit": [
"OWLV... | 631 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name_... | 631 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_plan... | 631 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart imp... | 631 |
def lowercase ( a = 50 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[r... | 631 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"bert-base-uncased": "https://h... | 631 |
from __future__ import annotations
import math
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[Any] = u
for i in range(1 , a ):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = temp * (u - i)
return temp
def lowercase ( ... | 631 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_... | 631 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase ( a )... | 631 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderO... | 631 |
from timeit import timeit
def lowercase ( a ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
SCREAMING_SNAKE_CASE_ :Optional[int] = 0
while number:
number &= number - 1
result += 1
return result
def lower... | 631 | 1 |
SCREAMING_SNAKE_CASE__ = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "hu... | 631 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_... | 631 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"facebook/xmod-base": "https://... | 631 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
def _snake_case ( self : Union[str, Any]):
SCREAMING... | 631 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( lowercase ):
@staticmethod
@abstractmethod
def _snake_case ( UpperCAmelCase : ArgumentParser):
raise NotImplementedError()
@abstractmethod
def _snake_case (... | 631 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaa... | 631 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 631 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
SCREAMING_SNAKE_CASE__ = False
class _UpperCAmelCase ( unittest.TestCase ):
def _sna... | 631 | 1 |
from torch import nn
class _UpperCAmelCase ( nn.Module ):
def __init__( self : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : str):
super().__init__()
SCREAMING_SNAKE_CASE_ :Optional[Any] = class_size
SCREAMING_SNA... | 631 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 631 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( lowercase ):
def __init__( self : int , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperC... | 631 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampl... | 631 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampl... | 631 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _UpperCAmelCase ( yaml.SafeLoader ):
def _snake_case ( self : Dict , UpperCAmelCase : Union[str, Any]):
SCREAMING_SNAKE_CASE_ :List[Any] ... | 631 | 1 |
from __future__ import annotations
def lowercase ( a ):
'''simple docstring'''
if len(a ) == 0:
return []
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :List[str] = min(a ), max(a )
SCREAMING_SNAKE_CASE_ :Union[str, Any] = int(max_value - min_value )... | 631 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPEN... | 631 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,... | 631 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = "examples/"
SCREAMING_SNAKE_CASE__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\... | 631 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPEN... | 631 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def lowercase ( a , a , a , a , a , a , a ):
'''simple docstring'''
global process_lock
# we perfor... | 631 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class _... | 631 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE__ = (720, 1_280) # Height, Width
SCREAMING_SNAKE_CASE__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__ ... | 631 | 1 |
def lowercase ( a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Optional[int] = 0
while len(a ) > 1:
SCREAMING_SNAKE_CASE_ :str = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
SCREAMING_SNAKE_CASE_ :Optional[Any] ... | 631 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transforme... | 631 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
def _snake_case ( self : Union[str, Any]):
SCREAMING... | 631 |
def lowercase ( a , a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [False] * len(a )
SCREAMING_SNAKE_CASE_ :List[Any] = []
queue.append(a )
SCREAMING_SNAKE_CASE_ :int = True
while queue:
SCREAMING_SNAKE_CASE_... | 631 | 1 |
import string
import numpy
def lowercase ( a , a ):
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , a )
class _UpperCAmelCase :
lowerCamelCase_ : Union[str, Any] = string.ascii_uppercase + string.digits
# This cipher takes alph... | 631 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
SCREAMING_SNAKE_CASE__ = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\... | 631 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",... | 631 |
import qiskit
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
SCREAMING_SNAKE_CASE_ :Union[str, Any] = qiskit.QuantumCircuit(a , a... | 631 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transform... | 631 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( s... | 631 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 631 |
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,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_owlvit": [
"OWLV... | 631 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenize... | 631 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_plan... | 631 | 1 |
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE__ = 300 # TEMPERATURE (unit = K)
def lowercase ( a , a , a , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("Donor concentration should be positive" )
... | 631 |
def lowercase ( a = 50 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[r... | 631 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 631 |
from __future__ import annotations
import math
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[Any] = u
for i in range(1 , a ):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = temp * (u - i)
return temp
def lowercase ( ... | 631 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.jso... | 631 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase ( a )... | 631 | 1 |
import baseaa
def lowercase ( a ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def lowercase ( a ):
