code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _lowercase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) ... | 175 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
... | 27 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase : Dict = False
try:
... | 239 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Union[str, Any] = {
'configuration_blenderbot': [
... | 27 | 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__":
SCREAMING_SNAKE_CASE__ = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(in... | 150 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils i... | 27 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 287 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] ):
__a : Any = te... | 27 | 0 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'google/umt5-small': 'https://huggingface.co/google/u... | 320 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 27 | 0 |
import warnings
from functools import wraps
from typing import Callable
def a( A : Callable ) -> Any:
"""simple docstring"""
@wraps(_SCREAMING_SNAKE_CASE )
def _inner_fn(*A : List[str] , **A : List[str] ):
warnings.... | 227 |
'''simple docstring'''
import requests
__lowercase : Tuple = '' # <-- Put your OpenWeatherMap appid here!
__lowercase : Tuple = 'https://api.openweathermap.org/data/2.5/'
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "Chicago" , _SCREAMING_SNAKE_CASE ... | 27 | 0 |
import argparse
import os
import evaluate
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 accelerate import Accele... | 212 |
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCamelCase ( torch.nn.Module ):
def __init__( self , __a="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(__a , self ).__init__()
__a ... | 27 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __magic_name__ ( lowerCAmelCase_ ... | 188 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase (_SCRE... | 27 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 285 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
im... | 27 | 0 |
"""simple docstring"""
import math
def __magic_name__ ( ) -> int:
lowercase : Union[str, Any] = input("Enter message: " )
lowercase : List[str] = int(input(f"""Enter key [2-{len(_SCREAMING_SNAKE_CASE ) - 1}]: """ ) )
lowercase... | 202 |
'''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... | 27 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ : Dict = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available... | 121 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transfo... | 27 | 0 |
import re
import subprocess
import sys
a_ = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
a_ = subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
a_ = '|'.join(sys.argv[1:])
a_ = r... | 175 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils imp... | 27 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[int]=None ) -> List[Any]:
lowercase_ : Union[str, Any] = ... | 239 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
'google/umt5-small': 'htt... | 27 | 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
SCREAMING_SNAKE_CASE__ = Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE__ = Mapping[str... | 150 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
__a : List[Any] = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , 'html.parser' ... | 27 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A__ :
_UpperCAmelCase : Tuple = field(
default="""codeparrot/codeparrot""" , metadata={"""help""": """Model name or path of model to be trained."""})
_UpperCAmelCase : Lis... | 287 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_visio... | 27 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.util... | 320 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVeca... | 27 | 0 |
_lowercase: Optional[int] = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...... | 227 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnode... | 27 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Optional[Any]:
lowerCAmelCase__ : Tuple ... | 212 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase : str = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'config... | 27 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ ( _A : str , _A : str ):
'''simple docstring'''
a__ =list(_SCREAMING_SNAKE_CASE )
a__ =list(_SCREAMING_SNAKE_CASE )
a__ =0
for ... | 188 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import loa... | 27 | 0 |
from __future__ import annotations
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
create_state_space_tree(_SCREAMING_SNAKE_CASE , [] , 0 , [0 for i in range(len(_SCREAMING_SNAKE_CASE ) )] )
def __lowerCamelCa... | 285 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
__lowercase : Tuple = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowercase : List[str] =... | 27 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase : int = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCamelCase : Dict ... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for b... | 28 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configu... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> list:
"""simple docstring"""
UpperCamelCase = len(A__ )
for i in range(1 , A__ ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
... | 28 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_... | 28 |
'''simple docstring'''
import math
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 SchedulerMix... | 28 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
return np.array_equal(A__ , matrix.conjugate().T )
def __lowerCamelCase ( A__ , A_... | 28 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_commo... | 28 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mode... | 28 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfi... | 28 | 1 |
'''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 A ( self : ... | 28 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as ... | 28 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import Pretr... | 28 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH ... | 28 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase : Union[str, Any] = "\\n\n"
_lowerCamelCase : List[str] ... | 28 |
'''simple docstring'''
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
@datacl... | 28 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokeniz... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is... | 28 | 1 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 10**9 ) -> int:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
while perimeter <= max_perimeter:
... | 28 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module,... | 28 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Optional[Any]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Any = {
"nielsr/canine-s": 2048,
... | 28 |
'''simple docstring'''
