code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import itertools
import math
def __UpperCamelCase ( _A ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
retu... | 278 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 0 |
"""simple docstring"""
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 _A ( UpperCamelCase_ : Tuple) -> int:
'... | 17 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (... | 29 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common impor... | 181 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> np.array:
'''simple docstring'''
lowerCamelCase__ = int(np.ceil((x_end - xa) / step_size ... | 209 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 0 |
def a__ ( _UpperCamelCase : float ):
return 10 - x * x
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ):
if equation(__a ) * equation(__a ) >= 0:
raise ValueError('''Wrong space!''' )
__lowerCamelCase ... | 330 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_pr... | 276 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 327 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_A = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCamelCase ( da... | 171 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : List[Any] = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : Optional[Any] = 2 * i + 1
_a : Optional[int] = 2... | 235 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 39 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 0 |
"""simple docstring"""
import os
import sys
import unittest
a = 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... | 315 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 | 0 |
import operator as op
_A = '''scaler.pt'''
_A = '''pytorch_model'''
_A = '''random_states'''
_A = '''optimizer'''
_A = '''scheduler'''
_A = '''pytorch_model.bin'''
_A = '''pytorch_model.bin.index.json'''
_A = '''model.safetensors'''
_A = '''model.safetensors.... | 278 |
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_a : Optional[Any] = 0
_a : List[Any] = len(__a ) - 1
_a : List[str] = 0
while index >= 0:
_a : Dict = ... | 235 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrate... | 17 |
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__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase ... | 29 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 0 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None ) -> Optional[Any]:
UpperCAmelCase__ : str = (path or []) + [u]
for v ... | 181 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_a = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "output.dense",
"atte... | 209 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 0 |
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Optional[Any] ):
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(__a ):
for j in range(__a ):
if dist[i][j] != float('''inf''' ):
... | 330 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 | 0 |
'''simple docstring'''
A__: Optional[int] = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''Trans... | 276 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase_ :
"""simple docstring"""
pass
| 235 | 0 |
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
from ...utils import logging
... | 327 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerF... | 171 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 0 |
def __A ( __lowerCAmelCase )-> int:
"""simple docstring"""
assert isinstance(__a , __a ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
_UpperCAmelCase = F"""The... | 39 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 235 | 0 |
"""simple docstring"""
a = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_avail... | 315 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 0 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 appli... | 278 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _A ( UpperCamelCase_ : Optional[Any... | 17 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 29 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
fro... | 181 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = FileLock(str(tmpdir / '''foo.lock''' ) )
lowerCamelCase__ = FileLock(str... | 209 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 330 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list[int] ,_UpperCAmelCase : list[int] ,_UpperCAmelCase : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__a ) )
... | 276 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 0 |
import requests
_SCREAMING_SNAKE_CASE = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[str] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list i... | 327 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase ) -> "list[int]":
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
UpperCAmelCase__ : Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCA... | 171 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : List[Any] = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : Optional[Any] = 2 * i + 1
_a : Optional[int] = 2... | 235 | 0 |
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False )-> Optional[Any]:
"""simple docstring"""
if isinstance(__a , __a ) and isinstance(__a , __a ):
_UpperCAmelCase = len(set_a.intersection(__a ) )
if altern... | 39 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chann... | 315 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 | 0 |
import math
def __UpperCamelCase ( _A , _A ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__a )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y ... | 278 |
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_a : Optional[Any] = 0
_a : List[Any] = len(__a ) - 1
_a : List[str] = 0
while index >= 0:
_a : Dict = ... | 235 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__lowercase ):
"""simple docstring"""
__UpperCAmelCase : Any = ["torch", "scipy"]
def __init__( self : Optional[int], *UpperCAmelCase__ : Dict, ... | 17 |
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__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 0 |
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,
SkipBatchSampler,
... | 29 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 0 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCamelCase_ ( pl.LightningModule ):
def __init__( self : List[str] ... | 181 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 | 0 |
from collections.abc import Callable
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase = None ):
'''simple docstring'''
lowerCamelCase__ = []
