code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import comet # From: unbabel-comet
import torch
import datasets
_SCREAMING_SNAKE_CASE = datasets.logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
... | 88 |
_SCREAMING_SNAKE_CASE = {
"""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""",
"... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a = 10**9 ):
snake_case_ : str = 1
snake_case_ : int = 2
snake_case_ : Optional[int] = 0
snake_case_ : List[str] = 0
snake_case_ : List[Any] = 0
while perimeter <= max_perimeter... | 88 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_t... | 88 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = u
for i in range(1 , __a ):
snake_case_ : Optional[Any] = temp * (u - i)
return temp
def SCREAMING_SNAKE_CA... | 88 | 1 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( __a ):
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE__ ( __a ):
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_p... | 88 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_SCREAMING_SNAKE_CASE = """\
@misc{chen2021evaluating,
title={Evalua... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a = 60_08_51_47_51_43 ):
try:
snake_case_ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater t... | 88 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 88 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a ):
return 10 - x * x
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
# Bolzano theory in order to find if there is a root between a and b
if equation(__a ) * equation(__a ) >= 0:
raise ValueError('Wrong space!' )
snake_... | 88 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 88 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_... | 88 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Liter... | 88 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Dict = [int(__a ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(__a ) == 4 and all(0 <= int(__a ) <= 2_54 for octet in octets )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = ... | 88 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def SCREAMING_SNAKE_CAS... | 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
... | 88 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Tuple = len(__a )
# We need to create solution object to save path.
snake_case_ : Dict = [[0 for _ in range(__a )] for _ in range(__a )]
snake_case_ : ... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a = False ):
if not isinstance(__a , __a ):
snake_case_ : str = f"""Expected string as input, found {type(__a )}"""
raise ValueError(__a )
if not isinstance(__a , __a ):
snake_case_ : int = f... | 88 | 1 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 88 |
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[Any] = defaultdict(__a )
snake_case_ : Optional[Any] = []
... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_SCREAMING_SNAKE_CASE = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_SCRE... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = int(__a )
# Initialize Result
snake_case_ : Tuple = []
# Traverse through all denomination
for denomination in reversed(__a ):
# Find denominations
while int(__a ... | 88 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Optional[int] = 2
snake_case_ : List[str] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__a )
if n > 1:
factors.append(... | 88 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 88 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all P... | 88 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 | 1 |
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
_SCREAMING_SNAKE_CASE = get_tests_dir("""fixtures/test_sentencepiece_... | 88 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_ ( snake_case_ ... | 88 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from di... | 88 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
... | 88 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a = 1 , __a = 1 , __a = 1.0E4 , __a = False , __a = 1.0 , ):
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f"""E... | 88 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatur... | 88 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : str = ArgumentParser(
description=(
'PyTorch TPU d... | 88 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 88 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Any , _A : str=None , **_A : Union[str, Any] ) -> ... | 88 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def UpperCAmelCase_ ( self : int ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[int] = [10, 20, 3... | 88 |
_SCREAMING_SNAKE_CASE = {
"""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""",
"... | 88 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : O... | 88 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig... | 88 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = u
for i in range(1 , __a ):
snake_case_ : Optional[Any] = temp * (u - i)
return temp
def SCREAMING_SNAKE_CA... | 88 | 1 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from tr... | 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : int = [
'decoder.version',
'decoder.output_projection.weight',
... | 88 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a = 60_08_51_47_51_43 ):
try:
snake_case_ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater t... | 88 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_SCREAMING_SNAKE_CASE = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app tar... | 88 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 88 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_... | 88 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 88 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 | 1 |
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Union[str, Any] , _A : Optional[int] , _A : Union[str, Any]=None , _A : List[Any]=2048 ) ->... | 88 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.uti... | 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
... | 88 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a ):
if num <= 0:
snake_case_ : Dict = f"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(__a )
snake_case_ : Tuple = [True] * (num +... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a = False ):
if not isinstance(__a , __a ):