'''simple docstring'''
return baseaa.aaadecode(a ).decode("utf-8" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 631 |
from timeit import timeit
def lowercase ( a ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
SCREAMING_SNAKE_CASE_ :Optional[int] = 0
while number:
number &= number - 1
result += 1
return result
def lower... | 631 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowercase ( a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Tuple ... | 631 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_... | 631 | 1 |
from __future__ import annotations
def lowercase ( a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Dict = [True] * limit
SCREAMING_SNAKE_CASE_ :int = False
SCREAMING_SNAKE_CASE_ :List[Any] = False
SCREAMING_SNAKE_CASE_ :Optional[int] ... | 631 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
def _snake_case ( self : Union[str, Any]):
SCREAMING... | 631 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( s... | 631 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaa... | 631 | 1 |
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,
EulerDiscreteSc... | 631 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
SCREAMING_SNAKE_CASE__ = False
class _UpperCAmelCase ( unittest.TestCase ):
def _sna... | 631 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperC... | 631 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 631 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
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 ConfigTester
from ...test_mo... | 631 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampl... | 631 | 1 |
import sys
import turtle
def lowercase ( a , a ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase ( a , a , a , a , ):
'''simple docstring'''
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_p... | 631 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _UpperCAmelCase ( yaml.SafeLoader ):
def _snake_case ( self : Dict , UpperCAmelCase : Union[str, Any]):
SCREAMING_SNAKE_CASE_ :List[Any] ... | 631 | 1 |
from math import factorial
def lowercase ( a , a , a ):
'''simple docstring'''
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers" )
if not i... | 631 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPEN... | 631 | 1 |
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 _UpperCAmelCase ( lowercase , unittest.TestCase ):
lo... | 631 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = "examples/"
SCREAMING_SNAKE_CASE__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\... | 631 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 631 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def lowercase ( a , a , a , a , a , a , a ):
'''simple docstring'''
global process_lock
# we perfor... | 631 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def lowercase ( a , a , a , a , a , a , a ):
'''simple docstring'''
global process_lock
# we perfor... | 631 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE__ = (720, 1_280) # Height, Width
SCREAMING_SNAKE_CASE__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__ ... | 631 | 1 |
def lowercase ( a ):
'''simple docstring'''
if not isinstance(a , a ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0... | 631 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transforme... | 631 | 1 |
from heapq import heappop, heappush
import numpy as np
def lowercase ( a , a , a , a , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Tuple = grid.shape
SCREAMING_SNAKE_CASE_ :List[Any] = [-1, 1, 0, 0]
SCREAMING... | 631 |
def lowercase ( a , a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [False] * len(a )
SCREAMING_SNAKE_CASE_ :List[Any] = []
queue.append(a )
SCREAMING_SNAKE_CASE_ :int = True
while queue:
SCREAMING_SNAKE_CASE_... | 631 | 1 |
from math import isclose, sqrt
def lowercase ( a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = point_y / 4 / point_x
SCREAMING_SNAKE_CASE_ :Optional[int] = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
SCREAMING_SNAKE_CA... | 631 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
SCREAMING_SNAKE_CASE__ = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\... | 631 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( a , a , a=None ):
'''simple docstring'''
assert torch... | 631 |
import qiskit
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
SCREAMING_SNAKE_CASE_ :Union[str, Any] = qiskit.QuantumCircuit(a , a... | 631 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.... | 631 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( s... | 631 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, l... | 631 |
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,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_owlvit": [
"OWLV... | 631 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, B... | 631 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_plan... | 631 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.model... | 631 |
def lowercase ( a = 50 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[r... | 631 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
SCREAMING_SNAKE_CASE__ = False
class _UpperCAmelCase ( unittest.TestCase ):
def _sna... | 631 |
from __future__ import annotations
import math
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[Any] = u
for i in range(1 , a ):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = temp * (u - i)
return temp
def lowercase ( ... | 631 | 1 |
from __future__ import annotations
from typing import Any
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : float = 0):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_... | 631 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase ( a )... | 631 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTes... | 631 |
from timeit import timeit
def lowercase ( a ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
SCREAMING_SNAKE_CASE_ :Optional[int] = 0
while number:
number &= number - 1
result += 1
return result
def lower... | 631 | 1 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
SCREA... | 631 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_... | 631 | 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_CHECKING:... | 631 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
def _snake_case ( self : Union[str, Any]):
SCREAMING... | 631 | 1 |
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