
'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowercase    =		logging.get_logger(__name__)




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       ['''pixel_values''']




						def __init__( self   ,       _lowercase = True   ,       _lowercase = 32   ,       _lowercase=PILImageResampling.BILINEAR   ,       _lowercase = True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			do_resize
												_lowerCAmelCase  						=			do_rescale
												_lowerCAmelCase  						=			size_divisor
												_lowerCAmelCase  						=			resample
												super().__init__(**_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			get_image_size(_lowercase				)
												# Rounds the height and width down to the closest multiple of size_divisor
												_lowerCAmelCase  						=			height // size_divisor * size_divisor
												_lowerCAmelCase  						=			width // size_divisor * size_divisor
												_lowerCAmelCase  						=			resize(_lowercase   ,       (new_h, new_w)   ,       resample=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)
												return image




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase				):

												"""simple docstring"""



												return rescale(image=_lowercase   ,       scale=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase=None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = ChannelDimension.FIRST   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			do_resize if do_resize is not None else self.do_resize
												_lowerCAmelCase  						=			do_rescale if do_rescale is not None else self.do_rescale
												_lowerCAmelCase  						=			size_divisor if size_divisor is not None else self.size_divisor
												_lowerCAmelCase  						=			resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																		raise ValueError("""size_divisor is required for resizing"""				)

												_lowerCAmelCase  						=			make_list_of_images(_lowercase				)

												if not valid_images(_lowercase				):
																		raise ValueError("""Invalid image(s)"""				)

												# All transformations expect numpy arrays.
												_lowerCAmelCase  						=			[to_numpy_array(_lowercase				) for img in images]

												if do_resize:
																		_lowerCAmelCase  						=			[self.resize(_lowercase   ,       size_divisor=_lowercase   ,       resample=_lowercase				) for image in images]

												if do_rescale:
																		_lowerCAmelCase  						=			[self.rescale(_lowercase   ,       scale=1 / 255				) for image in images]

												_lowerCAmelCase  						=			[to_channel_dimension_format(_lowercase   ,       _lowercase				) for image in images]

												_lowerCAmelCase  						=			{"""pixel_values""": images}
												return BatchFeature(data=_lowercase   ,       tensor_type=_lowercase				)






'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    =		logging.get_logger(__name__)

_lowercase    =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       '''convbert'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=768   ,       _lowercase=12   ,       _lowercase=12   ,       _lowercase=3_072   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=1   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=768   ,       _lowercase=2   ,       _lowercase=9   ,       _lowercase=1   ,       _lowercase=None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(
												    pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase   ,       )

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			embedding_size
												_lowerCAmelCase  						=			head_ratio
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			num_groups
												_lowerCAmelCase  						=			classifier_dropout






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												if self.task == "multiple-choice":
																		_lowerCAmelCase  						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		_lowerCAmelCase  						=			{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]				)