snake_case_ : str = f"""Expected string as input, found {type(__a )}"""
raise ValueError(__a )
if not isinstance(__a , __a ):
snake_case_ : int = f... | 88 | 1 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 88 |
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[Any] = defaultdict(__a )
snake_case_ : Optional[Any] = []
... | 88 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_SCREAMING_SNAKE_CASE = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_SCREAMING_SNAKE_CASE = [ord(letter) for letter in string.asci... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = int(__a )
# Initialize Result
snake_case_ : Tuple = []
# Traverse through all denomination
for denomination in reversed(__a ):
# Find denominations
while int(__a ... | 88 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, loggi... | 88 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 88 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_SCREAMING_SNAKE_CASE = object()
# For specifying empty leaf dict `{}`
_SCREAMING_SNAKE_CASE = object()
... | 88 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
... | 88 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_ ( snake_case_ ... | 88 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : list[list[int]] = []
snake_case_ : list[int] = []
snake_case_ : Dict = 0
snake_case_ : Any = sum(__a )
create_state_space_tr... | 88 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = int(__a )
# Initialize Result
snake_case_ : Tuple = []
# Traverse through all denomination
for denomination in reversed(__a ):
# Find denominations
while int(__a ... | 88 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatur... | 88 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 88 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 88 | 1 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
fro... | 88 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Any , _A : str=None , **_A : Union[str, Any] ) -> ... | 88 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 88 |
_SCREAMING_SNAKE_CASE = {
"""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""",
"... | 88 | 1 |
_SCREAMING_SNAKE_CASE = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_SCREAMING_SNAKE_CASE = [{"""type""": """code""", """content""": INSTALL_CONTENT... | 88 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = u
for i in range(1 , __a ):
snake_case_ : Optional[Any] = temp * (u - i)
return temp
def SCREAMING_SNAKE_CA... | 88 | 1 |
import random
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : str = a[left_index]
snake_case_ : str = left_index + 1
for j in range(left_index + 1 , __a ):
if a[j] < pivot:
snake_case_ ,snake_case_ : Any = ... | 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ :
__magic_name__: Dict = None
@experimental
def SCR... | 88 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 1 |
import math
import os
import sys
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[str] = ''
try:
with open(__a , 'rb' ) as binary_file:
snake_case_ : int = binary_file.read()
for dat in data:
snake_case_ : int = f... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a = 60_08_51_47_51_43 ):
try:
snake_case_ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater t... | 88 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_ ( snake_case_ ... | 88 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
... | 88 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 88 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_SCREAMING_SNAKE_CASE = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_... | 88 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE__ ( __a , __a = "cpu" , __a = None ):
snake_case_ : str = torch.load(__a , map_location=__a )
for k, v in tqdm(state_dict.items() ):
if not isinstanc... | 88 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_SCREAMING_SNAKE_CASE = {
"""E""": 1_2.7_0,
"""T""": 9.0_6,
"""A""": 8.1_7,
"""O""": 7.5_1,
"""I""": 6.9_7,
"""N""": 6.7_5,
"""S""": 6.3_3,
"""H""": 6.0_9,
"""R""": 5.9... | 88 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_com... | 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLT... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a = False ):
if not isinstance(__a , __a ):
snake_case_ : str = f"""Expected string as input, found {type(__a )}"""
raise ValueError(__a )
if not isinstance(__a , __a ):
snake_case_ : int = f... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
... | 88 |
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[Any] = defaultdict(__a )
snake_case_ : Optional[Any] = []
... | 88 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.stru... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = int(__a )
# Initialize Result
snake_case_ : Tuple = []
# Traverse through all denomination
for denomination in reversed(__a ):
# Find denominations
while int(__a ... | 88 | 1 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
_SCREAMING_SNAKE_CASE = 6_3_7_8_1_3_7.0
_SCREAMING_SNAKE_CASE = 6_3_5_6_7_5_2.3_1_4_2_4_5
_SCREAMING_SNAKE_CASE = 6_37_81_37
def SCREAMING_SNAKE_CASE__ ( __a , _... | 88 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 88 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a = 10_00 ):
return sum(e for e in range(3 , __a ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 88 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_ ( snake_case_ ... | 88 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_SCREAMING_SNAKE_CASE ... | 88 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
... | 88 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 88 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatur... | 88 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 88 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SC... | 88 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Any , _A : str=None , **_A : Union[str, Any] ) -> ... | 88 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils ... | 88 |
_SCREAMING_SNAKE_CASE = {
"""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""",
"... | 88 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
# Initialise PyTorch model
snake_case_ : A... | 88 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = ... | 88 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = u
for i in range(1 , __a ):