_lowerCamelCase : int = "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,
)
... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
return "".join([hex(A__ )[2:].zfill(2 ).upper() for byte in list(A__ )] )
def __lowerCamelCase ( A__ ) -> bytes:
"""simple do... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "... | 28 | 1 |
'''simple docstring'''
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
@datacl... | 28 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 28 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : int = {
"configuratio... | 28 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_lowerCamelCase : str = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transf... | 28 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils imp... | 28 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : An... | 28 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : List[str] = 5_0000
_lowerCamelCase : Optional[int] = 5000
_lowerCamelCase ,_lowerCamelCase : int = os.pa... | 28 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trainin... | 28 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
UpperCamelCase = abs(A__ )
UpperCamelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def __lowerCamelCase ( A__ ... | 28 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> Image:
"""simple docstring"""
def brightness(A__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueEr... | 28 | 1 |
'''simple docstring'''
import argparse
import json
import subprocess
def __lowerCamelCase ( A__ , A__ ) -> Tuple:
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = (
F"""curl -H \"Accept: application/vnd.github... | 28 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,... | 28 | 1 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowerCamelCase : List[str] = TypeVar("T")
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
_SCRE... | 28 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tenso... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> str:
"""simple docstring"""
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplica... | 28 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase : Union[str, Any] = "\\n\n"
_lowerCamelCase : List[str] ... | 28 | 1 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for b... | 28 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( A__ , A__ , A__ ... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> list:
"""simple docstring"""
UpperCamelCase = len(A__ )
for i in range(1 , A__ ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
... | 28 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ ) -> list[int]:
"""simple docstring"""
return [ord(A__ ) - 96 for elem in plain]
def __lowerCamelCase ( A__ ) -> str:
... | 28 |
'''simple docstring'''
import math
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 SchedulerMix... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> float:
"""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__":
... | 28 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_commo... | 28 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embedd... | 28 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfi... | 28 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from .... | 28 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as ... | 28 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tenso... | 28 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH ... | 28 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available... | 28 |
'''simple docstring'''
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
@datacl... | 28 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
def A ( self : Optional[int] , UpperCamelCase__ : Dict=None , UpperCamelCase__ ... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is... | 28 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_lowerCamelCase : Union[str, Any] = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/con... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 10**9 ) -> int:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
while perimeter <= max_perimeter:
... | 28 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/... | 28 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Optional[Any]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise ... | 28 |
'''simple docstring'''
_lowerCamelCase : int = "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,
)
... | 28 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : ... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "... | 28 | 1 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPText... | 28 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 28 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_lowerCamelCase : Tuple = {"vocab_file": "vocab.txt", "to... | 28 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_lowerCamelCase : str = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( A__ , A__ = True , A__ = math.inf , A__ = -math.inf , A__ = math.inf , A__ = -math.inf , A__ =... | 28 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils imp... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
if not isinstance(A__ , A__ ):
UpperCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(A__ )
if number < 0:
... | 28 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : List[str] = 5_0000
_lowerCamelCase : Optional[int] = 5000
_lowerCamelCase ,_lowerCamelCase : int = os.pa... | 28 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,... | 28 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six... | 28 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
... | 28 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> Image:
"""simple docstring"""
def brightness(A__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueEr... | 28 | 1 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, i... | 28 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,... | 28 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCamelCase ( A__ ) ... | 28 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tenso... | 28 | 1 |
'''simple docstring'''
_lowerCamelCase : int = "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,
)
... | 28 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase : Union[str, Any] = "\\n\n"
_lowerCamelCase : List[str] ... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(A__ ) )[2:] # remove the leading "0b"... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for b... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for b... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> list:
"""simple docstring"""
UpperCamelCase = len(A__ )
for i in range(1 , A__ ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
... | 28 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __lowerCamelCase ( A__ , A__=None ) -> Any:
"""simple docstring"""
... | 28 |
'''simple docstring'''
import math