# Stores indexes of each item for supporting updates and deletion.
lowe... | 209 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow,... | 330 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 | 0 |
from manim import *
class __lowerCAmelCase ( lowerCAmelCase):
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :Tuple = Rectangle(height=0.5 , width=0.5 )
lowercase :Tuple = Rectangle(height=0.46 , width=0.46 ).set_str... | 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[str] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 236 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = "T5Config"
class __lowerCAmelCase ( lowerCAmel... | 236 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {"voca... | 236 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common ... | 236 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingfa... | 236 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase):
_a = ['''inp... | 236 |
import math
import sys
def UpperCAmelCase__ ( lowerCamelCase ):
if number != int(lowerCamelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
... | 236 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import C... | 236 |
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... | 236 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase):
... | 236 |
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
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCas... | 236 | 1 |
from math import factorial
def UpperCAmelCase__ ( lowerCamelCase = 20 ):
lowercase :Any = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowercase :List[str] = n // 2
return int(factorial(lowerCamelCase ) / (factorial(low... | 236 |
import torch
def UpperCAmelCase__ ( ):
if torch.cuda.is_available():
lowercase :Optional[int] = torch.cuda.device_count()
else:
lowercase :Dict = 0
print(F"Successfully ran on {num_gpus} GPUs" )
if __name__ == "__main__":
main()
| 236 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.... | 236 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_UpperCAmelCase : Dict = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase = 1000 ):
lowercase :Dict = 2**power
lowercase :List[Any] = str(lowerCamelCase )
lowercase :Tuple = list(lowerCamelCase )
lowercase :Any = 0
for i in list_num:
sum_of_num += int(lowerCamelCase )
... | 236 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accel... | 236 | 1 |
_UpperCAmelCase : int = range(2, 20 + 1)
_UpperCAmelCase : List[Any] = [10**k for k in range(ks[-1] + 1)]
_UpperCAmelCase : dict[int, dict[int, list[list[int]]]] = {}
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
lowercase :Optional[int]... | 236 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : int = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
rai... | 236 | 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 imp... | 236 |
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 __lowerCAmelCase ( datasets.BuilderConfig):
_a = None
class __lowerCAmel... | 236 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[str] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 236 |
_UpperCAmelCase : Tuple = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase__ ( lowerCamelCase ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase ) )
def UpperCAmelCase__ ( ):
return sum(
number
for number in range(1... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(lowerCamelCase ) * abs(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True... | 236 |
import numpy
# List of input, output pairs
_UpperCAmelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_UpperCAmelCase : Optional[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase : Tuple = [2, 4, 1, 5... | 236 | 1 |
_UpperCAmelCase : Tuple = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase__ ( lowerCamelCase ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase ) )
def UpperCAmelCase__ ( ):
return sum(
number
for number in range(1... | 236 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.t... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase = 100 ):
lowercase :int = (n * (n + 1) // 2) ** 2
lowercase :List[str] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 236 |
import os
import pytest
from attr import dataclass
_UpperCAmelCase : List[str] = "us-east-1" # defaults region
@dataclass
class __lowerCAmelCase :
_a = 42
_a = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_a = {
... | 236 | 1 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self: Union[str, Any] , _lowerCAmelCase: int ):
lowercase :Optional[int] = data
lowercase :Node | None = None
lowercase :Node | None = None
... | 236 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = "cpu", lowerCamelCase = None ):
lowercase :Optional[Any] = torch.load(lowerCamelCase, map_location=lowerCamelCase )
for k, v in tqdm(state_dict.items... | 236 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCAmelCase__ ( lowerCamelCase ):
return (data["data"], data... | 236 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging... | 236 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import A... | 236 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# Return True if there is node that has not iterated.
