code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def snake_case ( A__ ):
return "".join([hex(A__ )[2:].zfill(2 ).upper() for byte in list(A__ )] )
def snake_case ( A__ ):
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(A__ ) % 2)... | 268 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 268 | 1 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = len(A__ )
# We need to create solution object to save path.
UpperCAmelCase_ : Dict = [[0 for _ in range(A__ )] for _ in range(A__ )]
... | 268 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ : Any = data
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ ... | 268 | 1 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
if index == number_of_items:
return 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : Tuple = knapsack(A__ ,A__ ,A__ ,A__ ,index + 1 )
i... | 268 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()... | 268 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 268 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase_ (__A , ... | 268 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCamelCase_ (__A ):
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[st... | 268 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 268 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCamelCase_ (__A ):
__magic_name__ ... | 268 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''t5-small''': '''https://huggingface.co/t... | 268 | 1 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoin... | 268 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 268 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def snake_case ( A__ ):
UpperCAmelCase_ : Optional[Any] = np.max(A__ ,axis=-1 ,keepdims=A__ )
UpperCAmelCase_ : List[Any] = np.exp(outputs - maxes )
return shifted_exp / ... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase_ : Dict = (boundary[1] - boundary[0]) / steps
UpperCAmelCase_ : Optional[int] = boundary[0]
UpperCAmelCase_ : s... | 268 | 1 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ =... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case ( A__ ,A__ ,A__ ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ,):
UpperCAmelCase_ : int = len(A__ )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row ==... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0... | 268 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 268 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 268 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test... | 268 |
"""simple docstring"""
from torch import nn
def snake_case ( A__ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {ac... | 268 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp impor... | 268 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
if index == number_of_items:
return 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : Tuple = knapsack(A__ ,A__ ,A__ ,A__ ,index + 1 )
i... | 268 |
"""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_availa... | 268 | 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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = ... | 268 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import V... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_pro... | 268 |
"""simple docstring"""
lowerCamelCase_ = [
(1000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def... | 268 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase_ (__A ... | 268 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 268 | 1 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def snake_case ( A__ ):
return ConvertCommand(
args.model_type ,args.tf_checkpoint ,args.pytorch_dump_output ,args.config ,args.finetuning_task_na... | 268 |
"""simple docstring"""
import os
def snake_case ( ):
with open(os.path.dirname(A__ ) + "/grid.txt" ) as f:
UpperCAmelCase_ : Any = [] # noqa: E741
for _ in range(20 ):
l.append([int(A__ ) for x in f.readline().split()] )
UpperCAm... | 268 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from tr... | 268 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = SwinConfig(image_size=1_92 )
if "base" in mod... | 268 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ ... | 268 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV... | 268 | 1 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class UpperCamelCase_ (__A ):
def __init__( self... | 268 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 268 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMo... | 268 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ : Any = data
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ ... | 268 | 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... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
if index == number_of_items:
return 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : Tuple = knapsack(A__ ,A__ ,A__ ,A__ ,index + 1 )
i... | 268 | 1 |
"""simple docstring"""
import math
def snake_case ( A__ = 1_00 ):
UpperCAmelCase_ : int = sum(i * i for i in range(1 ,n + 1 ) )
UpperCAmelCase_ : Union[str, Any] = int(math.pow(sum(range(1 ,n + 1 ) ) ,2 ) )
return square_of_sum... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 268 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {
'''configuration_layoutlmv3''': [
''... | 268 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase_ = 10
def snake_case ( A__ ,A__ ,A__ ,A__ ):
for i in range(A__ ,A_... | 268 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 268 | 1 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : Dict = 0
if start < end:
UpperCAmelCase_ : Any = randint(A__ ,A__ )
UpperCAmelCase_ ... | 268 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''t5-small''': '''https://huggingface.co/t... | 268 | 1 |
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Be... | 268 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
class UpperCamelCase_ :
def __init__( self : Optional[Any] , lowerCAmelCase_ : int | None = None ) -> Any:
UpperCAmelCase_ : Union[str, Any] = value
UpperCAme... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase_ : Dict = (boundary[1] - boundary[0]) / steps
UpperCAmelCase_ : Optional[int] = boundary[0]
UpperCAmelCase_ : s... | 268 | 1 |
"""simple docstring"""
import numpy as np
def snake_case ( A__ ):
return 1 / (1 + np.exp(-vector ))
def snake_case ( A__ ):
return vector * sigmoid(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 268 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case ( A__ ,A__ ,A__ ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
return int((input_a, input_a).count(0 ) == 0 )
def snake_case ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if __na... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