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 SchedulerMix... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( ) -> Dict:
"""simple docstring"""
UpperCamelCase = 0
for i in range(1 , 1_001 ):
total += i**i
return str(A__ )[-10:]
if __name__ == "__main__":
print(solution())
| 28 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_commo... | 28 | 1 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH ... | 28 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfi... | 28 | 1 |
'''simple docstring'''
import numpy as np
def __lowerCamelCase ( A__ ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( A__ ) -> np.array:
"""simple docstring... | 28 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as ... | 28 | 1 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
if not isinstance(A__ , A__ ):
raise TypeError('Undefined for non-integers' ... | 28 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH ... | 28 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base... | 28 |
'''simple docstring'''
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
@datacl... | 28 | 1 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipe... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is... | 28 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
if not nums:
return 0
UpperCamelCase = nums[0]
UpperCamelCase = 0
for num in nums[1:]:
UpperCamelCas... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 10**9 ) -> int:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
while perimeter <= max_perimeter:
... | 28 | 1 |
'''simple docstring'''
from timeit import timeit
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('the value of input must not be negative' )
UpperCamelCase = 0
while number:
number... | 28 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Optional[Any]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
from torch import nn
def __lowerCamelCase ( A__ ) -> Any:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn... | 28 |
'''simple docstring'''
_lowerCamelCase : int = "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,
)
... | 28 | 1 |
'''simple docstring'''
from timeit import timeit
_lowerCamelCase : List[str] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "... | 28 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVec... | 28 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 28 | 1 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> Image:
"""simple docstring"""
def brightness(A__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueEr... | 28 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_lowerCamelCase : str = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __lowerCamelCase ( A__ ... | 28 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils imp... | 28 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except ... | 28 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : List[str] = 5_0000
_lowerCamelCase : Optional[int] = 5000
_lowerCamelCase ,_lowerCamelCase : int = os.pa... | 28 | 1 |
'''simple docstring'''
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class SCREAMING_SN... | 28 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six... | 28 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feat... | 28 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> Image:
"""simple docstring"""
def brightness(A__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueEr... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 1_000_000 ) -> int:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 1
UpperCamelCase = {1: 1}
for inputa in range(2 , A__ ):
UpperCamelCase = 0
Upp... | 28 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,... | 28 | 1 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __lowerCamelCase ( A__ , A__=7 ) -> Tuple:
"""simple docstring"""
UpperCamelCase = None
i... | 28 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tenso... | 28 | 1 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCS... | 28 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase : Union[str, Any] = "\\n\n"
_lowerCamelCase : List[str] ... | 28 | 1 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for b... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 2
while i * i <= n:
UpperCamelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
n... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> list:
"""simple docstring"""
UpperCamelCase = len(A__ )
for i in range(1 , A__ ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
... | 28 | 1 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowerCamelCase ( ) -> str:
"""simple docstring"""
UpperCamelCase = HfArgumentParser(A__ )
UpperCamelCase = parser.pars... | 28 |
'''simple docstring'''
import math
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 SchedulerMix... | 28 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( _a ,... | 28 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_commo... | 28 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"ut/deta": "http... | 28 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfi... | 28 | 1 |
'''simple docstring'''
import math
import qiskit
def __lowerCamelCase ( A__ = 1 , A__ = 1 , A__ = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(A__ , A__ )
or isinstance(A__ , ... | 28 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as ... | 28 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : List[str] = 5_0000
_lowerCamelCase : Optional[int] = 5000
_lowerCamelCase ,_lowerCamelCase : int = os.pa... | 28 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH ... | 28 | 1 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
def __lowerCamelCase (... | 28 |
'''simple docstring'''
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
@datacl... | 28 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is... | 28 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeli... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 10**9 ) -> int:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
while perimeter <= max_perimeter:
... | 28 | 1 |
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_downl... | 28 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Optional[Any]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
... | 28 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> list[list[float]]:
"""simple docstring"""
UpperCamelCase = []
for data in source_data:
for i, el in enumerate(A__ ):
if len(A__ ) < i + 1:
data_lists.append([] )
... | 28 |
'''simple docstring'''
_lowerCamelCase : int = "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,
)
... | 28 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTok... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "... | 28 | 1 |
'''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/licens... | 28 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 28 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.