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 |
|---|---|---|---|---|
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Dict = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : st... | 285 |
class __snake_case :
def __init__( self ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Tuple = data
lowercase : List[Any] = previous
lowercase : List[str] = next_... | 285 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowercase : str = 50000
lowercase : str = 5000
lowercase , lowercase : List[str] = os.path.split(__file__)
lowercase : Any = os.path.join(RES... | 285 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase : Tuple = """<<<<<<< This should probably be modified because it mentions: """
lowercase : Any = ... | 285 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _snake_case( SCREAMING_SNAKE_CASE__ = 3 ) -> qiskit.result.counts.Counts:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
... | 285 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
... | 285 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case( SCREAMING_SNAKE_CASE__ = "laptop" ) -> DataFrame:
lowercase : Tuple = f"https://www.amazon.in/laptop/s?k={product}"
lowercase : i... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase : str ... | 285 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetCo... | 285 |
from collections.abc import Callable
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> np.array:
lowercase : Optional[int] = int(np.ceil((x_en... | 285 | 1 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCAmelCase ):
_a : Any= ["flax", "transformers"]
def __init__( self ,*snake_case ,**snake_case ):
'''simple docstring'''
requires_backends(self ,["""flax""", """transfo... | 285 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 285 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeling_tf_auto import TF_MODEL_F... | 285 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padding... | 285 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowercase : Union[str, Any] = get_tests_dir("""fixtures/test_se... | 285 |
from collections.abc import Generator
def _snake_case( ) -> Generator[int, None, None]:
lowercase , lowercase : List[str] = 0, 1
while True:
lowercase , lowercase : Optional[int] = b, a + b
yield b
... | 285 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> bool:
lowercase : Optional[int] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 285 |
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Dict = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : st... | 285 | 1 |
from collections.abc import Sequence
from queue import Queue
class __snake_case :
def __init__( self ,snake_case ,snake_case ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Any = start
lowercase : ... | 285 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case( ) -> tuple[list[int], int]:
lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lowercase : ... | 285 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __snake_case ( lowerCAmelCase ):
_a : int= "Wav2Vec2FeatureExtracto... | 285 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 | 1 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 285 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowercase : ... | 285 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase : List[str] = """src/transformers"""
lowercase : Dict = ... | 285 |
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 __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 1 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Tuple = {
"""google/umt5-small""": """https://huggingface.co/g... | 285 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""google/bit-50... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, loa... | 285 |
from ...processing_utils import ProcessorMixin
class __snake_case ( lowerCAmelCase ):
_a : Union[str, Any]= "WhisperFeatureExtractor"
_a : int= "WhisperTokenizer"
def __init__( self ,snake_case ,snake_case ):
'''simple docstring'''
super().__... | 285 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : List[Any] = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispe... | 285 |
from bisect import bisect
from itertools import accumulate
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Dict = sorted(zip(SCREAMING_SNAKE_CASE__ , SCREAMIN... | 285 | 1 |
from collections.abc import Generator
def _snake_case( ) -> Generator[int, None, None]:
lowercase , lowercase : List[str] = 0, 1
while True:
lowercase , lowercase : Optional[int] = b, a + b
yield b
... | 285 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase : Union[str, Any] = logging.get_logger(_... | 285 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 285 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : Tuple = logging.get_logger(__name__)
lowercase : List[str] = {
"""shi-labs/dinat-mini-in1... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1... | 285 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
... | 285 |
class __snake_case :
def __init__( self ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Tuple = data
lowercase : List[Any] = previous
lowercase : List[str] = next_... | 285 | 1 |
# Lint as: python3
import itertools
import os
import re
lowercase : Tuple = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
lowercase : Union[str, Any] = re.compile(R"""([a-z\d])([A-Z])""")
lowercase : List[Any] = re.compile(R"""(?<!_)_(?!_)""")
lowercase : List[str] = ... | 285 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase : Tuple = """<<<<<<< This should probably be modified because it mentions: """
lowercase : Any = ... | 285 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list:
for i in range(len(SCREAMING_SNAKE_CASE__ ) - 1 , 0 , -1 ):
lowercase : List[Any] = False
for j in range(SCREAMING_SNAKE_CASE__ , 0 , -1 ):
if unsorted[j]... | 285 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
... | 285 | 1 |
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 PretrainedConfig
from ...onnx import OnnxConfig
from... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase : str ... | 285 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 285 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers im... | 285 |
from collections.abc import Callable
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> np.array:
lowercase : Optional[int] = int(np.ceil((x_en... | 285 | 1 |
import math
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 285 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 285 | 1 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowercase : Tuple = (
"""This metric will be removed from the library soon, metrics sho... | 285 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padding... | 285 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
... | 285 |
from collections.abc import Generator
def _snake_case( ) -> Generator[int, None, None]:
lowercase , lowercase : List[str] = 0, 1
while True:
lowercase , lowercase : Optional[int] = b, a + b
yield b
... | 285 | 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_to_model_forward
fro... | 285 |
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Dict = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : st... | 285 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogit... | 285 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case( ) -> tuple[list[int], int]:
lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lowercase : ... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Union[str, Any] = {}
class __snake_case ( lowerCAmelCase ):
_a : int= "llama"
_a : Union[str, Any]= ["past_ke... | 285 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json""... | 285 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowercase : ... | 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : List[Any] = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaC... | 285 |
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 __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[Any]:
# A local function to see if a dot lands in the circle.
def is_in_circle(SCREAMING_SNAKE_CASE__ , SCREAMI... | 285 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Tuple = {
"""google/umt5-small""": """https://huggingface.co/g... | 285 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thu... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 | 1 |
from torch import nn
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> str:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise Va... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 1 |
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,
)
class __snake_case ( nn.Module ):
de... | 285 |
from ...processing_utils import ProcessorMixin
class __snake_case ( lowerCAmelCase ):
_a : Union[str, Any]= "WhisperFeatureExtractor"
_a : int= "WhisperTokenizer"
def __init__( self ,snake_case ,snake_case ):
'''simple docstring'''
super().__... | 285 | 1 |
class __snake_case :
def __init__( self ,snake_case ,snake_case ,snake_case ):
'''simple docstring'''
lowercase : List[str] = name
lowercase : List[str] = value
lowercase : int = weight
def __rep... | 285 |
from bisect import bisect
from itertools import accumulate
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Dict = sorted(zip(SCREAMING_SNAKE_CASE__ , SCREAMIN... | 285 | 1 |
import numpy as np
lowercase : Dict = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class... | 285 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase : Union[str, Any] = logging.get_logger(_... | 285 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
lowercase : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowercase : Any = n - k
# Calculate C(n,k)
fo... | 285 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padding... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1... | 285 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
Ba... | 285 |
class __snake_case :
def __init__( self ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Tuple = data
lowercase : List[Any] = previous
lowercase : List[str] = next_... | 285 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_ver... | 285 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase : Tuple = """<<<<<<< This should probably be modified because it mentions: """
lowercase : Any = ... | 285 | 1 |
import math
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(SCREAMING_SNAKE_CASE__ )
else:
if x == 0: ... | 285 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
... | 285 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeature... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase : str ... | 285 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase : List[Any] = logging.get_logger(__name__)
class __snake_case ( lowerCAmelCase ):
def __init__( self ,*snake_case ,**snake_case ):
'''simple docstring'''
... | 285 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 |
from collections.abc import Callable
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> np.array:
lowercase : Optional[int] = int(np.ceil((x_en... | 285 | 1 |
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
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : List[str] = "... | 285 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 285 | 1 |
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 __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padding... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : str = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/... | 285 |
from collections.abc import Generator
def _snake_case( ) -> Generator[int, None, None]:
lowercase , lowercase : List[str] = 0, 1
while True:
lowercase , lowercase : Optional[int] = b, a + b
yield b
... | 285 | 1 |
lowercase : Dict = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": """ABAAB""... | 285 |
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Dict = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : st... | 285 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 285 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case( ) -> tuple[list[int], int]:
lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lowercase : ... | 285 | 1 |
from collections.abc import Callable
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> np.array:
lowercase : Optional[int] = int(np.ceil((x_en... | 285 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Traini... | 285 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowercase : ... | 285 | 1 |
from __future__ import annotations
from typing import Any
class __snake_case :
def __init__( self ,snake_case = 6 ):
'''simple docstring'''
lowercase : Node | None = None
lowercase : Node | None = None
self.create_lin... | 285 |
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 __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _snake_case( ) -> str:
raise RuntimeError("""CUDA out of memory.""" )
class __snake_case ( nn.Module ... | 285 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Tuple = {
"""google/umt5-small""": """https://huggingface.co/g... | 285 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class __... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : List[Any] = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config... | 285 |
from ...processing_utils import ProcessorMixin
class __snake_case ( lowerCAmelCase ):
_a : Union[str, Any]= "WhisperFeatureExtractor"
_a : int= "WhisperTokenizer"
def __init__( self ,snake_case ,snake_case ):
'''simple docstring'''
super().__... | 285 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_to... | 285 |
from bisect import bisect
from itertools import accumulate
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Dict = sorted(zip(SCREAMING_SNAKE_CASE__ , SCREAMIN... | 285 | 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
lowercase : Any = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFi... | 285 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase : Union[str, Any] = logging.get_logger(_... | 285 | 1 |
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 _snake_case( SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : ... | 285 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = " " ) -> list:
lowercase : str = []
lowercase : int = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE__ ):
if char == separator:
split... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1... | 285 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dumm... | 285 |
class __snake_case :
def __init__( self ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Tuple = data
lowercase : List[Any] = previous
lowercase : List[str] = next_... | 285 | 1 |
class __snake_case :
def __init__( self ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Tuple = data
lowercase : List[Any] = previous
lowercase : List[str] = next_... | 285 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase : Tuple = """<<<<<<< This should probably be modified because it mentions: """
lowercase : Any = ... | 285 | 1 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCAmelCase ):
_a : Dict= ["torch", "transformers", "onnx"]
def __init__( self ,*snake_case ,**snake_case ):
'''simple docstring'''
