code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : Optional[int] = [1]
for i in range(2 , _UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < ... | 60 |
from typing import Any
class __lowercase :
def __init__( self , lowercase_) -> str:
__snake_case = data
__snake_case = None
def __repr__( self) -> str:
return F"Node({self.... | 313 | 0 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_lowerCAmelCase = 2048
_lowerCAmelCase = 4096
_lowerCAmelCase = 42
_lowerCAmelCase = os.environ.pop("PROCESS_TRAIN", "false")
_lowerCAmelCase = {"null": 0, "short": 1, "long":... | 245 |
'''simple docstring'''
from collections.abc import Callable
def UpperCamelCase ( a , a , a ) -> float:
'''simple docstring'''
__magic_name__ = a
__magic_name__ = b
if function(a ) == 0: # one of the a or b is a root for the function
... | 245 | 1 |
lowerCamelCase : Optional[Any] = 8.314_4598
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : float ):
'''simple docstring'''
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
... | 70 |
"""simple docstring"""
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__ : int = logging.get_logger(__name... | 238 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class UpperCamelCase ( ctypes.Structure ):
"""simple docstring"""
# _fields is a specific attr expected by ctypes
SCR... | 600 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_lowercase : Union[str, Any] = ... | 600 | 1 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Ima... | 41 |
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... | 228 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 721 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTest... | 109 | 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_cha... | 495 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowercase ( logging.LoggerAdapter ):
@staticmethod
def _snake_case ( lowercase ) -> Optional[Any]:
lowerCAmelCase = PartialState()
return n... | 532 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowercase_ = """Usage of script: script_name <size_of_canvas:int>"""
lowercase_ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperC... | 715 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 0 |
from ... import PretrainedConfig
lowerCamelCase : Dict = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class __lowercase (lowerCamelCase__ ):
"""simple docstring"""
_snake_case = NEZHA_PRETRAINED_... | 587 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 327 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__UpperCAmelCase : Optional[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
__UpperCAmelCase : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class lowerCamelCase :
UpperCAmelCase : ... | 249 |
from manim import *
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
def snake_case_ ( self : int ) -> Tuple:
_a : Optional[int] = Rectangle(height=0.5 , width=0.5 )
_a : Dict = Rectangle(height=0.25 , width=0... | 249 | 1 |
import qiskit
def UpperCamelCase( __UpperCamelCase : int ,__UpperCamelCase : int ):
lowerCAmelCase_ : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
lowerCAmelCase_ : Union[str, Any] ... | 171 |
def UpperCamelCase( __UpperCamelCase : int = 10**12 ):
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : Dict = 1
while numerator <= 2 * min_total - 1:
prev_numer... | 171 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def SCREAMING_SNAKE_CASE ( lowercase_ : Union[str, Any] ):
lowercase = os.path.join(args.tf_model_dir , """pa... | 653 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase... | 653 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_mbart''': ['''MBART_PRETRAINED_... | 84 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils ... | 358 | 0 |
'''simple docstring'''
import random
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : dict = {i: [] for i in range(_lowerCAmelCase )}
# if p... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
import math
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
return math.pow(snake_case__ , 2 ) - a
def a ( snake_case__: float ):
'''simple docstring'''
return 2 * x
def a ( snake_case__... | 97 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_... | 562 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCAmelCase_ : str = re.compile(R'\b(a|an|the)\b', re.UNICODE)
lowerCAmelCase_ : List[str] = None
def _lowerCamelCase ... | 700 |
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _lowerCamelCase ( lowercase : Union[str, Any] , ... | 521 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionS... | 189 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__snake_case = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
... | 189 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanorama... | 277 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOut... | 277 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : int = logging.get_logger(__name__)
lowerCAmelCase__ : List[Any] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-ba... | 347 |
'''simple docstring'''
from math import factorial
def _a ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : float ):
"""simple docstring"""
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or... | 347 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( A ,A ... | 714 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunD... | 425 | 0 |
"""simple docstring"""
import string
import numpy
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE_ )
class lowerCAmel... | 247 |
"""simple docstring"""
from collections.abc import Callable
def lowercase (SCREAMING_SNAKE_CASE_ : Callable[[float], float] , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> float:
SCREAMING_SNAKE_CASE = ... | 247 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a : int = 1_00
_a : List[str] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
p... | 571 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import Ba... | 571 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( snake_case_ ):
_A : Optional[int] = []
if... | 307 |
import inspect
import unittest
from transformers import RegNetConfig
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 ConfigTester
from ...... | 307 | 1 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
