code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class lowercase_ ( a ):
'''simple docstring'''
__lowerCAmelCase : str = field(de... | 447 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord i... | 447 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCamelCase : Dict = logging.get_logger(_... | 610 | '''simple docstring'''
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 : Optional[Any] = logging.get_logger(__name__)
Upper... | 610 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase_ : str = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjern... | 24 |
def lowercase_ (A : list , A : int = 0 ):
snake_case__ : List[str] = length or len(A )
snake_case__ : Any = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
snake_case__... | 478 | 0 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_a : Optional[Any] = logging.get_log... | 715 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 218 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
snake_case_ =None
snake_case_ =False
snake_case_ =False
snake_case_ =False
snake_case_ =Non... | 647 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCamelCase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (t... | 151 |
from copy import deepcopy
class A__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase__ : list[int] | None = None , lowerCamelCase__ : int | None = None ):
if arr is None and size is not None:
a__ : Uni... | 151 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase_ : s... | 461 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ : int = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_tran... | 461 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin ... | 226 |
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_forwar... | 226 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : List[Any] = 'timm_backbone'
def __init__( self : List[Any] , __lower... | 277 | """simple docstring"""
from collections.abc import Generator
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = 0, 1
while True:
_UpperCAmelCase , _UpperCAmelCase = b, a + b
yield b
def __UpperCAmelCase... | 277 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import repl... | 721 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"""google/umt5-small""": """https://huggingface.co/g... | 688 | 0 |
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared t... | 6 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _lowerCamelCase ... | 243 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self: Tuple , *__A: ... | 716 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 | 0 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path... | 509 |
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
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase ... | 509 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
__A : int ... | 237 | """simple docstring"""
lowerCamelCase : int =[0, 2, 4, 6, 8]
lowerCamelCase : List[str] =[1, 3, 5, 7, 9]
def _lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : li... | 237 | 1 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['torch', 'torchsde']
def __init__( self : Any , *UpperCAmelCase : int ... | 86 | def A__ ( snake_case_ : int ):
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
SCREAMING_SNAKE_CASE__: List[Any]= [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE__: List[str]= 1
if upper_limit > 0:
SCREAMING_SNAKE_CASE_... | 64 | 0 |
from functools import lru_cache
def A(__a: int ):
lowerCAmelCase_ = 2
lowerCAmelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__a )
if n > 1:
factors.add(__a )
return factors
@lru_cache
def A(__a: int ... | 226 |
def A(__a: int ):
lowerCAmelCase_ = abs(__a )
lowerCAmelCase_ = 0
while n > 0:
res += n % 10
n //= 10
return res
def A(__a: int ):
lowerCAmelCase_ = abs(__a )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def A(__a: int ... | 226 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
A__: in... | 380 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 0 |
from __future__ import annotations
class a__ :
def __init__( self : Optional[int] , lowerCamelCase_ : int = 0 ):
a_ : str = key
def UpperCAmelCase( self : Optional[Any] , lowerCamelCase_ : str ... | 718 |
from heapq import heappop, heappush
import numpy as np
def _a ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
a_ , a_ : Any = grid.shape
a_ : Dict = [-1, 1, 0, 0]
a_ : List[Any] = [0, 0, -... | 478 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_imag... | 86 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ : Optional[int] = [True] * 1_00_00_01
lowerCamelCase__ : List[Any] = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
lowerCamelCas... | 238 | 0 |
from __future__ import annotations
import pandas as pd
def UpperCamelCase ( snake_case__ : list[int] ,snake_case__ : list[int] ,snake_case__ : int ):
'''simple docstring'''
__snake_case :Optional[int] = [0] * no_of... | 291 |
from __future__ import annotations
import time
import numpy as np
lowerCamelCase__ = [8, 5, 9, 7]
lowerCamelCase__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase__ = [
[3, 2, 1, 4],
[0,... | 291 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils im... | 130 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F40... | 130 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase () -> Any:
A__ : Any = HfArgumentParser(lowercase_ )
A__ : List[Any] = parser.parse_args_into_dataclasses()[0]
A__ : str = TensorFlowBenchm... | 64 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 | 1 |
'''simple docstring'''
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 TensorT... | 331 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase_ : str = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-... | 331 | 1 |
"""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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condit... | 190 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowercase ( __UpperCAmelCase ):
_lowerCamelCase = (EulerDiscreteScheduler,)
_lowerCamelCase ... | 190 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is... | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 | 0 |
'''simple docstring'''
a__ : Optional[Any] ={
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBB... | 434 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CAS... | 434 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _a :
"""simple docstring"""
def __init__( self , _UpperCAmelCase ) -> Optional[int]:
UpperCamelCase_ = data
UpperCamelCase_ = [0... | 23 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/... | 156 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the... | 689 |
def UpperCamelCase_ ( a_ = 6008_5147_5143 ) ->int:
try:
A =int(a_ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
A =2
A =0
if n == 2:
r... | 689 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelin... | 633 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 633 | 1 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Ima... | 702 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple ... | 7 | 0 |
"""simple docstring"""
import os
import sys
import unittest
__UpperCamelCase : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_fil... | 450 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__UpperCamelCase : str = 4
__UpperCamelCase : List[str] = 3
... | 450 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeli... | 714 |
import numpy as np
def lowerCamelCase__ ( a : np.ndarray , a : np.ndarray , a : float = 1e-12 , a : int = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a )[0] == np.shape(a )[1]