requires_backends(self ,["""torch""",... | 285 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
... | 285 | 1 |
from bisect import bisect
from itertools import accumulate
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Dict = sorted(zip(SCREAMING_SNAKE_CASE__ , SCREAMIN... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase : str ... | 285 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
lowercase : Tuple = {}
lowercase ... | 285 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 | 1 |
from __future__ import annotations
from math import pi
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argum... | 285 |
from collections.abc import Callable
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> np.array:
lowercase : Optional[int] = int(np.ceil((x_en... | 285 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
... | 285 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 285 | 1 |
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
lowercase : Optional[int] = int(np.ceil((x_end - xa) / h ) )
lo... | 285 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padding... | 285 | 1 |
import qiskit
def _snake_case( SCREAMING_SNAKE_CASE__ = 2 ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qubits
# Using Aer's simulator
lowercase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creat... | 285 |
from collections.abc import Generator
def _snake_case( ) -> Generator[int, None, None]:
lowercase , lowercase : List[str] = 0, 1
while True:
lowercase , lowercase : Optional[int] = b, a + b
yield b
... | 285 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smar... | 285 |
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Dict = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : st... | 285 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if ... | 285 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case( ) -> tuple[list[int], int]:
lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lowercase : ... | 285 | 1 |
import warnings
from functools import wraps
from typing import Callable
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Callable:
@wraps(SCREAMING_SNAKE_CASE__ )
def _inner_fn(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
... | 285 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer... | 285 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowercase : ... | 285 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import... | 285 |
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 __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 1 |
from functools import reduce
lowercase : int = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""668... | 285 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Tuple = {
"""google/umt5-small""": """https://huggingface.co/g... | 285 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils ... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 | 1 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list:
if n_term == "":
return []
lowercase : list = []
for temp in range(int(SCREAMING_SNAKE_CASE__ ) ):
series.append(f"1/{temp + 1}" if series else """1""" )
return series
i... | 285 |
from ...processing_utils import ProcessorMixin
class __snake_case ( lowerCAmelCase ):
_a : Union[str, Any]= "WhisperFeatureExtractor"
_a : int= "WhisperTokenizer"
def __init__( self ,snake_case ,snake_case ):
'''simple docstring'''
super().__... | 285 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 285 |
from bisect import bisect
from itertools import accumulate
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Dict = sorted(zip(SCREAMING_SNAKE_CASE__ , SCREAMIN... | 285 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerConfig... | 285 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase : Union[str, Any] = logging.get_logger(_... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
lowercase : Optional[Any] = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingfac... | 285 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 | 1 |
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 __snake_case ( lowerCAmelCase ):
_a : Union[str, Any]... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1... | 285 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 |
class __snake_case :
def __init__( self ,snake_case ,snake_case=None ,snake_case=None ):
'''simple docstring'''
lowercase : Tuple = data
lowercase : List[Any] = previous
lowercase : List[str] = next_... | 285 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""t5-small""": """https://huggingface.co/t5-sma... | 285 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase : Tuple = """<<<<<<< This should probably be modified because it mentions: """
lowercase : Any = ... | 285 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowercase : List[Any] = logging.get_logger(__name__)
class __snake_case ( lowerCAmelCase ):
def __init__( self ,*snake_case ,**snake_case ):
'''simple docstring'''
... | 285 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
... | 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase : str ... | 285 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 285 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 285 |
from collections.abc import Callable
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> np.array:
lowercase : Optional[int] = int(np.ceil((x_en... | 285 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowercase : Tuple = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu... | 285 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 285 | 1 |
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 np
from decord import VideoReader
if is_to... | 285 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padding... | 285 | 1 |
import functools
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
# Validation
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or not all(isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for day in days )... | 285 |
from collections.abc import Generator
def _snake_case( ) -> Generator[int, None, None]:
lowercase , lowercase : List[str] = 0, 1
while True:
lowercase , lowercase : Optional[int] = b, a + b
yield b
... | 285 | 1 |
import socket
def _snake_case( ) -> List[Any]:
lowercase : Optional[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowercase : Any = socket.gethostname()
lowercase : List[Any] = 12_312
sock.connect((... | 285 |
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Dict = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : st... | 285 | 1 |
from __future__ import annotations
import time
import numpy as np
lowercase : Optional[Any] = [8, 5, 9, 7]
lowercase : Union[str, Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowercase : Optional[Any] = [
[3, 2,... | 285 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case( ) -> tuple[list[int], int]:
lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lowercase : ... | 285 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowercase : Tuple = logging.get_logger(__name__)
class __snake_case ( lowerCAmelCase ):
def __init__( self ,*snake_case ,**snake_case ):
'''simple d... | 285 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 | 1 |
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