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 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 275 | 0 |
from collections.abc import Sequence
def _UpperCAmelCase ( snake_case , snake_case = False ):
"""simple docstring"""
if not arr:
return 0
_lowerCAmelCase = 0 if allow_empty_subarrays else float("""-inf""" )
_lowerCAmelCase = 0.0
for num ... | 82 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 275 | 0 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCamelCase : Tuple = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCamelCase : Optional[A... | 83 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCAmelCase = (7_20, 12_80) # Height, Width
__UpperCAmelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.... | 84 |
def _lowercase ( lowercase__ ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCAmelCase : int = sorted(string.lower() )
return len(lowercase__ ) == len(set(lowerca... | 275 | 0 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : List[str] = TypeVar("T")
class _snake_case ( G... | 85 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCamelCase__ = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def __lowerCAmelCase (_... | 86 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercas... | 275 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
... | 87 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils im... | 275 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorT... | 88 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase (unittest.TestCase ):
@property
def Upper... | 275 | 0 |
'''simple docstring'''
from __future__ import annotations
__lowerCAmelCase = 8.988e9 # units = N * m^s * C^-2
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> dict[str, float]:
_a : Tuple = ... | 89 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_UpperCamelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller tha... | 275 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> int:
"""simple docstring"""
stooge(UpperCamelCase__ , 0 , len(UpperCamelCase__ ) - 1 )
return arr
def lowerCamelCase_ ( UpperCamelCase__ : Dict , ... | 90 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
i... | 275 | 0 |
"""simple docstring"""
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : List[str] , lowercase_ : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = arr.split(''',''')
... | 91 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {... | 275 | 0 |
UpperCamelCase__ = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
UpperCamelCase__ = froz... | 92 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Tuple = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeT... | 93 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 275 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 94 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/reso... | 275 | 0 |
def _A ( SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
def merge(SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0... | 95 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : Any... | 275 | 0 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowercase__ = namedtuple("""covid_data""", """cases deaths recovered""")
def _snake_case ( lowercase__ = "https://www.worldometers.info/co... | 96 |
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 (
IMAGENET_STAND... | 275 | 0 |
'''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-2.0
#... | 97 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowercase (unittest.TestCase ):
def UpperCamelCase__ ( self ) ->None:
'''simple docstring'''
__lowerCAmelCase : ... | 275 | 0 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won... | 98 |
def _lowercase ( lowercase__ , lowercase__ ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":... | 275 | 0 |
from collections import defaultdict
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__ : Optional[int] = total # total no of tasks (N)
# DP table will have a d... | 99 |
def _lowercase ( lowercase__ , lowercase__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowercase ( lowercase__ , lowercase__=0 ):
return sorted(lowercase__ , key=lambda lowercase__ : x[column] )
def _lowercase ( lower... | 275 | 0 |
"""simple docstring"""
__magic_name__ = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip... | 100 |
def _lowercase ( lowercase__ = 2_0_0 ):
__lowerCAmelCase : Union[str, Any] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
__lowerCAmelCase : Dict = [0] * (pence + 1)
__lowerCAmelCase : Optional[int] = 1 # base case: 1 way to make 0 pence
... | 275 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_a... | 101 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import ena... | 275 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : list ) ->int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += ... | 102 |
from collections import deque
from .hash_table import HashTable
class __lowercase (_UpperCAmelCase ):
def __init__( self , *A_ , **A_ ) ->int:
'''simple docstring'''
super().__init__(*A_ , **A_ )
def UpperCamelCase__ ( s... | 275 | 0 |
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 import logging
... | 103 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 275 | 0 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowercase_ :
"""simple docstring"""
@property
... | 104 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 275 | 0 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenize... | 105 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 0 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Any ,lowercase_ : Any ):
lowerCAmelCase__ : List[str] = data
lowerCAmelCase__ : Any = None
class S... | 106 |
def _lowercase ( lowercase__ ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCAmelCase : int = sorted(string.lower() )
return len(lowercase__ ) == len(set(lowerca... | 275 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils imp... | 107 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str ): # noqa: E741
'''simple docstring'''
while r ... | 108 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercas... | 275 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Tensor... | 109 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils im... | 275 | 0 |
import os
def __UpperCamelCase ( ) ->Any:
"""simple docstring"""
lowerCamelCase_ =os.path.dirname(os.path.realpath(lowercase__ ) )
lowerCamelCase_ =os.path.join(lowercase__ , """triangle.txt""" )
with open(lowercase__ ) as f:
... | 154 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase (unittest.TestCase ):
@property
def Upper... | 275 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = 0... | 14 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_UpperCamelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller tha... | 275 | 0 |
'''simple docstring'''
def a__ ( lowercase : Any = 600851475143 ) -> str:
"""simple docstring"""
try:
_UpperCamelCase = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to ... | 324 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
i... | 275 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/res... | 309 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {... | 275 | 0 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase__ : List[str] = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def __lowercase ( ) -> int:
SCREAMING_SNAKE_CASE : List[str] = ... | 245 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_snake_case : str = TypeVar("T")
def lowerCAmelCase_ ( __lowerCamelCase ):
return (position - 1) // 2
def lowerCAmelCase_ ( __lowerCamelCase ):
return (2 *... | 123 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 275 | 0 |
"""simple docstring"""
def lowercase ( a__ : Union[str, Any] , a__ : Union[str, Any] ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCamelCase = str(bin(lowercase__ ) )[2:] # remove the leading "0... | 256 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/reso... | 275 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : List[str] = logging.get_logger(__name__)
lowerCAmelCase_ : List[Any] = {
'asapp/sew-d-tiny-100k': 'https://hugging... | 63 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : Any... | 275 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 3
class SCREAMING_SNAKE_CASE_ ( _UpperCAmelC... | 343 |
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 (
IMAGENET_STAND... | 275 | 0 |
'''simple docstring'''
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 re... | 234 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowercase (unittest.TestCase ):
def UpperCamelCase__ ( self ) ->None:
'''simple docstring'''
__lowerCAmelCase : ... | 275 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"microsoft/git-base": "https://huggingface.co/microsoft/g... | 46 |
def _lowercase ( lowercase__ , lowercase__ ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":... | 275 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Op... | 154 |
def _lowercase ( lowercase__ , lowercase__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowercase ( lowercase__ , lowercase__=0 ):
return sorted(lowercase__ , key=lambda lowercase__ : x[column] )
def _lowercase ( lower... | 275 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( _UpperCAmelCase ):
'''simple docstring'''
UpperCAmelCase__ = (KDPMaDiscreteScheduler,)
UpperCAme... | 14 |
def _lowercase ( lowercase__ = 2_0_0 ):
__lowerCAmelCase : Union[str, Any] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
__lowerCAmelCase : Dict = [0] * (pence + 1)
__lowerCAmelCase : Optional[int] = 1 # base case: 1 way to make 0 pence
... | 275 | 0 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
lowercase__ : str = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input(... | 324 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import ena... | 275 | 0 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase_ = Mapping[str, np.ndarray]
UpperCamelCase_ = Mapping[str, Any] # Is a nested dict.