lowerCamelCase_ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-la... | 268 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowerCamelCase_ = TypeVar('''T''')
class UpperCamelCase_ (Generic[T] ):
__magic_name__ = 42 # Cache store of keys
__magic_name__ ... | 268 |
"""simple docstring"""
from torch import nn
def snake_case ( A__ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {ac... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0,... | 268 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""... | 268 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/umt5-small''': '''https://hugging... | 268 |
"""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_availa... | 268 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
f... | 268 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import V... | 268 | 1 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 |
"""simple docstring"""
lowerCamelCase_ = [
(1000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def... | 268 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
lowerCamelCase_ ... | 268 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 268 | 1 |
"""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_availa... | 268 |
"""simple docstring"""
import os
def snake_case ( ):
with open(os.path.dirname(A__ ) + "/grid.txt" ) as f:
UpperCAmelCase_ : Any = [] # noqa: E741
for _ in range(20 ):
l.append([int(A__ ) for x in f.readline().split()] )
UpperCAm... | 268 | 1 |
"""simple docstring"""
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_a... | 268 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = SwinConfig(image_size=1_92 )
if "base" in mod... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ = 0 ,A__ = 0 ):
UpperCAmelCase_ : List[Any] = right or len(A__ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
... | 268 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ = 10 ,A__ = 22 ):
UpperCAmelCase_ : Dict = range(1 ,A__ )
UpperCAmelCase_ : Optional[int] = range(1 ,A__ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) =... | 268 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 268 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 268 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ : Any = data
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ ... | 268 | 1 |
"""simple docstring"""
lowerCamelCase_ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': ''... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
if index == number_of_items:
return 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : Tuple = knapsack(A__ ,A__ ,A__ ,A__ ,index + 1 )
i... | 268 | 1 |
"""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 .t... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import fl... | 268 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def snake_case ( A__ ,A__=0 ):
return sorted(A__ ,key=lambda A__ : x[column] )
def snake_case ( A__ ,A__ ,A__=float("inf" ) ... | 268 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 1 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.confi... | 268 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 268 | 1 |
"""simple docstring"""
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, torc... | 268 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''t5-small''': '''https://huggingface.co/t... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'{price_plus_tax(100, 0.25) = }')
print(f'{price_plus_tax(125.50, 0.05) = }')
| 268 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 268 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
f... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase_ : Dict = (boundary[1] - boundary[0]) / steps
UpperCAmelCase_ : Optional[int] = boundary[0]
UpperCAmelCase_ : s... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that g... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case ( A__ ,A__ ,A__ ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[... | 268 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_t... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0... | 268 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolv... | 268 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCamelCase_ :
__magic_name__ = 42
__magic_name__ = None
__magic_name__ ... | 268 |
"""simple docstring"""
from torch import nn
def snake_case ( A__ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {ac... | 268 | 1 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowerCamelCase_ = logging.get_logger(__name__)
class UpperCamelCase_ :
__magic_name__ = None
@experimental
def snak... | 268 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ = {'''configuration_glpn''': ['''GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GLPNConfig''']}
try:
if not is_vision_availa... | 268 |
"""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_availa... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenizati... | 268 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import V... | 268 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrate... | 268 |
"""simple docstring"""
lowerCamelCase_ = [
(1000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def... | 268 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 268 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 268 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ (metaclass=__A ):
__magic_name__ = ['''onnx''']
def __init__( self : Optional[int] , *lowerCAmelCase_ : Dict , **lowerCAmelCase_ : Optional[Any] ) -> Li... | 268 |
"""simple docstring"""
import os
def snake_case ( ):
with open(os.path.dirname(A__ ) + "/grid.txt" ) as f:
UpperCAmelCase_ : Any = [] # noqa: E741
for _ in range(20 ):
l.append([int(A__ ) for x in f.readline().split()] )
UpperCAm... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''In... | 268 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = SwinConfig(image_size=1_92 )
if "base" in mod... | 268 | 1 |
"""simple docstring"""
from manim import *
class UpperCamelCase_ (__A ):
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[str]:
UpperCAmelCase_ : Any = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ : List[Any] ... | 268 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ):
if len(A__ ) <= 1:
return [tuple(A__ )]
UpperCAmelCase_ : Dict = []
def generate(A__ ,A__ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 ,A__ ... | 268 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 268 | 1 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
lowerCamelCase_ = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537... | 268 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ : Any = data
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ ... | 268 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCamelCase_ = loggi... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
if index == number_of_items:
return 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : Tuple = knapsack(A__ ,A__ ,A__ ,A__ ,index + 1 )
i... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ):
if not numbers:
return 0
if not isinstance(A__ ,(list, tuple) ) or not all(
isinstance(A__ ,A__ ) for number in numbers ):
raise ValueError("numbers must be an iterable of integers" )
UpperCAmelCase_ ... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
"""simple docstring"""
# Copyright 2022 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
#
... | 268 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 268 | 1 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class UpperCamelCase_ (__A ):
def __init__( self : List[str] , lowerCAmelCase_ : Optional[Any]="" , lowerCAmelCase_ : Optional[int]="train" ) -> ... | 268 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 1 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowerCamelCase_ = 2_9979_2458
# Symbols
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = symbols('''ct x y z''')
def snake_case (... | 268 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 268 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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... | 268 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''t5-small''': '''https://huggingface.co/t... | 268 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
... | 268 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 268 | 1 |
"""simple docstring"""
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 pathl... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase_ : Dict = (boundary[1] - boundary[0]) / steps
UpperCAmelCase_ : Optional[int] = boundary[0]
UpperCAmelCase_ : s... | 268 | 1 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class UpperCamelCase_ (tf.keras.layers.Layer ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCa... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case ( A__ ,A__ ,A__ ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list cannot be empty" )
UpperCAmelCase_ : Any = sum(
cash_flow / ((1... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0... | 268 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase_ = 3
def snake_case ( A__ ):
print("Generating primitive root of p" )
while True:
UpperCAmelCase_ : Dict = ... | 268 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 268 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_ava... | 268 |
"""simple docstring"""
from torch import nn
def snake_case ( A__ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {ac... | 268 | 1 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 268 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""... | 268 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE... | 268 |
"""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_availa... | 268 | 1 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', defau... | 268 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import V... | 268 | 1 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should ru... | 268 |
"""simple docstring"""
lowerCamelCase_ = [
(1000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def... | 268 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 268 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 268 | 1 |
"""simple docstring"""
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_do... | 268 |
"""simple docstring"""
import os
def snake_case ( ):
with open(os.path.dirname(A__ ) + "/grid.txt" ) as f:
UpperCAmelCase_ : Any = [] # noqa: E741
for _ in range(20 ):
l.append([int(A__ ) for x in f.readline().split()] )
UpperCAm... | 268 | 1 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
class UpperCamelCase_ (__A ):
__magic_name__ =... | 268 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = SwinConfig(image_size=1_92 )
if "base" in mod... | 268 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
lowerCamelCase_ = logging.get_logger(__name__)
... | 268 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV... | 268 | 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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 268 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 268 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''xlm-mlm-en-2048''':... | 268 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ : Any = data
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ ... | 268 | 1 |
"""simple docstring"""
def snake_case ( A__ = 10**9 ):
UpperCAmelCase_ : Optional[Any] = 1
UpperCAmelCase_ : Optional[Any] = 2
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Optional[int] ... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
if index == number_of_items:
return 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : Tuple = knapsack(A__ ,A__ ,A__ ,A__ ,index + 1 )
i... | 268 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def snake_case ( A__ ,A__ ):
UpperCAmelCase_ : int = iter(A__ )
while True:
UpperCAmelCase_ : Union[str, Any] = tuple(itertools.islice(A__ ,A__ ... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case ( A__ ):
return (data["data"... | 268 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 268 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''t5-small''': '''https://huggingface.co/t... | 268 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 1 |
"""simple docstring"""
import os
def snake_case ( A__ = "matrix.txt" ):
with open(os.path.join(os.path.dirname(A__ ) ,A__ ) ) as in_file:
UpperCAmelCase_ : Optional[Any] = in_file.read()
UpperCAmelCase_ : Optional[int] = [[int(A__ ) ... | 268 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( A__ ):
return len(set(A__ ) ) == len(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 268 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''t5-small''': '''https://huggingface.co/t... | 268 | 1 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( ):
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" ,type=A__ ,default="biencoder-nq-dev.json" ,help="Path to raw DPR tra... | 268 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 268 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_... | 268 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase_ : Dict = (boundary[1] - boundary[0]) / steps
UpperCAmelCase_ : Optional[int] = boundary[0]
UpperCAmelCase_ : s... | 268 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ (__A ):
__magic_name__ = '''WhisperFeatureExtractor'''
__magic_name__ = '''WhisperTokenizer'''
def __init__( self : Tuple , lowerCAmelCase_ : List[Any] , lowerCAm... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case ( A__ ,A__ ,A__ ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[... | 268 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0... | 268 | 1 |
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