# Ensure proper dimensionality.
assert np.shape(a )[0] ==... | 373 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _A ( __lowercase , __lowercase , __lowercase = None ):
"""simple docstring"""
if version.parse(hfh.__version__... | 129 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_A... | 129 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[list] ):
"""simple docstring"""
_snake_case : Optional[int] = current_set.copy()
for row_index, row in enumerate(_lowerCamelCase ):
_snake_case : Any = row[0]
for colum... | 705 |
"""simple docstring"""
import os
import sys
import unittest
A_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files... | 28 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__UpperCamelCase : List[Any] = 50000
__UpperCamelCase : str = 5000
__UpperCamelCase , __UpperCamelCase : Tuple = os.path.split(__file__)
__UpperCamelCa... | 519 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.util... | 41 | 0 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accel... | 179 | '''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 179 | 1 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def lowerCamelCase__ (_UpperCAmel... | 73 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {str(digit): digit**5 for digit in range(1_0)}
def lowercase__ ( __UpperCamelCase )-> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__UpperCamelCase ) )
def ... | 301 | 0 |
def snake_case_ ( lowercase__ : list , lowercase__ : int = 0 ) -> List[Any]:
'''simple docstring'''
_lowerCAmelCase =length or len(_A )
_lowerCAmelCase =False
for i in range(length - 1 ):
if list_data[i] > list_data[i + ... | 705 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__SCREAMING_SNAKE_CASE : List[str] = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''at... | 149 | 0 |
'''simple docstring'''
# 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_... | 127 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : List[str] , __snake_case : Any ) -> Lis... | 127 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class a__( lowerCamelCase__ ):
def __init__( self : List[str] , __snake_case : List[Any] , __snake_case : int ):
super().__init__()
self.register_modules(unet... | 195 |
'''simple docstring'''
class a__:
def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : Any , __snake_case : Tuple ):
a : List[str] = name
a : Dict = value
a : List[str] ... | 195 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :str =logging.get_logger(__name__)
__snake_case :int ={
"""tiiuae/falcon-40b""": """https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json""",
"""tiiuae/falcon-7b""": """http... | 106 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
lowerCamelCase_ = os.path.dirname(os.path.realpath(_lowerCamelCase ) )
lowerCamelCase_ = os.path.join(_lowerCamelCase , '''triangle.txt''' )
with open(_lowerCamelCase ) as f:
... | 142 | 0 |
from __future__ import annotations
from math import gcd
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : int , UpperCamelCase : int = 2 , UpperCamelCase : int = 1 , UpperCamelCase : int = 3 , ) -> int | None:
"""simple docstring"""
if num < 2:
raise ValueError... | 705 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.... | 403 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging... | 542 |
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,
PNDMSch... | 542 | 1 |
from string import ascii_uppercase
__a : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__a : Optional[int] = dict(enumerate(ascii_uppercase))
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
... | 522 | from __future__ import annotations
__a : str = """Muhammad Umer Farooq"""
__a : Optional[Any] = """MIT"""
__a : int = """1.0.0"""
__a : Optional[int] = """Muhammad Umer Farooq"""
__a : Dict = """contact@muhammadumerfaro... | 522 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : float , lowerCAmelCase : float , lowerCAmelCase : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum... | 561 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase :Dict = datasets.utils.logging.get_logger(__name__)
class _lowerCamelCase ( folder_based_builder.FolderBasedBuil... | 561 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .... | 719 |
from __future__ import annotations
import math
def UpperCamelCase_ ( a_ , a_ ) ->float:
A =u
for i in range(1 , a_ ):
A =temp * (u - i)
return temp
def UpperCamelCase_ ( ) ->None:
A =int(input("enter the numbers of values: " ) )
A =[]
for _ in ... | 689 | 0 |
import math
import tensorflow as tf
from packaging import version
def snake_case__ ( lowerCamelCase_ ):
A : Optional[int] = tf.convert_to_tensor(lowerCamelCase_ )