A_ = None
try:
import msvcrt
except ImportError:
A_ = None
try:
import fcntl
except ImportError:
A_ ... | 719 |
'''simple docstring'''
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Dict = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( snake_case = 5000 ):
SCREAMING_SNAKE_CASE:int = [(i * (3 * i - 1)) // 2 for i in range(1 , snake_case )]
... | 465 | 0 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
lowercase__ : List[Any] = 3_00 # TEMPERATURE (unit = K)
def __lowercase ( _a , _a , _a , ):
if donor_conc <= 0:
raise ValueError('''Don... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 1 |
import operator as op
SCREAMING_SNAKE_CASE__ = '''scaler.pt'''
SCREAMING_SNAKE_CASE__ = '''pytorch_model'''
SCREAMING_SNAKE_CASE__ = '''random_states'''
SCREAMING_SNAKE_CASE__ = '''optimizer'''
SCREAMING_SNAKE_CASE__ = '''scheduler'''
SCREAMING_SNAKE... | 705 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torc... | 577 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pi... | 560 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple ):
"""simple docstring"""
UpperCamelCase ... | 430 | 0 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase ( UpperCamelCase__ : Tuple="ro" , UpperCamelCase__ : Any="en" , UpperCamelCase__ : Dict="wmt16" , UpperCamelCase__ : Dict=None ):
... | 712 | '''simple docstring'''
from ..utils import DummyObject, requires_backends
class A ( metaclass=UpperCAmelCase ):
a_ = ['''torch''']
def __init__( self : Optional[int] , *__a : List[str] , **__a : Union[str, Any]... | 654 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A__ : Dict = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A__ : Any = [file for file in filepaths i... | 233 |
from math import isclose, sqrt
def _a ( __UpperCamelCase : float ,__UpperCamelCase : float ,__UpperCamelCase : float ):
lowerCAmelCase__ : Union[str, Any] = point_y / 4 / point_x
lowerCAmelCase__ : str = 2 * normal_gradient / (1 + normal_g... | 233 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _A (__a , __a , __a ) -> Optional[Any]:
"""simp... | 720 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
tr... | 176 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _a (self ):
"""simple docstrin... | 182 |
"""simple docstring"""
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase__ : Dict = []
UpperCAmelCase__ : Tuple = 1
while len(lowerCAmelCase ) < 1E6:
constant.append(str(lowerCAmelCase ) )
i += 1
UpperCAmelCase__ ... | 182 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [1]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 0, 0, 0
UpperCAmelCase = ugly_nums[ia] * 2
UpperCAm... | 378 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = ... | 378 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT... | 485 |
from math import asin, atan, cos, radians, sin, sqrt, tan
SCREAMING_SNAKE_CASE = 6_37_81_37.0
SCREAMING_SNAKE_CASE = 6_35_67_52.31_42_45
SCREAMING_SNAKE_CASE = 6378137
def _lowerCamelCase ( __A : float , __A : float , __A : float , __... | 485 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
... | 712 |
from __future__ import annotations
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> list[int]:
lowercase__ : List[str] = [True] * limit
lowercase__ : Union[str, Any] = False
lowercase__ : List[str] = False
... | 298 | 0 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
assert column_title.isupper()
__a = 0
__a = len(_SCREAMING_SNAKE_CASE ) - 1
__a = 0
while index >= 0:
__a = (ord(column_title[index] ) - 64) * pow(26 , ... | 225 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
__a = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
... | 225 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _lowercase ) -> Optional[int]:
_lowercase : Union[str, Any] = str(_snake_case )
return n == n[::-1]
def __UpperCamelCase ( _lowercase = 100_0000 ) -> An... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig'''... | 189 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def a ( __a = "AAPL" ) -> str:
'''simple docstring'''
UpperCamelCase__ :str = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
UpperCamelCase__ :Tuple = BeautifulSoup(re... | 189 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_b... | 700 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> Tuple:
'''simple docstring'''
A__ = [
'encoder.version',
... | 586 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( lowercase , unittest.TestCase ):
lo... | 631 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = "examples/"
SCREAMING_SNAKE_CASE__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\... | 631 | 1 |
"""simple docstring"""
from __future__ import annotations
a : List[Any] = list[tuple[int, int]]
a : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[... | 716 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE ( _lowercase : Optional[int] , _lowercase : Optional[Any] , _lowercase : Union[str, Any] ) ->Dict:
'''... | 31 | 0 |
'''simple docstring'''
UpperCamelCase_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def lowercase__( __UpperCamelCase: dict ,__UpperCamelCase: Dict ... | 28 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_ver... | 654 | 0 |
from __future__ import annotations
def UpperCamelCase ( _a , _a , _a ) -> tuple[float, list[float]]:
'''simple docstring'''
lowercase_ :Optional[int] = list(range(len(_a ) ) )
lowercase_ :int = [v / w for ... | 441 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_... | 441 | 1 |
from collections import defaultdict
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: List[str] = total # total no of tasks (N)
... | 57 | """simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Dict:
_SCREAMING_SNAKE_CASE : Any = ... | 338 | 0 |
"""simple docstring"""
def __snake_case ( ) -> int:
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def __snake_case ( UpperCamelCase__ ) -> str:
"""simple docstring"""
A = 1
A = 2
while... | 91 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
while b:
A , A = b, a % b
return a
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docst... | 91 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase: Union[str, Any] = logging.get_logger(__name__)
class a__( lowerCamelCase__ ):