lowercase :Union[str, Any] = [False] * len(lowerCamelCase )
lowercase :Union[str, Any] = []
queue.append(lowerCamelCase ... | 236 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_UpperCAmelCase : Optional[int] = "sshleifer/bart-tiny-random"
_... | 236 |
import os
from math import logaa
def UpperCAmelCase__ ( lowerCamelCase = "base_exp.txt" ):
lowercase :float = 0
lowercase :str = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase ), lowerCamelCase ) ) ):
lowercase , low... | 236 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: Union[str, Any] , _lowerCAmelCase: str , _lowerCAmelCase: Any ):
lowercase :Union[str, An... | 236 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :List[str] = ""
for word_or_phrase in separated:
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separa... | 236 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCAmelCase__ ... | 236 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 236 | 1 |
from __future__ import annotations
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase, n - 1 )
rec_insertion_sort(lowerCamelCase, n - 1 )
... | 236 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExt... | 236 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_UpperCAmelCase : str = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=DataR... | 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[str] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 236 | 1 |
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
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : int = {
... | 236 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {"voca... | 236 | 1 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if not arr:
return None, None, 0
if low == high:
return low, ... | 236 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingfa... | 236 | 1 |
from __future__ import annotations
import numpy as np
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase , lowercase :Tuple = np.shape(lowerCamelCase )
if rows != columns:
lowercase :Dict = (
"'table' has to be of square shaped array but got a "
... | 236 |
import math
import sys
def UpperCAmelCase__ ( lowerCamelCase ):
if number != int(lowerCamelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
... | 236 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_... | 236 |
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... | 236 | 1 |
from torch import nn
class __lowerCAmelCase ( nn.Module):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Any , _lowerCAmelCase: int ):
super().__init__()
lowercase :List[Any] = class_size
lowercase :Optional[int... | 236 |
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
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCas... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase ):
if not numbers:
return 0
if not isinstance(lowerCamelCase, (list, tuple) ) or not all(
isinstance(lowerCamelCase, lowerCamelCase ) for number in numbers ):
raise ValueError("numbers must be an iterable of integers" )
... | 236 |
import torch
def UpperCAmelCase__ ( ):
if torch.cuda.is_available():
lowercase :Optional[int] = torch.cuda.device_count()
else:
lowercase :Dict = 0
print(F"Successfully ran on {num_gpus} GPUs" )
if __name__ == "__main__":
main()
| 236 | 1 |
_UpperCAmelCase : dict[tuple[int, int, int], int] = {}
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no da... | 236 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_UpperCAmelCase : Dict = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["... | 236 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : int = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
rai... | 236 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accel... | 236 | 1 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_UpperCAmelCase : T... | 236 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : int = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
rai... | 236 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 236 |
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 __lowerCAmelCase ( datasets.BuilderConfig):
_a = None
class __lowerCAmel... | 236 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logg... | 236 |
_UpperCAmelCase : Tuple = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase__ ( lowerCamelCase ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase ) )
def UpperCAmelCase__ ( ):
return sum(
number
for number in range(1... | 236 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingfa... | 236 |
import numpy
# List of input, output pairs
_UpperCAmelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_UpperCAmelCase : Optional[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase : Tuple = [2, 4, 1, 5... | 236 | 1 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils ... | 236 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.t... | 236 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_UpperCAmelCase : Tuple = ["small", "medium", "large"]
_UpperCAmelCase : List[str] = "lm_head.decoder.weight"
_UpperCAmelCase : Dict = "lm_head.weight"
def UpperCAmelCase__ ( lowerCamelCase, lowerCamel... | 236 |
import os
import pytest
from attr import dataclass
_UpperCAmelCase : List[str] = "us-east-1" # defaults region
@dataclass
class __lowerCAmelCase :
_a = 42
_a = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_a = {
... | 236 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 236 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = "cpu", lowerCamelCase = None ):
lowercase :Optional[Any] = torch.load(lowerCamelCase, map_location=lowerCamelCase )
for k, v in tqdm(state_dict.items... | 236 | 1 |
from __future__ import annotations
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :Union[str, Any] = 0.00
lowercase :int = 0
for resistor in resistors:
if resistor <= 0:
lowercase :List[str] = F"Resistor at index {index} has a negative or zero value... | 236 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging... | 236 | 1 |
import os
import sys
import unittest
_UpperCAmelCase : 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_bac... | 236 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# Return True if there is node that has not iterated.