snake_case_ : Optional[Any] = temp * (u - i)
return temp
def SCREAMING_SNAKE_CA... | 88 | 1 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : List[Any] , _A : int , _A : Tuple=None ... | 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE_ ( sn... | 88 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 1 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a = 60_08_51_47_51_43 ):
try:
snake_case_ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater t... | 88 | 1 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE__ ( __a ):
create_state_space_tree(__a , [] , 0 )
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
if index == len(__a ):
print(__a )
return
cre... | 88 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/m... | 88 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 88 | 1 |
import re
from filelock import FileLock
try:
import nltk
_SCREAMING_SNAKE_CASE = True
except (ImportError, ModuleNotFoundError):
_SCREAMING_SNAKE_CASE = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"""... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_... | 88 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Tuple = 1.... | 88 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 | 1 |
import requests
_SCREAMING_SNAKE_CASE = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def SCREAMING_SNAKE_CASE__ ( __a ):
# fetching a list of articles in json format
snake_case_ : List[Any] = requests.get(_NEWS_API + bbc_news_api_k... | 88 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 | 1 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( __a = None ):
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
snake_case_ : Tuple = nums[0]
for i in range(1 , len(__a ) ):
snake_case_ : ... | 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
... | 88 | 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 SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def UpperCAmelCase_ ( self : Tuple )... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a = False ):
if not isinstance(__a , __a ):
snake_case_ : str = f"""Expected string as input, found {type(__a )}"""
raise ValueError(__a )
if not isinstance(__a , __a ):
snake_case_ : int = f... | 88 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import i... | 88 |
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[Any] = defaultdict(__a )
snake_case_ : Optional[Any] = []
... | 88 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_SCREAMING_SNAKE_CASE = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfi... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = int(__a )
# Initialize Result
snake_case_ : Tuple = []
# Traverse through all denomination
for denomination in reversed(__a ):
# Find denominations
while int(__a ... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : str = 0
snake_case_ : List[Any] = len(__a )
for i in range(n - 1 ):
for j in range(i + 1 , __a ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def SC... | 88 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Optional[Any] = abs(__a )
snake_case_ : Dict = 0
while n > 0:
res += n % 10
n //= 10
return res
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[str] = ab... | 88 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_... | 88 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_ ( snake_case_ ... | 88 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Any , *_A : Optional[int] ... | 88 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
... | 88 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See a... | 88 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatur... | 88 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalD... | 88 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 88 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...u... | 88 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Any , _A : str=None , **_A : Union[str, Any] ) -> ... | 88 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a , __a , __a , __a , __a , __a , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
snake_case_ : Any ... | 88 |
_SCREAMING_SNAKE_CASE = {
"""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""",
"... | 88 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_SCREAMING_SNAKE_CASE = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
f... | 88 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 88 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_SCREAMING_SNAKE_CASE... | 88 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = u
for i in range(1 , __a ):
snake_case_ : Optional[Any] = temp * (u - i)
return temp
def SCREAMING_SNAKE_CA... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if digit_amount > 0:
return round(number - int(__a ) , __a )
return number - int(__a )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decimal_isolate(3_5.3_4_5, 1))
print(decimal_isol... | 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
snake_case_ : Dict = len(__a )
snake_case_ : Optional[int] = max(__a )
snake_case_ ... | 88 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 1 |
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[Any] = defaultdict(__a )
snake_case_ : Optional[Any] = []
... | 88 |
def SCREAMING_SNAKE_CASE__ ( __a = 60_08_51_47_51_43 ):
try:
snake_case_ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater t... | 88 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def SCREAMING_SNAKE_CASE__ ( __a ): # picklable for multiprocessi... | 88 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
fr... | 88 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 88 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_logger(__name_... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_... | 88 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_imag... | 88 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ ,snake_case_ : Dict = 9, 14 # noqa: F841
snake_case_ : Union[str, Any] = [
[0, 1, 4],
[0, ... | 88 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informe... | 88 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a ):
stooge(__a , 0 , len(__a ) - 1 )
return arr
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
snake_case... | 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
... | 88 | 1 |
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