A : Union[str, Any] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(... | 542 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SI... | 542 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used i... | 717 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelin... | 279 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
return round(float(moles / volume ) * nfactor )
def __UpperCAmelCase ( _UpperCAmelCase : float , _UpperCAmelCase ... | 69 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCAmelCase : int = TypeVar('''KT''')
__lowerCAmelCase : Union[str, Any] = TypeVar('''VT''')
class _lowerC... | 58 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import f... | 715 | """simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
_lowercase : ... | 283 | 0 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_... | 662 | 1 |
"""simple docstring"""
__UpperCAmelCase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ :str = 0
while number:
... | 256 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
... | 256 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.u... | 688 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def A_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> np.ndarray:
_UpperCamelCase :str = int(np.ceil((x_end - xa) / step_... | 355 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 707 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FIL... | 172 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( _lowercase : list[int] , _lowercase : int ) -> bool:
__UpperCAmelCase: int = len(_lowercase )
__UpperCAmelCase: Optional[int] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for eac... | 523 | '''simple docstring'''
class a :
"""simple docstring"""
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: List[Any] = None
__UpperCAmelCase: Tuple = None
__UpperCAmelCase: L... | 523 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_f... | 703 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase ( a ) -> Optional[int]:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , arg... | 245 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 340 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging... | 297 | 0 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __sna... | 714 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {
'configuration_distilbert': [
'DISTILBERT_PRETRAIN... | 217 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_SCREAMING_SNAKE_CASE = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining a... | 239 |
"""simple docstring"""
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self , lowerCAmelCase__ ):
'''simple docstring'''
_UpperCamelCase : List[str] = size
_UpperCamelCase : Optional[int] = [0] * size
... | 239 | 1 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowercase ( lowerCAmelCase__ ):
for param in module.parameters():
lowerCamelCase_ = False
def lowercase ( ):
lowerCamelCase_ = '''cuda''' if torch.cuda.is_availabl... | 29 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __a ( lowerCAmelCase_ : int = 8 ) -> str:
'''simple docstring'''
UpperCAmelCase_= ascii_letters + digits + punctuation
return "".... | 593 | 0 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 702 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all fi... | 463 | 0 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclas... | 653 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configurat... | 653 | 1 |
"""simple docstring"""
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__ = logging.get_logger(__name__)
UpperCAm... | 702 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int ) -> bool:
if num < 0:
return False
_snake_case = num
_snake_case = 0
while num > 0:
_snake_case = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
... | 430 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class snake_case_ ( lowerCamelCase_ ):
"""simple docst... | 34 |
"""simple docstring"""
from itertools import permutations
def lowerCamelCase ( _snake_case ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ : List[str] = ... | 110 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _A... | 214 |
def _A ( __snake_case :int ) -> bool:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise ValueError("check_bouncy() accepts only integer arguments" )
__SCREAMING_SNAKE_CASE = str(__snake_case )
__SCRE... | 214 | 1 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase: List[str] = argpa... | 271 | from pathlib import Path
import fire
def lowerCAmelCase_ ( lowercase: str , lowercase: str , lowercase: int ) -> int:
'''simple docstring'''
_UpperCamelCase: Any = Path(lowercase )
_UpperCamelCase: int = Path(lowercase )
dest_dir.mkdir(exist_o... | 271 | 1 |
# 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-2.0
#
# Unless required by app... | 719 |
def _snake_case (_snake_case : int) -> bool:
if p < 2:
raise ValueError('p should not be less than 2!')
