



'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A__    :   Dict           =					logging.get_logger(__name__)



class  snake_case__						(    lowercase__				):
 A__				= ['''pixel_values''']
 def __init__(   self						:							List[Any]      ,     __a						:							bool = True      ,     __a						:							Union[int, float] = 1 / 255      ,     __a						:							bool = True      ,     __a						:							int = 8      ,     **__a						:							str      ,     )      ->   List[str]:







       '''simple docstring'''
       super().__init__(**UpperCAmelCase__  )

       __snake_case :   Union[str, Any]													=    do_rescale
       __snake_case :   List[Any]													=    rescale_factor
       __snake_case :   Tuple													=    do_pad
       __snake_case :   List[str]													=    pad_size
 def A_   (   self						:							List[str]      ,     __a						:							np.ndarray      ,     __a						:							float      ,     __a						:							Optional[Union[str, ChannelDimension]] = None      ,     **__a						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       return rescale(UpperCAmelCase__      ,     scale=UpperCAmelCase__      ,     data_format=UpperCAmelCase__      ,     **UpperCAmelCase__  )
 def A_   (   self						:							Tuple      ,     __a						:							np.ndarray      ,     __a						:							int      ,     __a						:							Optional[Union[str, ChannelDimension]] = None  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    get_image_size(UpperCAmelCase__  )
       __snake_case :   Tuple													=    (old_height // size + 1) * size - old_height
       __snake_case :   Tuple													=    (old_width // size + 1) * size - old_width

       return pad(UpperCAmelCase__      ,     ((0, pad_height), (0, pad_width))      ,     mode='symmetric'      ,     data_format=UpperCAmelCase__  )





 def A_   (   self						:							List[str]      ,     __a						:							ImageInput      ,     __a						:							Optional[bool] = None      ,     __a						:							Optional[float] = None      ,     __a						:							Optional[bool] = None      ,     __a						:							Optional[int] = None      ,     __a						:							Optional[Union[str, TensorType]] = None      ,     __a						:							Union[str, ChannelDimension] = ChannelDimension.FIRST      ,     **__a						:							Optional[Any]      ,     )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   int													=    do_rescale if do_rescale is not None else self.do_rescale
       __snake_case :   Tuple													=    rescale_factor if rescale_factor is not None else self.rescale_factor
       __snake_case :   int													=    do_pad if do_pad is not None else self.do_pad
       __snake_case :   List[Any]													=    pad_size if pad_size is not None else self.pad_size

       __snake_case :   Any													=    make_list_of_images(UpperCAmelCase__  )

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

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

       # All transformations expect numpy arrays.
       __snake_case :   Dict													=    [to_numpy_array(UpperCAmelCase__  ) for image in images]

       if do_rescale:
             __snake_case :   Tuple													=    [self.rescale(image=UpperCAmelCase__      ,     scale=UpperCAmelCase__  ) for image in images]

       if do_pad:
             __snake_case :   Union[str, Any]													=    [self.pad(UpperCAmelCase__      ,     size=UpperCAmelCase__  ) for image in images]

       __snake_case :   Dict													=    [to_channel_dimension_format(UpperCAmelCase__      ,     UpperCAmelCase__  ) for image in images]

       __snake_case :   Any													=    {'''pixel_values''': images}
       return BatchFeature(data=UpperCAmelCase__      ,     tensor_type=UpperCAmelCase__  )






'''simple docstring'''





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


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'speech_to_text_2'
				lowerCamelCase_		       =    ['past_key_values']
				lowerCamelCase_		       =    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Dict=10000		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		str=2048		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[Any]="relu"		,					UpperCAmelCase__     :		List[str]=256		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		List[Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		str=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		List[Any]=1024		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[int]    		=d_model
										lowercase :							Optional[Any]    		=decoder_ffn_dim
										lowercase :							Any    		=decoder_layers
										lowercase :							Dict    		=decoder_attention_heads
										lowercase :							List[Any]    		=dropout
										lowercase :							List[Any]    		=attention_dropout
										lowercase :							Any    		=activation_dropout
										lowercase :							Optional[Any]    		=activation_function
										lowercase :							Optional[int]    		=init_std
										lowercase :							Dict    		=decoder_layerdrop
										lowercase :							Optional[int]    		=use_cache
										lowercase :							Optional[Any]    		=decoder_layers
										lowercase :							List[str]    		=scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase :							str    		=max_target_positions

										super().__init__(
										    pad_token_id=UpperCAmelCase__		,					bos_token_id=UpperCAmelCase__		,					eos_token_id=UpperCAmelCase__		,					decoder_start_token_id=UpperCAmelCase__		,					**UpperCAmelCase__		,					)





