code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCAmelCase_ : int = pd.read_csv("sample_data.csv", header=No... | 120 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
UpperCAmelCase_ ... | 120 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 120 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[str] = year % 1_9
_a : int = year % 4
_a : Optional[int] = year % 7
_a : Any = math.floor(year /... | 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Dict = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCamelCase_ , **lowerCam... | 120 |
'''simple docstring'''
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 Mod... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 1 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def UpperCAmelCase_ ( A ):
'''simple docstring'''
@wraps(A )
def _inner_fn(*A , **A ):
warnings.warn(
(f'''\'{fn.__name__}\' is experimental and might be subjec... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[int] = []
_a : Optional[Any] ... | 120 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 1 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassif... | 120 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ : int = logging.get_logger(__name... | 120 | 1 |
'''simple docstring'''
import string
import numpy
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , A )
class a :
'''simple docstring'''
__lowerCAmelCase : Dict = string.ascii_upperca... | 120 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 1 |
'''simple docstring'''
from functools import lru_cache
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[int] = 2
_a : Dict = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(A )
... | 120 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCAmelCase_ ( A , A , A , A , A = None , A = None , A = None , ):
'''simple docstring'''
if config_... | 120 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
def count_of_possible_combinations(A ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for ... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
'''simple docstring'''
import numpy as np
import datasets
UpperCAmelCase_ : Optional[int] = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the E... | 120 |
'''simple docstring'''
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {"configuration_reformer": ["REFORMER_PRETRAINED_CO... | 120 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 1 |
'''simple docstring'''
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_tokenizatio... | 120 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5Onnx... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[int] = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegas... | 120 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : Tuple = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dor... | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__snake_case = logging.get_logger(__name__)
def _A ( _lowercase ) -> List[int]:
"""simple docstring"""
if isinstance(_lowercase , n... | 1 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modelin... | 2 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 0 |
'''simple docstring'''
def A_( A : int = 5000_0000):
UpperCamelCase = set()
UpperCamelCase = int((limit - 24) ** (1 / 2))
UpperCamelCase = set(range(3 , prime_square_limit + 1 , 2))
primes.add(2)
for p in range(3 , prime_... | 3 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase ,lowerCAmelCase = analyze_text(_UpperCAmelCase )
lowerCAmelCase = list(' '... | 4 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 0 |
'''simple docstring'''
import os
def A ():
with open(os.path.dirname(__lowerCamelCase ) + """/p022_names.txt""" ) as file:
_lowerCAmelCase = str(file.readlines()[0] )
_lowerCAmelCase = names.replace("""\"""" , """""" ).split(""",""" ... | 5 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ ( metaclass=UpperCamelCase__ ):
lowerCamelCase_ = ["flax"]
def __init__( self :Dict , *__A :List[str] , **__A :Dict ) -> Union[str, Any]:
"""simple do... | 6 |
'''simple docstring'''
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 Mod... | 120 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int = 4 ) -> list[list[int]]:
'''simple docstring'''
_A = abs(_snake_case ) or 4
return [[1 + x + y * row_size for x in range(_snake_case )] for y in range(_snak... | 7 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowerc... | 8 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 0 |
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 __lowerCAmelCase ( UpperCAmelCase_ ):
""... | 9 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDAR... | 10 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ : int = logging.get_logger(__name... | 120 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDCondition... | 11 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 0 |
from __future__ import annotations
import time
import numpy as np
lowerCamelCase__ : List[str] = [8, 5, 9, 7]
lowerCamelCase__ : Union[str, Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase__ : Union[str, A... | 12 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> bool:
__lowerCamelCase : Optional[int] = str(UpperCAmelCase_ )
return len(UpperCAmelCase_ ) == 9 and set(UpperCAmelCase_ ) == se... | 13 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 0 |
import collections
import os
import re
from pathlib import Path
a__ = '''src/transformers'''
# Matches is_xxx_available()
a__ = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
a__ = re.compile(R'''^_import_struc... | 14 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 0 |
from ...configuration_utils import PretrainedConfig
A : Optional[int] = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/goog... | 15 |
'''simple docstring'''
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizer... | 16 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 17 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
... | 18 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 0 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _UpperCAmelCase( nn.Module ):
def __init__( self , __a = 16 , __a = 88 , __... | 19 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase: Optional[int] = logging.get_logger(__name__)
class lowercase_ (lowercase__ , ... | 20 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 0 |
import json
import sys
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with open(lowerCamelCase , encoding="""utf-8""" ) as f:
__magic_name__ : Tuple =json.load(lowerCamelCase )
__magic_name__ : Optional[Any] =[""... | 21 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 0 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDatase... | 22 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case__ : List[Any] = (3, 9, -1_1, 0, 7, 5, 1, -1)
snake_case__ : Optional[Any] = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class ... | 23 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase_ : Union[str, Any] = [True] * 1_0_0_0_0_0_1
UpperCAmelCase_ : Optional[Any] = 2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
UpperCAmelCase_ ... | 24 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = args.log_outputs
SCREAMING_SNA... | 25 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 0 |
'''simple docstring'''