def __init__( self : Optional[Any] , *__snake_cas... | 526 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase: Tuple = logging.get_logger(__name__)
lowerCAmelCase: Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See al... | 526 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
... | 714 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowercase__( UpperCAmelCase , unitt... | 409 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Any = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available(... | 210 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCamelCase__ ( A : str = "isbn/0140328726" ):
'''simple docstring'''
UpperCAmelCase = olid.strip().strip('''/''' ) # Remove leading/tr... | 210 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.... | 209 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'vocab... | 209 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentPars... | 522 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCamelCase_ ( *UpperCAmelCase_ ) ->Optional[int]:
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , ... | 522 | 1 |
import math
import os
import sys
def UpperCAmelCase__( __UpperCAmelCase : str ):
__snake_case : Union[str, Any] = ''
try:
with open(__UpperCAmelCase , 'rb' ) as binary_file:
__snake_case : Optional[Any] = binary_file.read()
for dat i... | 679 | import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_Upp... | 679 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_lowerCAmelCase :Union[str, Any] = logging.get_logger(__name__)
class _UpperCAmelCase ( a ):
'''simple docstring'''
def __init__( self , ... | 506 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 506 | 1 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 671 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase ... | 671 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :list[int] ): # This function is recursive
__UpperCAmelCase = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition of
# recu... | 49 | UpperCamelCase = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
UpperCamelCase ... | 520 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase ( lowercase_ , unittest.TestC... | 687 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : List[str] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeB... | 687 | 1 |
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.training.common_utils i... | 37 |
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.pa... | 37 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaPr... | 143 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 143 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/umt5-small": "https://huggingface.co/go... | 11 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A ( UpperCamelCase_ : List[Any] ) -> Tuple:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 48 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ : list ):
def merge(lowercase_ : list , lowercase_ : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 705 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers impor... | 653 | 0 |
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
a__ : List[str] = """scheduler_config.json"""
class lowercase ( ... | 165 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : Any = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Conf... | 165 | 1 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import ... | 716 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ( A ):
... | 450 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
A_ = re.compile(R"\b(a|an|the)\b", re.UNICODE)
A_ = None
def UpperCamelCase__ ( ) -> Optional[Any]:
snake_case__ : List[... | 270 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_available():
... | 132 | 0 |
def __lowerCamelCase ( __lowerCAmelCase : int = 100 ) -> int:
__UpperCamelCase : Optional[Any] = n * (n + 1) * (2 * n + 1) / 6
__UpperCamelCase : Any = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == ... | 515 |
def __lowerCamelCase ( __lowerCAmelCase : list ) -> list:
__UpperCamelCase : Dict = len(__lowerCAmelCase )
for i in range(1 , __lowerCAmelCase ):
__UpperCamelCase : Dict = collection[i]
__UpperCamelCase : Option... | 515 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects i... | 31 |
'''simple docstring'''
import re
def __lowerCamelCase ( __lowerCAmelCase : str ) -> list:
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def __lowerCamelCase ( __lowerCAmelCase : str ) -> ... | 369 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa... | 447 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = """https://o... | 447 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Dict = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
... | 394 |
import qiskit
def lowerCamelCase_ ( UpperCamelCase_ , UpperCamelCase_ ):
_a : Tuple = qiskit.Aer.get_backend('''aer_simulator''' )
_a : Any = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
... | 471 | 0 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPV... | 572 |
'''simple docstring'''
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
f... | 572 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 387 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docst... | 387 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case : Dict = logging.get_logger(__name__)
class a (UpperCAmelCase_ , UpperCAmelCase_ ):
... | 707 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
fro... | 203 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
def get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
_snake_case = []
_snake_case = min(len(_stra ) , len(_stra ) ) // 2
for i, ... | 103 |
"""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 UpperCAmelCase ( A : ... | 573 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( a : int , a : int ):
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
a__ = str(bin(a ) )
binary_number += "0" * shift_amount
... | 126 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 126 | 1 |
"""simple docstring"""
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 = {
"YituTech/conv-bert-base": "https://huggingface... | 77 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 419 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase: Callable , lowercase: float , lowercase: float , lowercase: float , lowercase: float ) -> np.array:
'''simple docstring'''
_UpperCamelCase: Tuple... | 264 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 264 | 1 |
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 _lowerCAmelCase ( ... | 282 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.d... | 282 | 1 |
'''simple docstring'''
from string import ascii_uppercase
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
SCREAMING_SNAKE_CASE__ : List[str] = dict(enumerate(ascii_uppercase))
def a ( UpperCamelCase_ : str , UpperCam... | 581 |
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_... | 581 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
snake_case__ = datasets.logging.get_logger(__name__)
snake_case__ = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault ... | 583 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase_ ( UpperCAme... | 583 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowerCAmelCase ( lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[int] = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
import random
def __UpperCAmelCase ( __a : int ) -> bool:
"""simple docstring"""
_a : str = num - 1
_a : Dict = 0
while s % 2 == 0:
_a : Dict = s // 2
t += 1
for _ in rang... | 14 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = "▁"
_A = {"vocab_file": "spiece.model"}
_A = {
"vocab_file": {"google/pegasus-xsum": "ht... | 279 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelin... | 279 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
SCREAMING_SNAKE_CASE_ = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubse... | 523 | '''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torc... | 523 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
UpperCAmelCase__ =3
def lowerCAmelCase_ ( UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
print("""Generati... | 706 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase__ =logging.get_logger(__name__)
UpperCAmelCase__ ... | 442 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_im... | 23 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.tes... | 412 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _... | 170 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class _lowerCAmel... | 170 | 1 |
'''simple docstring'''
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
f... | 11 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase__ :Union[str, Any] = HfApi()
lowercase__ :Optional[Any] = {}
# fmt: off
lowercase__ :Optional[int] = torch.... | 522 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:... | 308 |
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... | 308 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCamelCase = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parse... | 71 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase = get_tests_dir('fixtures/spiece.model')
... | 464 | 0 |
import unittest
from knapsack import knapsack as k
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = 0
_UpperCamelCase = [0]
_UpperCamelCase = [0]
... | 719 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
# See all GPTNeoX models at https://huggingface.c... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ : Union[str, Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseC... | 12 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (... | 633 | 0 |
"""simple docstring"""
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 = logging.get_logger(__name__)
UpperCAmelCase = ... | 700 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 475 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : List[str] = {
'''andreasmadsen/efficient_mlm_m0.40''': (
... | 343 |
__A : Optional[Any] = '''Input must be a string of 8 numbers plus letter'''
__A : str = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> bool:
'''simple docstring'''
if not isinstance(_UpperCAmelCase, _UpperCAmelCase ):
... | 343 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
fro... | 86 |
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
UpperCamelCase__ = len(__a )
UpperCamelCase__ = len(__a )
UpperCamelCase__ = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
UpperCamelCase__ = True
for i in... | 86 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : List[str] = (DDIMParallelScheduler,)
UpperCamelCase_ : Optional[An... | 332 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_a... | 332 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.... | 713 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 2 | 0 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
A_ = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __UpperCamelCase ( a) ->int:
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS... | 360 |
def __UpperCamelCase ( a = 100) ->int:
lowerCamelCase__ = (n * (n + 1) // 2) ** 2
lowerCamelCase__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 360 | 1 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : str = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8_6_6_0_2_5_4])
_lowerCAmelCase ... | 289 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase : Any = HfApi()
_lowerCAmelCase : int = {}
# fmt: off
_lowerCAmelCase : List[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_... | 289 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 impor... | 703 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedCon... | 305 | 0 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
f... | 543 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelM... | 543 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __UpperCamelCase (_UpperCAmelCase ):
__A = ... | 720 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) ... | 653 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( A__ ) -> List[Any]:
'''simple docstring'''
for param in module.parameters():
SCREAMING_SNAKE_CASE__ : Optional[int] = False
def a ( ) -> Dict:
... | 35 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase ( unittest.TestCase ):
lowerCamelCase : List[Any] = inspect... | 35 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ :List[str] = logging.get_logger(__name__)
UpperCAmelCase__ ... | 483 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_... | 483 | 1 |
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