elif p == 2:
return True
_lowercase =4
_lowercase =(1 << p) - 1
for _ in range(p - 2):
_lowercase =((s * s) - 2) % m
ret... | 557 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _... | 82 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
f... | 227 | 0 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 98 |
'''simple docstring'''
from manim import *
class a__ ( a__ ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
lowerCAmelCase__ = Rectangle(height=0.5 , width=... | 98 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 85 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCamelCase ="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot a... | 208 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _snake_case ( __snake_case , __snake_case = "cpu" , __snake_case = None ) -> Any:
'''simple docstring'''
UpperCAmelCase_ : Dict = torch.load(lowercase__ , map_lo... | 702 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = """ClapFeatureExtractor"""
_lowerCamelCase = ("""RobertaTokenizer""", """RobertaTokeniz... | 455 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def lowerCamelCase__ ( __lowerCamelCase : int ):
return input_array.reshape((input_array.size, 1) )
de... | 63 |
"""simple docstring"""
def lowercase__(A ) ->str:
"""simple docstring"""
if isinstance(A , A ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(A , A ):
raise TypeErro... | 218 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCamelCase )... | 694 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case ( __low... | 694 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def snake_case_ (UpperCamelCase : Tuple ):
'''simple docstring'''
_a = os.path.join(... | 22 |
def _lowerCamelCase ( __A : str ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__A ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod()
| 485 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
snake_case_ = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _lowerCamelCase( UpperCamelCase__ : str = "mumbai" ):
A ... | 717 |
'''simple docstring'''
from math import pi
def _lowerCamelCase( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 537 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils imp... | 74 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from t... | 571 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCamelCase__ : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
... | 718 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCAmelCase ( lowerCamelCase_ : Tuple , lowerCamelCase_ : int=() , lowerCamelCase_ ... | 685 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( ... | 323 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeat... | 323 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_case ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@register_to_config
def __... | 198 |
import sys
a__ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6689664895044524452316173185640... | 198 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configur... | 614 | '''simple docstring'''
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - gener... | 614 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A_ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class a_ ( snake_c... | 711 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import f... | 696 | 0 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ... | 483 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : Union[str, Any] ={}
class lowercase_ ( UpperCamelCase__):
"""simple docstring"""
... | 483 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__a : str = ""
__a : List[Any] = ""
__a : Union[str, Any] = ""
__a : Optional[Any] = 1 # (0 is vertical, 1 is horizontal)
def _SCREAMING_SNAKE_CAS... | 716 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _SCREAMING_SNAKE_CASE ( __lowercase : str ) -> str:
"""simple docstring"""
if "cls_token" in name:
__A ... | 199 | 0 |
from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 472 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _lowercase ( *UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_=True , UpperCamelCase_=2 ) -> Optional[int]:
'''simple docstring'''
... | 472 | 1 |
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_available():
import torch
... | 149 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__SCREAMING_SNAKE_CASE : List[str] = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''at... | 149 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
fr... | 631 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE__ = (720, 1_280) # Height, Width
SCREAMING_SNAKE_CASE__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__ ... | 631 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
return "\n".join(
f"{number} * {i} = {number * i}" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(m... | 487 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class a__ :
lowercase_ = None
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[An... | 487 | 1 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase__ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , _A , _A , _A , _A , _A=1 , _A=Fals... | 102 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
UpperCa... | 384 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Any:
if "cls_token" in name:
A_ = name.r... | 667 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
a_ : List[str] = object()
# For specifying empty leaf dict `{}`
a_ : str = object()
de... | 676 |
'''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_... | 8 | 0 |
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 2 ):
'''simple docstring'''
__UpperCamelCase :Tuple = qubits
# Using Aer's simulator
__UpperCamelCase :Optional[int] = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acti... | 452 | import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , ... | 452 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 554 | '''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common ... | 427 | 0 |
"""simple docstring"""
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_downlo... | 718 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[... | 95 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import lo... | 356 |
'''simple docstring'''
def lowerCamelCase__ ( a ):
__snake_case = [0] * len(a )
__snake_case = []
__snake_case = []
__snake_case = 0
for values in graph.values():
for i in values:
indegree[i] += 1
... | 356 | 1 |
def snake_case_ ( __lowercase ):
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCAmelCase_ : List[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCAmelCase_ : Optional[Any] ... | 641 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : str = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'... | 641 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/c... | 328 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingfa... | 201 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_in... | 710 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if n == 1 or not isinstance(UpperCamelCase , UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
_a = ... | 377 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCamelCase ( UpperCAmelCase_ : Tuple="ro", UpperCAmelCase_ : Any="en", UpperCAmelCase_ : List[str]="wmt16", UpperCAmelCase_ : Dict=None ) -> None:
... | 104 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
snake_case_ =None
snake_case_ =False
snake_case_ =False
snake_case_ =False
snake_case_ =Non... | 647 | 0 |
import os
import numpy
import onnx
def lowerCAmelCase_ ( __UpperCAmelCase: Any , __UpperCAmelCase: Tuple ) -> List[Any]:
UpperCamelCase__ : str = a.name
UpperCamelCase__ : str = b.name
UpperCamelCase__ : Tupl... | 715 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''... | 369 | 0 |
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