code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def UpperCAmelCase ( a_ = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
__A = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , a_ ):
phi[j] -= ... | 15 |
from maths.prime_check import is_prime
def snake_case_ ( lowerCAmelCase_ : int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
__lowercase : Dict = F"Input value of [number={number}] must be an integer"
raise TypeError(lower... | 233 | 0 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Tuple:
return getitem, k
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str... | 328 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'la... | 328 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_m... | 125 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...te... | 142 | 0 |
import os
def __UpperCamelCase ( ) ->Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =os.path.join(os.path.dirname(lowerCAmelCase__ ) , """num.txt""" )
with open(lowerCAmelCase__ ) as file_hand:
return str... | 361 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__A : Any = '▁'
__A : Union[s... | 49 | 0 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 336 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 ...tes... | 336 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( lowerCAmelCase_... | 30 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : List[str] ... | 30 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
A : List[Any] = logging.get_logger(__na... | 305 |
# 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 require... | 305 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def a__ ( lowercase : float, lowercase : float, lowercase : float ) -> dict[str, float]:
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
r... | 350 |
'''simple docstring'''
import math
def a__ ( lowercase : float, lowercase : float ) -> float:
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of init... | 287 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extrac... | 4 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@require_torch
... | 317 | 0 |
def A_ ( _lowerCAmelCase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCamelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Union[str, Any] = input_str.replace(" " , "" )
... | 140 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 140 | 1 |
'''simple docstring'''
from timeit import timeit
snake_case_ : List[str] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is va... | 83 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : List[str] = 5_0000
_lowerCamelCase : Optional[int] = 5000
_lowerCamelCase ,_lowerCamelCase : int = os.pa... | 28 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeni... | 259 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [False] * len(_UpperCamelCase )
__lowerCAmelCase = []
queue.append(_UpperCamelCase )
__... | 259 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import ... | 189 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from... | 189 | 1 |
'''simple docstring'''
import math
import qiskit
def _lowerCAmelCase ( __snake_case : int = 1 , __snake_case : int = 1 , __snake_case : int = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__snake_case , __sna... | 190 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase = None):
'''simple docstring'''
__A : str = value
__A ... | 190 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMS... | 347 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
f... | 347 | 1 |
"""simple docstring"""
def UpperCamelCase__( UpperCamelCase__ : Any )->Tuple:
A__ = [0] * len(UpperCamelCase__ )
A__ = []
A__ = []
A__ = 0
for values in graph.values():
for i in values:
... | 357 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
A__ = [
'''safety_checker/pytorch_model.bin''',
... | 39 | 0 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ ): # This function is recursive
__SCREAMING_SNAKE_CASE : Optional[int] = len(lowercase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion... | 9 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch model
... | 49 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str , __lowercase : bool = False ) -> str:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_UpperCAmelCase = f'Expected string as input, fo... | 156 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__SCREAMING_SNAKE_CASE :Tuple = '''\
'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = ... | 156 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 30 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 30 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassific... | 58 |
"""simple docstring"""
from ....utils import logging
__snake_case : Optional[Any] = logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: str , _SCREAMING_SNAKE_CA... | 58 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCAmelCase : Optional[int] = 10
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ... | 13 |
import warnings
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 | 0 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE_ = False
class a ( unittest.TestCase ):
pass
@slow
@req... | 189 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = [
['attention', 'attn'],
['enco... | 189 | 1 |
import os
import sys
import unittest
_UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 140 | def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : int ,__lowercase : int ,__lowercase : list[int] ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in... | 140 | 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 applica... | 141 |
from typing import Any
def UpperCamelCase (lowercase_: list ) -> list[Any]:
if not input_list:
return []
A__ : Any = [input_list.count(lowercase_ ) for value in input_list]
A__ : List[Any] = max(lowercase_ ) # Gets the maximum count in the input list.