Uppe... | 309 |
from collections import deque
from .hash_table import HashTable
class __lowercase (_UpperCAmelCase ):
def __init__( self , *A_ , **A_ ) ->int:
'''simple docstring'''
super().__init__(*A_ , **A_ )
def UpperCamelCase__ ( s... | 275 | 0 |
import math
import os
import sys
def __lowercase ( _A ) -> Any:
SCREAMING_SNAKE_CASE : Any = ''''''
try:
with open(lowercase__ , """rb""" ) as binary_file:
SCREAMING_SNAKE_CASE : int = binary_fil... | 245 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 275 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class a :
"""simple docstring"""
@property
def __snake_case ( ... | 123 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 275 | 0 |
"""simple docstring"""
import os
import re
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 = logging.get_logger(__name__)
UpperCAmelCase ... | 256 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark im... | 63 |
def _lowercase ( lowercase__ ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCAmelCase : int = sorted(string.lower() )
return len(lowercase__ ) == len(set(lowerca... | 275 | 0 |
from collections import deque
from .hash_table import HashTable
class SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase ):
def __init__( self : Optional[Any] , *lowerCamelCase_ : str , **lowerCamelCase_ : Tuple ):
"""simple docstring"""
super().__init__(*A_ , ... | 343 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
'''simple docstring'''
from math import factorial
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] , lowerCamelCase__ : int , lowerCamelCase__ : Tuple ) ->Any:
'''simple docstring'''
_UpperCAme... | 234 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercas... | 275 | 0 |
"""simple docstring"""
import socket
def UpperCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCAmelCase = socket.gethostname()
lowerCAmelCase = ... | 46 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils im... | 275 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase):
_UpperCamelCase:List[str] ... | 154 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase (unittest.TestCase ):
@property
def Upper... | 275 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=0 ) -> Union[str, Any]:
... | 14 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_UpperCamelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller tha... | 275 | 0 |
'''simple docstring'''
def a__ ( lowercase : Union[str, Any], lowercase : Union[str, Any] ) -> int:
"""simple docstring"""
return number | (1 << position)
def a__ ( lowercase : Dict, lowercase : int ) -> int:
... | 324 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
i... | 275 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a_ :
__lowerCAmelCase : List[str] = 4_2
__lowe... | 309 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {... | 275 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase__ : Dict = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (... | 245 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 | 0 |
import os
import sys
import unittest
_snake_case : List[Any] = 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_... | 123 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 275 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase ( a__ : Optional[int] ) -> Optional[Any]:
_UpperCamelCase = analyze_text(lowercase__ )
_UpperCamelCase ... | 256 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/reso... | 275 | 0 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
... | 63 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : Any... | 275 | 0 |
import requests
from bsa import BeautifulSoup
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
UpperCamelCase = BeautifulSoup(requests.get(lowercase__ , params=lowercase__ ).content , """html.parser""" )
UpperCamelCase ... | 343 |
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 (
IMAGENET_STAND... | 275 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase__ = tuple[int, int, int]
lowerCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ----------------... | 234 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowercase (unittest.TestCase ):
def UpperCamelCase__ ( self ) ->None:
'''simple docstring'''
__lowerCAmelCase : ... | 275 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig... | 46 |
def _lowercase ( lowercase__ , lowercase__ ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":... | 275 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class _SCREAMING_SNAKE_C... | 154 |
def _lowercase ( lowercase__ , lowercase__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowercase ( lowercase__ , lowercase__=0 ):
return sorted(lowercase__ , key=lambda lowercase__ : x[column] )
def _lowercase ( lower... | 275 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
if not isinstance(lowercase__ , lowercase__ ):
A__ = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase__ )
if nu... | 14 |
def _lowercase ( lowercase__ = 2_0_0 ):
__lowerCAmelCase : Union[str, Any] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
__lowerCAmelCase : Dict = [0] * (pence + 1)
__lowerCAmelCase : Optional[int] = 1 # base case: 1 way to make 0 pence
... | 275 | 0 |
'''simple docstring'''
def a__ ( lowercase : List[Any] ) -> Any:
"""simple docstring"""
if not isinstance(lowercase__, lowercase__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input mus... | 324 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import ena... | 275 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfi... | 309 |
from collections import deque
from .hash_table import HashTable
class __lowercase (_UpperCAmelCase ):
def __init__( self , *A_ , **A_ ) ->int:
'''simple docstring'''
super().__init__(*A_ , **A_ )
def UpperCamelCase__ ( s... | 275 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
class a__ ( _UpperCAmelCase ):
"""simple docstring"""
def ... | 245 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 275 | 0 |
import logging
from transformers import PretrainedConfig
_snake_case : List[Any] = logging.getLogger(__name__)
_snake_case : List[str] = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",... | 123 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 275 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 256 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 0 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : ... | 63 |
def _lowercase ( lowercase__ ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCAmelCase : int = sorted(string.lower() )