lowercase :Union[str, Any] = [False] * len(lowerCamelCase )
lowercase :Union[str, Any] = []
queue.append(lowerCamelCase ... | 236 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_UpperCAmelCase : Dict = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["... | 236 |
import os
from math import logaa
def UpperCAmelCase__ ( lowerCamelCase = "base_exp.txt" ):
lowercase :float = 0
lowercase :str = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase ), lowerCamelCase ) ) ):
lowercase , low... | 236 | 1 |
from collections.abc import Sequence
def UpperCAmelCase__ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
lowercase :int = nums[0]
for i in range(1, len(lowerCamelCase ) ):
lowercase :int ... | 236 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :List[str] = ""
for word_or_phrase in separated:
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separa... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# Return True if there is node that has not iterated.
lowercase :Union[str, Any] = [False] * len(lowerCamelCase )
lowercase :Union[str, Any] = []
queue.append(lowerCamelCase ... | 236 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 236 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExt... | 236 | 1 |
import datasets
_UpperCAmelCase : Tuple = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n ... | 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[str] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 236 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 236 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {"voca... | 236 | 1 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase = False ):
if radian_mode:
return [magnitude * cos(lowerCamelCase ), magnitude * sin(lo... | 236 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingfa... | 236 | 1 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self: Optional[Any] , _lowerCAmelCase: int = 0 ):
lowercase :List[str] = key
def SCREAMING_SNAKE_CASE ( self: Tuple , _lowerCAmelCase: str , ... | 236 |
import math
import sys
def UpperCAmelCase__ ( lowerCamelCase ):
if number != int(lowerCamelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
... | 236 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {name: getattr(transformers, name + "Fast") for nam... | 236 |
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... | 236 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_Upp... | 236 |
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
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCas... | 236 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_UpperCAmelCase : int = get_tests_dir()... | 236 |
import torch
def UpperCAmelCase__ ( ):
if torch.cuda.is_available():
lowercase :Optional[int] = torch.cuda.device_count()
else:
lowercase :Dict = 0
print(F"Successfully ran on {num_gpus} GPUs" )
if __name__ == "__main__":
main()
| 236 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import... | 236 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_UpperCAmelCase : Dict = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["... | 236 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Tuple = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if n... | 236 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accel... | 236 | 1 |
import math
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :List[Any] = [True] * n
lowercase :int = False
lowercase :List[Any] = False
lowercase :Union[str, Any] = True
for i in range(3, int(n**0.5 + 1 ), 2 ):
lowercase :Tuple ... | 236 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : int = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
rai... | 236 | 1 |
import math
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :List[Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowerCamelCase )
def UpperCAmelCase__ ( lowerCamelCase = 1 / 12345 ):
lowercase :Tup... | 236 |
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 __lowerCAmelCase ( datasets.BuilderConfig):
_a = None
class __lowerCAmel... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :Dict = len(lowerCamelCase )
lowercase :Optional[Any] = len(matrix[0] )
lowercase :Any = min(lowerCamelCase, lowerCamelCase )
for row in range(lowerCamelCase ):
# Check if diagonal element... | 236 |
_UpperCAmelCase : Tuple = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase__ ( lowerCamelCase ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase ) )
def UpperCAmelCase__ ( ):
return sum(
number
for number in range(1... | 236 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 236 |
import numpy
# List of input, output pairs
_UpperCAmelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_UpperCAmelCase : Optional[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase : Tuple = [2, 4, 1, 5... | 236 | 1 |
from datetime import datetime
import requests
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :Union[str, Any] = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
lowercase :Optional[int] = requests.get(base_url + url ).json()[0]["urls"][0]["sr... | 236 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.t... | 236 | 1 |
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :int = []
lowercase :List[str] = []
lowercase :str = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # Priority of each operator
lowercase :Optional[Any] = len(l... | 236 |
import os
import pytest
from attr import dataclass
_UpperCAmelCase : List[str] = "us-east-1" # defaults region
@dataclass
class __lowerCAmelCase :
_a = 42
_a = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_a = {
... | 236 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnn... | 236 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = "cpu", lowerCamelCase = None ):
lowercase :Optional[Any] = torch.load(lowerCamelCase, map_location=lowerCamelCase )
for k, v in tqdm(state_dict.items... | 236 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft... | 236 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging... | 236 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.