# G... | 141 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at http... | 340 |
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 i... | 340 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = set(range(3 , _SCREAMING_SNAKE_CASE , 2 ) )
primes.add(2 )
for p in range(3 , _SCREAMING_SNAKE_C... | 193 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
class UpperCamelCase__ :
'''simple docstring'... | 193 | 1 |
'''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 ... | 190 |
'''simple docstring'''
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 imp... | 190 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
return x + 2
class _lowerCAmelCase ( unittest.TestCase ):
'''si... | 368 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from ac... | 270 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
... | 157 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import D... | 182 |
"""simple docstring"""
lowerCamelCase__ = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader imp... | 182 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics... | 156 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__lowerCAmelCase : Any = (3, 9, -11, 0, 7, 5, 1, -1)
__lowerCAmelCase : Tuple = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __lowerCAmelCase :
"""... | 156 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
)
... | 365 |
import itertools
import math
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all ... | 329 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IM... | 58 |
'''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 OptionalDep... | 58 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import Pr... | 87 |
UpperCamelCase__ = {
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
UpperCamelCase__ = {
"m": 0,
"km": 3,
... | 87 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switc... | 189 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowerCamelCase : int =TypeVar('''T''')
class __a ( Generic[T] ):
_lowerCAmelCase : deque[T] # Cache store of keys
_lowerCAmelCase ... | 189 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 87 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 87 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_fl... | 141 |
'''simple docstring'''
import math
class lowerCAmelCase :
def snake_case ( self : Optional[int] , __lowercase : list[list[float]] , __lowercase : list[int] ):
"""simple docstring"""
__lowercase =0.0
... | 141 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 355 | """simple docstring"""
from collections import namedtuple
__lowerCamelCase = namedtuple("from_to", "from_ to")
__lowerCamelCase = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.0_0_1, 10_00),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.0_0_4_5_4, 2_6_4... | 154 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 193 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = (DDIMParallelScheduler,)
__SCREAMING_SNAKE_CASE = (('''eta''', 0.0)... | 193 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCAmelCase :Optional[int] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add... | 357 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str , lowerCAmelCase : Any , lowerCAmelCase : Any=False ):
"""simple docstring"""
if isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : ... | 275 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 346 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 270 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _lowerCamelCase ( lowercase : int = 100_0000 , lowercase : int = 10 ) -> int:
_a = defaultdict(lowercase )
for outer_width in range(3 , ... | 346 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ : Dict = lo... | 346 | 1 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 182 | from math import ceil
def A ( _lowercase = 1_001 ):
SCREAMING_SNAKE_CASE : Any = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 2 * i + 1
SCREAMING_SNAKE_CASE ... | 182 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A : Any = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDep... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
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 import Dataset
from transf... | 6 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""... | 329 | 0 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE : Union[str, Any] ... | 363 |
def UpperCamelCase_( lowerCamelCase_ = 1000 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 84 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_n... | 87 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOn... | 87 | 1 |
"""simple docstring"""
import numpy as np
def a__ ( __SCREAMING_SNAKE_CASE ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class snake_case ( pl.LightningModule ):
def __init__( self : str , UpperCamelC... | 108 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : str , **_lowerCamelCase : Tuple):
lowercase__ : Tuple = AutoConfig.from_pretrained(_lowerCamelCase , **_... | 87 | import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class snake_case_ ( unittest.TestCase ):
@require_to... | 87 | 1 |
from scipy.stats import pearsonr
import datasets
_lowerCAmelCase : Any = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption tha... | 366 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
... | 70 | 0 |
from __future__ import annotations
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> None:
'''simple docstring'''
A__ = data
A__ = None
A__ = None
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: ... | 68 |
from collections import namedtuple
__A : List[str] = namedtuple('from_to', 'from_ to')
__A : int = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.0_01, 10_00),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_04_54, 2_64.1_72),
'cubicyard': from_to(0.7_64_55, 1.3_07_9... | 154 | 0 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test... | 12 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageI... | 12 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ : List[s... | 63 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''ut/deta''': '''https://huggingface.co/ut/... | 191 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _a ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
... | 191 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 , SCREAMING_SNAKE_CASE__ : int = 10 ):
'''simple docstring'''
UpperCAmelCase__ = defaultdict(SCREAMING_SNAKE_CASE__ )
... | 346 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ = logging.getLogger(__name__)
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = argparse.Argumen... | 346 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a__: Any = logging.get_logger(__name__)
class ... | 354 |
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int )->float:
return base * power(UpperCamelCase__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
a__:... | 39 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = field(... | 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 | 1 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSav... | 298 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sche... | 298 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
... | 127 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )... | 84 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[int] =logging.get_logger(__name__)
__snake_case : str ={
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
... | 369 |
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 TokenizerTest... | 94 | 0 |
"""simple docstring"""
import qiskit
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCase : int = qiskit.Aer.get_backend("aer_simulator" )
lowerCAmelCase : Tuple = ... | 108 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSam... | 108 | 1 |
# flake8: noqa
# Lint as: python3
__lowercase = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enabl... | 105 | from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrain... | 105 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCamelCase = '''scheduler_config.json'''
class __lowercase (snake_case_ ):
... | 275 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_co... | 70 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
snake_case__ = 42
snake_case__ = None
snake_case__ = None
__UpperCamelCase : Tuple = namedtuple('''Coi... | 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
fr... | 12 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
