




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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import SwinaSRImageProcessor
       from transformers.image_transforms import get_image_size




class 		UpperCamelCase__     (unittest.TestCase					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__=7					,  UpperCamelCase__=3					,  UpperCamelCase__=18					,  UpperCamelCase__=30					,  UpperCamelCase__=400					,  UpperCamelCase__=True					,  UpperCamelCase__=1 / 255					,  UpperCamelCase__=True					,  UpperCamelCase__=8					,  )					->				List[str]:
           lowerCamelCase       :				Any      			= parent
           lowerCamelCase       :				Dict      			= batch_size
           lowerCamelCase       :				str      			= num_channels
           lowerCamelCase       :				List[str]      			= image_size
           lowerCamelCase       :				Optional[Any]      			= min_resolution
           lowerCamelCase       :				Dict      			= max_resolution
           lowerCamelCase       :				Tuple      			= do_rescale
           lowerCamelCase       :				int      			= rescale_factor
           lowerCamelCase       :				Union[str, Any]      			= do_pad
           lowerCamelCase       :				Optional[Any]      			= pad_size







    def    _lowercase					(   self					)					->				Any:
           return {
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
               "pad_size": self.pad_size,
           }







    def    _lowercase					(   self					,  UpperCamelCase__=False					,  UpperCamelCase__=False					,  UpperCamelCase__=False					)					->				int:
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                  lowerCamelCase       :				Tuple      			= []
                  for i in range(self.batch_size					):
                         image_inputs.append(
                             np.random.randint(
                                 255					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta					)					)
           else:
                  lowerCamelCase       :				Tuple      			= []
                  for i in range(self.batch_size					):
                         lowerCamelCase       :				List[Any]      			= np.random.choice(np.arange(self.min_resolution					,  self.max_resolution					)					,  2					)
                         image_inputs.append(np.random.randint(255					,  size=(self.num_channels, width, height)					,  dtype=np.uinta					)					)

           if not numpify and not torchify:
                  # PIL expects the channel dimension as last dimension
                  lowerCamelCase       :				Optional[int]      			= [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1					)					) for x in image_inputs]

           if torchify:
                  lowerCamelCase       :				Union[str, Any]      			= [torch.from_numpy(UpperCamelCase__					) for x in image_inputs]

           return image_inputs




@require_torch
@require_vision
class 		UpperCamelCase__     (lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Tuple     					=				SwinaSRImageProcessor if is_vision_available() else None







    def    _lowercase					(   self					)					->				Optional[Any]:
           lowerCamelCase       :				Union[str, Any]      			= SwinaSRImageProcessingTester(self					)







    @property
    def    _lowercase					(   self					)					->				List[str]:
           return self.image_processor_tester.prepare_image_processor_dict()







    def    _lowercase					(   self					)					->				Any:
           lowerCamelCase       :				List[str]      			= self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_rescale"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "rescale_factor"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_pad"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "pad_size"					)					)







    def    _lowercase					(   self					)					->				Optional[int]:
           pass







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				int:
           lowerCamelCase       :				Any      			= get_image_size(UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self.image_processor_tester.pad_size

           lowerCamelCase       :				Dict      			= (old_height // size + 1) * size - old_height
           lowerCamelCase       :				int      			= (old_width // size + 1) * size - old_width
           return old_height + pad_height, old_width + pad_width







    def    _lowercase					(   self					)					->				Any:
           # Initialize image_processor
           lowerCamelCase       :				Tuple      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           lowerCamelCase       :				str      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  Image.Image					)

           # Test not batched input
           lowerCamelCase       :				int      			= image_processor(image_inputs[0]					,  return_tensors="pt"					).pixel_values
           lowerCamelCase       :				List[str]      			= self.calculate_expected_size(np.array(image_inputs[0]					)					)
           self.assertEqual(
               encoded_images.shape					,  (
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )







    def    _lowercase					(   self					)					->				Any:
           # Initialize image_processor
           lowerCamelCase       :				List[Any]      			= self.image_processing_class(**self.image_processor_dict					)
           # create random numpy tensors
           lowerCamelCase       :				Optional[int]      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  np.ndarray					)

           # Test not batched input
           lowerCamelCase       :				str      			= image_processor(image_inputs[0]					,  return_tensors="pt"					).pixel_values
           lowerCamelCase       :				int      			= self.calculate_expected_size(image_inputs[0]					)
           self.assertEqual(
               encoded_images.shape					,  (
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )







    def    _lowercase					(   self					)					->				List[str]:
           # Initialize image_processor
           lowerCamelCase       :				List[str]      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PyTorch tensors
           lowerCamelCase       :				str      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  torch.Tensor					)

           # Test not batched input
           lowerCamelCase       :				Optional[int]      			= image_processor(image_inputs[0]					,  return_tensors="pt"					).pixel_values
           lowerCamelCase       :				Union[str, Any]      			= self.calculate_expected_size(image_inputs[0]					)
           self.assertEqual(
               encoded_images.shape					,  (
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )












import copy

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


SCREAMING_SNAKE_CASE__	:				int  =       logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""speech-encoder-decoder"""
    lowerCamelCase_				:     List[str]     					=				True







    def __init__(   self					,  **UpperCamelCase__					)					->				Dict:
           super().__init__(**UpperCamelCase__					)
           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}'''					)

           lowerCamelCase       :				Dict      			= kwargs.pop("encoder"					)
           lowerCamelCase       :				Tuple      			= encoder_config.pop("model_type"					)
           lowerCamelCase       :				int      			= kwargs.pop("decoder"					)
           lowerCamelCase       :				List[Any]      			= decoder_config.pop("model_type"					)

           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= True







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				PretrainedConfig:
           logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
           lowerCamelCase       :				Dict      			= True
           lowerCamelCase       :				Union[str, Any]      			= True

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







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Tuple      			= copy.deepcopy(self.__dict__					)
           lowerCamelCase       :				Optional[int]      			= self.encoder.to_dict()
           lowerCamelCase       :				List[str]      			= self.decoder.to_dict()
           lowerCamelCase       :				Dict      			= self.__class__.model_type
           return output






