code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_A : Tuple = """Usage of script: script_name <size_of_canvas:int>"""
_A : Dict = [0] * 1_00 + [1] * 10
random.shuffle(choice)
def __snake_case ( ... | 100 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
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_channel_dimension... | 101 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
i... | 102 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
"""simple docstring"""
snake_case = 6_5_5_2_1
def snake_case ( lowerCAmelCase_ ) -> int:
_snake_case = 1
_snake_case = 0
for plain_chr in plain_text:
_snake_case = (a + ord(lowerCAmelCase_ )) % MOD_ADLER
_snake_case = ... | 103 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 104 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : Any = ["image_processor", "tokenizer"]
__a : Tuple = "ChineseCLIPImageProces... | 105 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Union[str, Any] =logging.get_logger(__name__)
__snake_case :List[Any] ={
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class ... | 106 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class lowercase_ :
"""simple docstring"""
def __init__( self : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[Any], UpperCamelCase__ : Optional[int], UpperCamelCa... | 107 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase =logging.get_logge... | 617 | 0 |
import sys
__a: str = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6689664895044524452316... | 108 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
'''simple docstring'''
import math
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
return math.sqrt(__UpperCAmelCase ) * math.sqrt(__UpperCAmelCase ) == num
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple d... | 109 |
"""simple docstring"""
UpperCAmelCase =[
[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 _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
def __A ( a_ : str ,a_ : str = " " ):
lowerCAmelCase : Any = []
lowerCAmelCase : List[str] = 0
for index, char in enumerate(_a ):
if char == separator:
split_words.append(string[last_index:index] )
... | 525 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_snake_case = logging.get_logger(__name__)
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_C... | 340 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
'''simple docstring'''
_lowerCAmelCase = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"h... | 432 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
SCREAMING_SNAKE_CASE : Tuple = {
'''sample_size''': 32,
'''in_channels''': 3,... | 260 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(_a , _a ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(_a ) == 0:
raise ValueError("Input list must be a non empty list" ... | 623 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowerCamelCase__ = "Create a default config file for Accelerate with only a few flags set."
def ... | 612 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
__A ='''\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe m... | 463 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.... | 449 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(_a ):
for j in range(_a ):
if dist[i][j] != float("inf" ):
... | 682 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
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_table.py
lowerCAmelCase_ = ''... | 531 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __A ( a_ : NDArray[floataa] ,a_ : NDArray[floataa] ,a_ : list[int] ,a_ : int ,):
lowerCAmelCase , lowerCAmelCase : str ... | 525 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/m... | 340 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> Any:
'''simple docstring'''
__magic_name__ = ''''''
for i in table:
res += inp[i - 1]
return res
def UpperCamelCase ( a ) -> List[str]:
'''simple docstring'''
... | 432 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = [
[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 ( lowerCAmelCase__... | 260 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : List[str] = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_to... | 623 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
from __future__ import annotations
import numpy as np
def __A(lowerCAmelCase ) -> str:
"""simple docstring"""
return np.maximum(0 , _a )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 612 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__A =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow(''''''... | 463 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_t... | 65 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.js... | 449 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase =logging.get_logge... | 617 | 0 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config i... | 682 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
imp... | 531 |
"""simple docstring"""
UpperCAmelCase =[
[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 _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.swi... | 525 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Con... | 340 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FI... | 432 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ = 100 ,):
A__ = x_start
... | 260 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
_A = 42
_A = None
_A = None
def __lowercase( UpperCAmelCase__ ):
"""simple doc... | 623 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
from __future__ import annotations
def __A(lowerCAmelCase , lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = []
create_all_state(1 , _a , _a , [] , _a )
return result
def __A(lowerCAmelCase , lowerCAmelCase , lowerCAmelCas... | 612 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCamelCase_ = mf_knapsack(i - 1 , _a ... | 463 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __lowercase ( __lowerCamelCase ):
snake_case_ = CustomTokenizer
pass
| 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ... | 449 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(_a ) * abs(_a )
if __name__ == "__main__":
import doctest
d... | 682 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {
'''configura... | 531 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
'''simple docstring'''
import requests
lowerCAmelCase = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __A ( a_ : str ):
lowerCAmelCase : Dict = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the l... | 525 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 340 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
_lowerCAmelCase = datasets.logging.get_logger(__name__)
_lowerCAmelCase = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, A... | 432 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
if moles < 0 or kelvin < 0 or volume < 0:
... | 260 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : Optional[Any] = get_tests_dir('fixtures/test_sentencepiece_with... | 623 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=__lowercase ):