el... | 12 | 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 import ... | 257 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCamelCase__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCAmelC... | 257 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCamelCase_ = {
"sample_size": 3_2,
"in_channels": 3,
"out_channels": 3,
"layers_per_blo... | 191 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConf... | 191 | 1 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __magic_name__ ( unittest.TestCase ):
def __lowercase ( self : L... | 107 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''dis... | 107 | 1 |
'''simple docstring'''
A__ : Optional[int] ='''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
A__ : int =[{'''type''': '''code''', '''c... | 70 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( lowerCamelCase__ ):
snake_case_ = '''ClapFeatureExtractor'''
snake_case_ = ('''RobertaTokenizer''', '''RobertaTokenizerFast''... | 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 | 0 |
"""simple docstring"""
def _lowerCamelCase( a ):
__a = [[0 for _ in range(a )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__a = 1
for n in range(m + 1 ):
for k in range(1 , a ):
... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:str = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CO... | 261 | 1 |
"""simple docstring"""
import sys
from collections import defaultdict
class _A :
"""simple docstring"""
def __init__( self : Optional[Any]):
a : Tuple = []
def __snake_case ( self : Optional[Any] , __UpperCAmelCase ... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
... | 226 | 0 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acc... | 106 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 1000 ):
"""simple docstring"""
a , a :int = 1, 1
a :Any = 2
while True:
a :Optional[int] = 0
a :str = fa + fa
... | 94 | 0 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__ : Optional[Any] = {
"""en... | 356 |
from math import ceil, sqrt
def _a ( UpperCAmelCase = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__ : Any = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__ : ... | 265 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
Ro... | 105 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class __UpperCamelCase :
def __init__( self , lowerCAmelCase__ ) -> Optional[int]:
a : List[str] = str(id_ )
a : Opti... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Union[str, Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 51 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __magic_name__ ( __lowerCAmelCase):
def __init__( self : Dict , l... | 51 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
stooge(_UpperCamelCase , 0 , len(_UpperCamelCase ) - 1 )
return arr
def a ( snake_case__: List[Any] , snake_case__: List[str] , snake_case__: Optional[i... | 30 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 330 | 0 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : Union[str, Any] = '▁'
_snake_... | 207 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def a_ ( lowerCAmelCase_ : Optional[Any] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.or... | 207 | 1 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
... | 257 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, ... | 257 | 1 |
def __lowerCAmelCase ( a__ ) -> Any:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
A : Union[str, Any] = int(input('Enter nu... | 369 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__ ) -> np.ndarray:
__a = cva.getAffineTransform(a__ , a__ )
return cva.warpAffine(a__ , a__ ... | 33 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput... | 107 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matr... | 107 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
... | 355 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__snake_case : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies ... | 136 | 0 |
class A__ :
"""simple docstring"""
def __init__( self , lowercase) -> None:
'''simple docstring'''
a__ : Optional[Any] = len(lowercase)
a__ : Tuple = [0] * len_array
if len_array > 0:
a__ : List... | 99 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , _lowercase : Optional[Any] ):
__UpperCAmelCase = str(id_ )
__UpperCAmelCase = None
... | 332 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cl... | 351 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("""String lengths must match!""" )
_lowerCAmelCase... | 220 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ = 10_00 ) -> int:
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f'{solution() =... | 247 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE = "src/diffusers"
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.... | 247 | 1 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def _lowerCAmelCase ( UpperCAmelCase__ : int ) ->int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __n... | 296 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
A_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def __init__( self : Optional[int] , snake_case : List[str]=None , ... | 296 | 1 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_lowerCAmelCase = datasets.logging.get_logger(__name__)
_lowerCAmelCase = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Tex... | 37 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stab... | 265 | 0 |
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 _A ( self : List[Any] ):
UpperCamelCase :Union[str, Any] = inspe... | 364 |
from string import ascii_lowercase, ascii_uppercase
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> str:
"""simple docstring"""
if not sentence:
return ""
UpperCamelCase :str = dict(zip(__magic_name__ , __magic_name__ ) )
return lower_to_u... | 62 | 0 |
def A (__A : float , __A : float ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.25) = }")
print(f"{price_plus_tax(125.50, 0.05) = }")
| 51 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 354 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueE... | 310 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class _... | 207 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = [1]
lowercase__ , lowercase__ , lowercase__ = 0, 0, 0
lowercase__ = ugly_nums[ia] * 2
lowercase__ = ugly_nums[ia] * 3
lowercase__ = ugly_nums[ia] * 5
for _ in range(1... | 207 | 1 |
def snake_case_ (__A : int ) -> bool:
if not isinstance(__A , __A ):
__lowerCAmelCase : Union[str, Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__A )
if number < 0:
return False
__lowerCAm... | 139 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__UpperCAmelCase = logging.get_logger(__name__)
de... | 139 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _snake_case ( unittest.Te... | 85 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : List[Any] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''M... | 33 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
A : Optional[int] = logging.get_logger(__name__)
def __lowerCamelCase ( __a :Dict ) ... | 276 |
import unittest
import numpy as np
def __lowerCamelCase ( __a :np.ndarray , __a :np.ndarray , __a :np.ndarray , __a :np.ndarray | None = None , ) -> np.ndarray:
"""simple docstring"""
A__ = np.s... | 276 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE__ = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n ... | 46 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Any:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__lowerCAmelCase , int(b / 2 ) ) * actual_power(__l... | 136 | 0 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 360 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase__ : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __ini... | 287 | 0 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 247 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
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] + arr[mi + 1 :... | 247 | 1 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 290 | 1 |
import math
import unittest
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1... | 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euc... | 296 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 366 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
a : Tuple = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "... | 79 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.