






'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor

class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      def __init__( self		:       List[str]		,      A		:       Union[str, Any]		,      A		:       Any=7		,      A		:       Any=3		,      A		:       Tuple=18		,      A		:       Optional[int]=30		,      A		:       Dict=400		,      A		:       Optional[int]=True		,      A		:       Dict=None		,      A		:       Dict=2		,      A		:       Dict=True		,      A		:       Dict=[0.5, 0.5, 0.5]		,      A		:       Any=[0.5, 0.5, 0.5]		,      ):
             _UpperCAmelCase  :						Dict							   =					size if size is not None else {"shortest_edge": 30}
             _UpperCAmelCase  :						Any							   =					parent
             _UpperCAmelCase  :						str							   =					batch_size
             _UpperCAmelCase  :						Dict							   =					num_channels
             _UpperCAmelCase  :						List[str]							   =					image_size
             _UpperCAmelCase  :						Optional[Any]							   =					min_resolution
             _UpperCAmelCase  :						Optional[Any]							   =					max_resolution
             _UpperCAmelCase  :						Optional[Any]							   =					do_resize
             _UpperCAmelCase  :						int							   =					size
             _UpperCAmelCase  :						str							   =					size_divisor
             _UpperCAmelCase  :						Union[str, Any]							   =					do_normalize
             _UpperCAmelCase  :						str							   =					image_mean
             _UpperCAmelCase  :						Union[str, Any]							   =					image_std




      def   _A			( self		:       Optional[int] ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "size_divisor": self.size_divisor,
             }





      def   _A			( self		:       List[str]		,      A		:       Optional[Any]		,      A		:       Tuple=False ):
             if not batched:
                    _UpperCAmelCase  :						Optional[Any]							   =					self.size["shortest_edge"]
                    _UpperCAmelCase  :						Dict							   =					image_inputs[0]
                    if isinstance(A		,      Image.Image ):
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					image.size
                    else:
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						List[Any]							   =					image.shape[1], image.shape[2]
                    _UpperCAmelCase  :						List[Any]							   =					size / min(A		,      A )
                    if h < w:
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						Tuple							   =					size, scale * w
                    else:
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					scale * h, size

                    _UpperCAmelCase  :						Dict							   =					int((1333 / 800) * size )
                    if max(A		,      A ) > max_size:
                           _UpperCAmelCase  :						int							   =					max_size / max(A		,      A )
                           _UpperCAmelCase  :						Dict							   =					newh * scale
                           _UpperCAmelCase  :						int							   =					neww * scale

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[Any]							   =					int(newh + 0.5 ), int(neww + 0.5 )
                    _UpperCAmelCase   ,						_UpperCAmelCase  :						Dict							   =					(
                        newh // self.size_divisor * self.size_divisor,
                        neww // self.size_divisor * self.size_divisor,
                    )

             else:
                    _UpperCAmelCase  :						List[Any]							   =					[]
                    for image in image_inputs:
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					self.get_expected_values([image] )
                           expected_values.append((expected_height, expected_width) )
                    _UpperCAmelCase  :						Optional[int]							   =					max(A		,      key=lambda A : item[0] )[0]
                    _UpperCAmelCase  :						Tuple							   =					max(A		,      key=lambda A : item[1] )[1]

             return expected_height, expected_width

@require_torch
@require_vision
class 			lowerCamelCase_			(_a							,				unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		ViltImageProcessor if is_vision_available() else None




      def   _A			( self		:       Optional[int] ):
             _UpperCAmelCase  :						Union[str, Any]							   =					ViltImageProcessingTester(self )




      @property
      def   _A			( self		:       List[str] ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[Any]							   =					self.image_processing_class(**self.image_processor_dict )
             self.assertTrue(hasattr(A		,      "image_mean" ) )
             self.assertTrue(hasattr(A		,      "image_std" ) )
             self.assertTrue(hasattr(A		,      "do_normalize" ) )
             self.assertTrue(hasattr(A		,      "do_resize" ) )
             self.assertTrue(hasattr(A		,      "size" ) )
             self.assertTrue(hasattr(A		,      "size_divisor" ) )




      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						List[str]							   =					self.image_processing_class.from_dict(self.image_processor_dict )
             self.assertEqual(image_processor.size		,      {"shortest_edge": 30} )

