
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__							:							Union[str, Any]									=			logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  ['pixel_values']



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = 1 / 255     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   None:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			size if size is not None else {'''shortest_edge''': 256}
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			do_resize
													__UpperCamelCase       =			size
													__UpperCamelCase       =			resample
													__UpperCamelCase       =			do_center_crop
													__UpperCamelCase       =			crop_size
													__UpperCamelCase       =			do_rescale
													__UpperCamelCase       =			rescale_factor
													__UpperCamelCase       =			do_normalize
													__UpperCamelCase       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__UpperCamelCase       =			image_std if image_std is not None else IMAGENET_STANDARD_STD



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													if "shortest_edge" not in size:
																			raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
													__UpperCamelCase       =			get_resize_output_image_size(SCREAMING_SNAKE_CASE_     ,	size=size['''shortest_edge''']     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													return resize(SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_     ,	resample=SCREAMING_SNAKE_CASE_     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													return center_crop(SCREAMING_SNAKE_CASE_     ,	size=(size['''height'''], size['''width'''])     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_			)->   np.ndarray:





													'''simple docstring'''






													return rescale(SCREAMING_SNAKE_CASE_     ,	scale=SCREAMING_SNAKE_CASE_     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






													return normalize(SCREAMING_SNAKE_CASE_     ,	mean=SCREAMING_SNAKE_CASE_     ,	std=SCREAMING_SNAKE_CASE_     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			do_resize if do_resize is not None else self.do_resize
													__UpperCamelCase       =			size if size is not None else self.size
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			resample if resample is not None else self.resample
													__UpperCamelCase       =			do_center_crop if do_center_crop is not None else self.do_center_crop
													__UpperCamelCase       =			crop_size if crop_size is not None else self.crop_size
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			do_rescale if do_rescale is not None else self.do_rescale
													__UpperCamelCase       =			rescale_factor if rescale_factor is not None else self.rescale_factor
													__UpperCamelCase       =			do_normalize if do_normalize is not None else self.do_normalize
													__UpperCamelCase       =			image_mean if image_mean is not None else self.image_mean
													__UpperCamelCase       =			image_std if image_std is not None else self.image_std

													__UpperCamelCase       =			make_list_of_images(SCREAMING_SNAKE_CASE_			)

													if not valid_images(SCREAMING_SNAKE_CASE_			):
																			raise ValueError(
																			    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																			    '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

													if do_resize and size is None:
																			raise ValueError('''Size must be specified if do_resize is True.'''			)

													if do_center_crop and crop_size is None:
																			raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

													if do_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

													# All transformations expect numpy arrays.
													__UpperCamelCase       =			[to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

													if do_resize:
																			__UpperCamelCase       =			[self.resize(image=SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_     ,	resample=SCREAMING_SNAKE_CASE_			) for image in images]

													if do_center_crop:
																			__UpperCamelCase       =			[self.center_crop(image=SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_			) for image in images]

													if do_rescale:
																			__UpperCamelCase       =			[self.rescale(image=SCREAMING_SNAKE_CASE_     ,	scale=SCREAMING_SNAKE_CASE_			) for image in images]

													if do_normalize:
																			__UpperCamelCase       =			[self.normalize(image=SCREAMING_SNAKE_CASE_     ,	mean=SCREAMING_SNAKE_CASE_     ,	std=SCREAMING_SNAKE_CASE_			) for image in images]

													__UpperCamelCase       =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) for image in images]

													__UpperCamelCase       =			{'''pixel_values''': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,	tensor_type=SCREAMING_SNAKE_CASE_			)






from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							Union[str, Any]									=			{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'mctct'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=8065     ,	SCREAMING_SNAKE_CASE_=1536     ,	SCREAMING_SNAKE_CASE_=36     ,	SCREAMING_SNAKE_CASE_=6144     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=384     ,	SCREAMING_SNAKE_CASE_=920     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=(7,)     ,	SCREAMING_SNAKE_CASE_=(3,)     ,	SCREAMING_SNAKE_CASE_=80     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="sum"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_     ,	pad_token_id=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			attention_head_dim
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			layer_norm_eps
													__UpperCamelCase       =			layerdrop
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			pad_token_id
													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id
													__UpperCamelCase       =			conv_glu_dim
													__UpperCamelCase       =			conv_dropout
													__UpperCamelCase       =			num_conv_layers
													__UpperCamelCase       =			input_feat_per_channel
													__UpperCamelCase       =			input_channels
													__UpperCamelCase       =			conv_channels
													__UpperCamelCase       =			ctc_loss_reduction
													__UpperCamelCase       =			ctc_zero_infinity

													# prevents config testing fail with exporting to json
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)

													if len(self.conv_kernel			) != self.num_conv_layers:
																			raise ValueError(
																			    '''Configuration for convolutional module is incorrect. '''
																			    '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
																			    F"but is `len(config.conv_kernel) = {len(self.conv_kernel			)}`, "
																			    F"`config.num_conv_layers = {self.num_conv_layers}`."			)