import operator as op
def _a ( _lowerCamelCase ) -> Tuple:
"""simple docstring"""
__snake_case : List[Any] = []
__snake_case : Optional[int] = lambda _lowerCamelCase ... | 26 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
... | 27 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 0 |
'''simple docstring'''
from timeit import timeit
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
if number < 0:
raise ValueError('the value of input must not be negative' )
SCREAMING_SNAKE_CASE : Optional[Any... | 28 |
'''simple docstring'''
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 Mod... | 120 | 0 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
A_ = 300 # TEMPERATURE (unit = K)
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,):
if donor_conc <= 0:
raise ValueError('''Donor concentration should be... | 29 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__a = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'... | 30 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_p... | 31 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_e... | 32 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ : int = logging.get_logger(__name... | 120 | 0 |
from __future__ import annotations
from typing import Any
class __magic_name__ :
'''simple docstring'''
def __init__( self:Tuple , _a:int ):
snake_case__ = num_of_nodes
snake_case__ = []
snake_case__ = {}
def SCREAMI... | 33 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 0 |
"""simple docstring"""
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 ... | 34 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : List[Any] = (DDIMParallelScheduler,)
lowerCamelCase : Union[str, Any] = (('''eta''', 0.0), ('''num_inference_st... | 35 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 0 |
from math import factorial
__lowercase : Optional[Any] = {str(d): factorial(d) for d in range(10)}
def lowercase ( __A : int ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(__A ) )
def lowercase ( ) -> int:... | 36 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 0 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_E... | 37 |
'''simple docstring'''
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( __magic_name__ : list[int] ) -> int:
'''simple docstring'''
if not nums:
return 0
snake_case__ : Dict = nums[0]
snake_case__ : str = 0
for num in nums[1:]:
sna... | 38 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []... | 39 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase_ ... | 40 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTe... | 41 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .... | 42 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 0 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _a ( SCREAMING_SNAKE_CASE ):
"""simpl... | 43 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 0 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
fr... | 44 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez... | 45 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 46 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_com... | 47 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : Optional[Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitP... | 48 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from .... | 49 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 0 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCamelCase : Dict = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'... | 50 |
'''simple docstring'''
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 Mod... | 120 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenerat... | 51 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __A ( a_ :Dataset , a_ :Dict[str, str]) -> int:
... | 52 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 0 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : str ):
def get_matched_characters(lowerCAmelCase_ : str, lowerCAmelCase_ : str ) -> str:
__lowerCAmelCase = []
__lowerCAmelCase = min(len(_stra ), len(_stra ... | 53 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__lowercase : Optional[int] =logging.get_logger(__name__)
__lowercase : List[Any] ={"""vocab_file""... | 54 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ : int = logging.get_logger(__name... | 120 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
... | 55 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a (lowercase__ : List[Any] , lowercase__ : List[str... | 56 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 57 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__lowerCAmelCase : List[Any] = '''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform... | 58 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 |
'''simple docstring'''
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 60 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def _A ( lowerCAmelCase_ : Iterable[str] , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = iter(lowerCAmelCase_ )
while True:
lowerC... | 61 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 0 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 62 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 0 |
import functools
def A__ ( snake_case_ : list[int] , snake_case_ : list[int] ):
# Validation
if not isinstance(snake_case_ , snake_case_ ) or not all(isinstance(snake_case_ , snake_case_ ) for day in days ):
raise ValueError('''The parameter days shou... | 64 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trai... | 65 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
raise Optional... | 66 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class A_ ( ... | 67 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 0 |
import math
import os
import sys
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
__UpperCAmelCase =""""""
try:
with open(A_ , """rb""" ) as binary_file:
__UpperCAmelCase =binary_file.r... | 68 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 69 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 70 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing impo... | 71 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 0 |
'''simple docstring'''
from math import loga
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowercase_ , lowercase_ ):
raise TypeError('''Input value mus... | 72 |
'''simple docstring'''
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 Mod... | 120 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.s... | 74 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transfor... | 75 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : Optional[int] = len(__UpperCamelCase )
__lowercase : Dict = len(__UpperCamelCase )
__lowercase : int = [[False for _ in range(m + ... | 76 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ : int = logging.get_logger(__name... | 120 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Autoforme... | 77 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 0 |
'''simple docstring'''
import unittest
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_inputs
if is_torch_ava... | 78 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
... | 79 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 0 |
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