             _UpperCAmelCase  :						Dict							   =					self.image_processing_class.from_dict(self.image_processor_dict		,      size=42 )
             self.assertEqual(image_processor.size		,      {"shortest_edge": 42} )




      def   _A			( self		:       Any ):
             pass




      def   _A			( self		:       Optional[Any] ):
             # Initialize image_processing
             _UpperCAmelCase  :						Optional[Any]							   =					self.image_processing_class(**self.image_processor_dict )
             # create random PIL images
             _UpperCAmelCase  :						Any							   =					prepare_image_inputs(self.image_processor_tester		,      equal_resolution=A )
             for image in image_inputs:
                    self.assertIsInstance(A		,      Image.Image )

             # Test not batched input
             _UpperCAmelCase  :						str							   =					image_processing(image_inputs[0]		,      return_tensors="pt" ).pixel_values

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Optional[int]							   =					self.image_processor_tester.get_expected_values(A )
             self.assertEqual(
                 encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

             # Test batched
             _UpperCAmelCase  :						Any							   =					image_processing(A		,      return_tensors="pt" ).pixel_values

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					self.image_processor_tester.get_expected_values(A		,      batched=A )
             self.assertEqual(
                 encoded_images.shape		,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )		,      )




      def   _A			( self		:       Any ):
             # Initialize image_processing
             _UpperCAmelCase  :						Union[str, Any]							   =					self.image_processing_class(**self.image_processor_dict )
             # create random numpy tensors
             _UpperCAmelCase  :						Optional[Any]							   =					prepare_image_inputs(self.image_processor_tester		,      equal_resolution=A		,      numpify=A )
             for image in image_inputs:
                    self.assertIsInstance(A		,      np.ndarray )

             # Test not batched input
             _UpperCAmelCase  :						str							   =					image_processing(image_inputs[0]		,      return_tensors="pt" ).pixel_values

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					self.image_processor_tester.get_expected_values(A )
             self.assertEqual(
                 encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

             # Test batched
             _UpperCAmelCase  :						List[str]							   =					image_processing(A		,      return_tensors="pt" ).pixel_values

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					self.image_processor_tester.get_expected_values(A		,      batched=A )
             self.assertEqual(
                 encoded_images.shape		,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )		,      )





      def   _A			( self		:       Tuple ):
             # Initialize image_processing
             _UpperCAmelCase  :						str							   =					self.image_processing_class(**self.image_processor_dict )
             # create random PyTorch tensors
             _UpperCAmelCase  :						Union[str, Any]							   =					prepare_image_inputs(self.image_processor_tester		,      equal_resolution=A		,      torchify=A )
             for image in image_inputs:
                    self.assertIsInstance(A		,      torch.Tensor )

             # Test not batched input
             _UpperCAmelCase  :						Union[str, Any]							   =					image_processing(image_inputs[0]		,      return_tensors="pt" ).pixel_values

             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					self.image_processor_tester.get_expected_values(A )
             self.assertEqual(
                 encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

             # Test batched
             _UpperCAmelCase  :						Tuple							   =					image_processing(A		,      return_tensors="pt" ).pixel_values

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					self.image_processor_tester.get_expected_values(A		,      batched=A )
             self.assertEqual(
                 encoded_images.shape		,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )		,      )


'''simple docstring'''




import copy

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


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"speech-encoder-decoder"
      __UpperCamelCase:						Optional[int]      			=		True




      def __init__( self		:       Optional[int]		,      **A		:       Tuple ):
             super().__init__(**A )
             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  :						Dict							   =					kwargs.pop("encoder" )
             _UpperCAmelCase  :						Any							   =					encoder_config.pop("model_type" )
             _UpperCAmelCase  :						str							   =					kwargs.pop("decoder" )
             _UpperCAmelCase  :						int							   =					decoder_config.pop("model_type" )

             _UpperCAmelCase  :						List[Any]							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						str							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						Optional[int]							   =					True




      @classmethod
      def   _A			( cls		:       int		,      A		:       PretrainedConfig		,      A		:       PretrainedConfig		,      **A		:       Dict ):
             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             _UpperCAmelCase  :						Any							   =					True
             _UpperCAmelCase  :						Union[str, Any]							   =					True

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





      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
             _UpperCAmelCase  :						Any							   =					self.encoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.decoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.__class__.model_type
             return output

