
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


UpperCamelCase_   	=	logging.get_logger(__name__)

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  ['pixel_values']







							def __init__(						self  :  int      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[int, float] = 1 / 255      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  bool = True      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )
								__lowerCAmelCase         =							size if size is not None else {"height": 384, "width": 384}
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )

								__lowerCAmelCase         =							do_resize
								__lowerCAmelCase         =							size
								__lowerCAmelCase         =							resample
								__lowerCAmelCase         =							do_rescale
								__lowerCAmelCase         =							rescale_factor
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__lowerCAmelCase         =							image_std if image_std is not None else OPENAI_CLIP_STD
								__lowerCAmelCase         =							do_convert_rgb







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  str      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
								if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}" )
								__lowerCAmelCase         =							(size["height"], size["width"])
								return resize(snake_case__      ,				size=snake_case__      ,				resample=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[int, float]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  ImageInput      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Dict[str, int]] = None      ,				snake_case__  :  PILImageResampling = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[float] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				snake_case__  :  bool = None      ,				snake_case__  :  ChannelDimension = ChannelDimension.FIRST      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							do_resize if do_resize is not None else self.do_resize
								__lowerCAmelCase         =							resample if resample is not None else self.resample
								__lowerCAmelCase         =							do_rescale if do_rescale is not None else self.do_rescale
								__lowerCAmelCase         =							rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCAmelCase         =							do_normalize if do_normalize is not None else self.do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else self.image_mean
								__lowerCAmelCase         =							image_std if image_std is not None else self.image_std
								__lowerCAmelCase         =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__lowerCAmelCase         =							size if size is not None else self.size
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )

								__lowerCAmelCase         =							make_list_of_images(snake_case__ )

								if not valid_images(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 or resample is None:
									raise ValueError("Size and resample must be specified if do_resize 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." )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
									__lowerCAmelCase         =							[convert_to_rgb(snake_case__ ) for image in images]

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

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

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

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

								__lowerCAmelCase         =							[to_channel_dimension_format(snake_case__      ,				snake_case__ ) for image in images]

								__lowerCAmelCase         =							BatchFeature(data={"pixel_values": images}      ,				tensor_type=snake_case__ )

								return encoded_outputs








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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "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       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'mctct'







							def __init__(						self  :  Dict      ,				snake_case__  :  Any=8_065      ,				snake_case__  :  Tuple=1_536      ,				snake_case__  :  Union[str, Any]=36      ,				snake_case__  :  List[Any]=6_144      ,				snake_case__  :  Tuple=4      ,				snake_case__  :  List[str]=384      ,				snake_case__  :  List[Any]=920      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.3      ,				snake_case__  :  List[str]="relu"      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=0.3      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  List[Any]=1      ,				snake_case__  :  Optional[int]=0      ,				snake_case__  :  Any=2      ,				snake_case__  :  Any=1      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  Tuple=1      ,				snake_case__  :  Union[str, Any]=(7,)      ,				snake_case__  :  List[Any]=(3,)      ,				snake_case__  :  List[Any]=80      ,				snake_case__  :  Optional[int]=1      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int="sum"      ,				snake_case__  :  Dict=False      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







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

								# prevents config testing fail with exporting to json
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(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}`." )