return len(lowercase__ ) == len(set(lowerca... | 275 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 343 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
lowerCamelCase__ = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
# Mark te... | 234 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercas... | 275 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from... | 46 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils im... | 275 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase):
def _snake_case ( self )-> Optional[int]:
debug_launcher(test_script.mai... | 154 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase (unittest.TestCase ):
@property
def Upper... | 275 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowerCamelCase : Union[str, Any] = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.p... | 14 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_UpperCamelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller tha... | 275 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __lowerCAmelCase ( _UpperCAmelCa... | 324 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
i... | 275 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImg... | 309 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {... | 275 | 0 |
import os
import string
import sys
UpperCAmelCase__ : Union[str, Any] = 1 << 8
UpperCAmelCase__ : Optional[int] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
... | 245 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_snake_case : Any = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be... | 123 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 275 | 0 |
"""simple docstring"""
def lowercase ( a__ : int , a__ : Tuple ) -> Any:
_UpperCamelCase = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCamelCase = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
_Up... | 256 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/reso... | 275 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : Optional[int] = 10 , lowercase : Tuple = 22 ) -> Any:
_a = range(1 , lowercase__ )
_a = range(1 , lowercase__ )
return sum(
1 for power in powers for ... | 63 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : Any... | 275 | 0 |
def lowercase( UpperCamelCase_ ) -> Dict:
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
UpperCamelCase = sorted(string.lower() )
return len(lowercase__ ) == len(set(lower... | 343 |
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 (
IMAGENET_STAND... | 275 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCamelCase... | 234 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowercase (unittest.TestCase ):
def UpperCamelCase__ ( self ) ->None:
'''simple docstring'''
__lowerCAmelCase : ... | 275 | 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_... | 46 |
def _lowercase ( lowercase__ , lowercase__ ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":... | 275 | 0 |
def __UpperCamelCase ( _A : str ) ->int:
"""simple docstring"""
return " ".join(
"""""".join(word[::-1] ) if len(lowercase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_lon... | 154 |
def _lowercase ( lowercase__ , lowercase__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowercase ( lowercase__ , lowercase__=0 ):
return sorted(lowercase__ , key=lambda lowercase__ : x[column] )
def _lowercase ( lower... | 275 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_... | 14 |
def _lowercase ( lowercase__ = 2_0_0 ):
__lowerCAmelCase : Union[str, Any] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
__lowerCAmelCase : Dict = [0] * (pence + 1)
__lowerCAmelCase : Optional[int] = 1 # base case: 1 way to make 0 pence
... | 275 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __lowerCAmelCase ( _UpperCAmelCase ):
... | 324 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import ena... | 275 | 0 |
'''simple docstring'''
from statistics import mean, stdev
def _UpperCAmelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] = 3 ) -> Tuple:
_lowerCAmelCase : Optional[int] = min(lowercase__ )
_lowerCAmelCase : List[Any] = m... | 309 |
from collections import deque
from .hash_table import HashTable
class __lowercase (_UpperCAmelCase ):
def __init__( self , *A_ , **A_ ) ->int:
'''simple docstring'''
super().__init__(*A_ , **A_ )
def UpperCamelCase__ ( s... | 275 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,... | 245 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 275 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 123 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 275 | 0 |
"""simple docstring"""
def lowercase ( a__ : int ) -> int:
_UpperCamelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowercase ( a__ : Optional[int] ) -> Dict:
_UpperCamelCase = 0
while n... | 256 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Tuple = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.j... | 63 |
def _lowercase ( lowercase__ ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCAmelCase : int = sorted(string.lower() )
return len(lowercase__ ) == len(set(lowerca... | 275 | 0 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ) -> Tuple:
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ) and isinstance(lowercase__ , lowercase__ ):
UpperCamelCase = len(set_a.intersection(lowercase__ ) )
... | 343 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowerCAmelCase ():
_UpperCAmelCase : Any = HfArgumentParser(lowercase__ )
_UpperCAmelCase : Any = parser.parse_args_in... | 234 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercas... | 275 | 0 |
"""simple docstring"""
import os
import sys
import unittest
SCREAMING_SNAKE_CASE__ = 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... | 46 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils im... | 275 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 154 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase (unittest.TestCase ):
@property
def Upper... | 275 | 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 (
IMAGENET_ST... | 14 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_UpperCamelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller tha... | 275 | 0 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : Optional[Any] ) -> Optiona... | 324 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
i... | 275 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-trans... | 309 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {... | 275 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : List[str] = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
tr... | 245 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.