UpperCamelCase_ : Any = ["sentencepiece"]
def __init__( self , *a , **a ) -> Tuple:
'''simple docstring'''
requires_bac... | 612 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''vocab.txt'''... | 463 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 65 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str = "" ):
"""simple docstring"""
snake_case : List[str] = url or "https://w... | 449 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase =logging.get_logge... | 617 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...tes... | 682 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 531 |
"""simple docstring"""
UpperCAmelCase =[
[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 _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase = get_tests_dir("""... | 525 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See... | 340 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_lowerCAmelCase = 3
def UpperCamelCase ( a ) -> Union[str, Any]:
'''simple docstring'''
print('''Generating primitive root of p''' ... | 432 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
"""simple docstring"""
import heapq
import sys
import numpy as np
SCREAMING_SNAKE_CASE : Dict = tuple[int, int]
class snake_case_ :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
A__ = ... | 260 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = AutoConfig.from_pretrained(_a ... | 623 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from t... | 612 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = field(default='summarization' , metadata={'include_... | 463 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not nums:
return 0
UpperCAmelCase__ : str = nums[0]
UpperCAmelCase__ : List[Any] = 0
for num in nums[1:]:
... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMix... | 449 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a__ : Dict = '''\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and... | 682 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
lowerCAmelCase_ = namedtuple(
'''_TestComma... | 531 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def __A ( a_ : float ,a_ : float ,a_ : float ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_... | 525 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
import math
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Tuple = []
lowerCamelCase : Dict = 2
lowerCamelCase : List[str] = int(math.sqrt(_a ) ) # Size of every segment
... | 340 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 432 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 260 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
a_ : List[str] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
a_ : str = [{'... | 623 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
def __A() -> Tuple:
"""simple docstring"""
_UpperCamelCase = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(_a )[-1_0:]
if __name__ == "__main__":
print(solution())
| 612 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A ={
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processin... | 463 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
"""simple docstring"""
from math import sqrt
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = 0
for i in range(1 , int(sqrt(_a ) + 1 ) ):
if n % i == 0 and i != sqrt(_a ):
total += i + n ... | 65 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
"""simple docstring"""
import re
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str ):
"""simple docstring"""
snake_case : Optional[Any] = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(_a , _a ):
... | 449 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase =logging.get_logge... | 617 | 0 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = grid.sha... | 682 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 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 VQD... | 531 |
"""simple docstring"""
UpperCAmelCase =[
[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 _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgum... | 525 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
from manim import *
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = Rectangle(height=0.5 , width=0.5 )
low... | 340 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCH... | 432 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {... | 260 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
from math import pow, sqrt
def __lowercase( *UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
... | 623 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_up... | 612 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_a ):
print(F'{i}\t\t{d}' )
def lowerCamelCase_ ( lowerCa... | 463 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import ... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: float , lowerCamelCase_: float , lowerCamelCase_: float , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_con... | 449 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def UpperCAmelCase__... | 682 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
... | 531 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 525 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return 10 - x * x
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if equation(_a ) * equation(_a ) >= 0:
raise ValueError("Wrong ... | 340 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from a... | 432 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
def __lowerCamelCase ( lowerCAmelCase__ ):
if not isinstance(_a ,_a ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
A__ = 0
while number:
# This way we... | 260 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from t... | 623 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
import qiskit
def __A(lowerCAmelCase , lowerCAmelCase ) -> Dict:
"""simple docstring"""
_UpperCamelCase = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
_UpperCamelCase = qiskit.QuantumCircuit(_a , _a )
... | 612 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''sentencepiece.bpe.model'''}
... | 463 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 65 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str , lowerCamelCase_: str ):
"""simple docstring"""
snake_case : Union[str, Any] = len(_a ) + 1
snake_case : Dict = len(_a ) + 1
# dp is a... | 449 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase =logging.get_logge... | 617 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 682 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING... | 531 |
"""simple docstring"""
UpperCAmelCase =[
[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 _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCAmelCase = """\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Syst... | 525 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test... | 340 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
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