

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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


A_		:			Dict										=   logging.get_logger(__name__)


if is_vision_available():
       import PIL
class 					A_   (      _a						):


    '''simple docstring'''






    a__     							=       ["pixel_values"]


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

        __UpperCAmelCase									=			size if size is not None else {'''height''': 2_560, '''width''': 1_920}
        if isinstance(lowercase__     ,      (tuple, list)  ):
            # The previous feature extractor size parameter was in (width, height) format
            __UpperCAmelCase									=			size[::-1]
        __UpperCAmelCase									=			get_size_dict(lowercase__  )

        __UpperCAmelCase									=			do_resize
        __UpperCAmelCase									=			size
        __UpperCAmelCase									=			resample
        __UpperCAmelCase									=			do_thumbnail
        __UpperCAmelCase									=			do_align_long_axis
        __UpperCAmelCase									=			do_pad
        __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      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None  )     ->      np.ndarray:
        __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )
        __UpperCAmelCase					,     __UpperCAmelCase									=			size['''height'''], size['''width''']

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
            __UpperCAmelCase									=			np.rotaa(lowercase__     ,      3  )

        if data_format is not None:
            __UpperCAmelCase									=			to_channel_dimension_format(lowercase__     ,      lowercase__  )

        return image


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = False     ,      lowercase__ = None     ,      )     ->      np.ndarray:
        __UpperCAmelCase					,     __UpperCAmelCase									=			size['''height'''], size['''width''']
        __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )

        __UpperCAmelCase									=			output_width - input_width
        __UpperCAmelCase									=			output_height - input_height

        if random_padding:
            __UpperCAmelCase									=			np.random.randint(low=0     ,      high=delta_height + 1  )
            __UpperCAmelCase									=			np.random.randint(low=0     ,      high=delta_width + 1  )
        else:
            __UpperCAmelCase									=			delta_height // 2
            __UpperCAmelCase									=			delta_width // 2

        __UpperCAmelCase									=			delta_height - pad_top
        __UpperCAmelCase									=			delta_width - pad_left

        __UpperCAmelCase									=			((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(lowercase__     ,      lowercase__     ,      data_format=lowercase__  )


    def      lowerCAmelCase_						(self     ,      *lowercase__     ,      **lowercase__  )     ->      List[Any]:
        logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''  )
        return self.pad_image(*lowercase__     ,      **lowercase__  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = PILImageResampling.BICUBIC     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      np.ndarray:
        __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )
        __UpperCAmelCase					,     __UpperCAmelCase									=			size['''height'''], size['''width''']

        # We always resize to the smallest of either the input or output size.
        __UpperCAmelCase									=			min(lowercase__     ,      lowercase__  )
        __UpperCAmelCase									=			min(lowercase__     ,      lowercase__  )

        if height == input_height and width == input_width:
            return image

        if input_height > input_width:
            __UpperCAmelCase									=			int(input_width * height / input_height  )
        elif input_width > input_height:
            __UpperCAmelCase									=			int(input_height * width / input_width  )

        return resize(
            lowercase__     ,      size=(height, width)     ,      resample=lowercase__     ,      reducing_gap=2.0     ,      data_format=lowercase__     ,      **lowercase__  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = PILImageResampling.BICUBIC     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      np.ndarray:
        __UpperCAmelCase									=			get_size_dict(lowercase__  )
        __UpperCAmelCase									=			min(size['''height''']     ,      size['''width''']  )
        __UpperCAmelCase									=			get_resize_output_image_size(lowercase__     ,      size=lowercase__     ,      default_to_square=lowercase__  )
        __UpperCAmelCase									=			resize(lowercase__     ,      size=lowercase__     ,      resample=lowercase__     ,      data_format=lowercase__     ,      **lowercase__  )
        return resized_image


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      List[str]:
        return rescale(lowercase__     ,      scale=lowercase__     ,      data_format=lowercase__     ,      **lowercase__  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      np.ndarray:
        return normalize(lowercase__     ,      mean=lowercase__     ,      std=lowercase__     ,      data_format=lowercase__     ,      **lowercase__  )






    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = False     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = ChannelDimension.FIRST     ,      **lowercase__     ,      )     ->      PIL.Image.Image:
        __UpperCAmelCase									=			do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase									=			size if size is not None else self.size
        if isinstance(lowercase__     ,      (tuple, list)  ):
            # Previous feature extractor had size in (width, height) format
            __UpperCAmelCase									=			size[::-1]
        __UpperCAmelCase									=			get_size_dict(lowercase__  )
        __UpperCAmelCase									=			resample if resample is not None else self.resample
        __UpperCAmelCase									=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        __UpperCAmelCase									=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        __UpperCAmelCase									=			do_pad if do_pad is not None else self.do_pad
        __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(lowercase__  )

        if not valid_images(lowercase__  ):
            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_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

        if do_pad and size is None:
            raise ValueError('''Size must be specified if do_pad 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(lowercase__  ) for image in images]

        if do_align_long_axis:
            __UpperCAmelCase									=			[self.align_long_axis(lowercase__     ,      size=lowercase__  ) for image in images]

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

        if do_thumbnail:
            __UpperCAmelCase									=			[self.thumbnail(image=lowercase__     ,      size=lowercase__  ) for image in images]

        if do_pad:
            __UpperCAmelCase									=			[self.pad_image(image=lowercase__     ,      size=lowercase__     ,      random_padding=lowercase__  ) for image in images]

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

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

        __UpperCAmelCase									=			[to_channel_dimension_format(lowercase__     ,      lowercase__  ) for image in images]

        __UpperCAmelCase									=			{'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,      tensor_type=lowercase__  )








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


A_		:			int										=   logging.get_logger(__name__)
class 					A_   (      _a						):


    '''simple docstring'''






    a__     							=       "speech-encoder-decoder"
    a__     							=       True


    def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
        super().__init__(**lowercase__  )
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )

        __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
        __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
        __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
        __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )

        __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
        __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
        __UpperCAmelCase									=			True


    @classmethod
    def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
        logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
        __UpperCAmelCase									=			True
        __UpperCAmelCase									=			True

        return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )






    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
        __UpperCAmelCase									=			self.encoder.to_dict()
        __UpperCAmelCase									=			self.decoder.to_dict()
        __UpperCAmelCase									=			self.__class__.model_type
        return output




