from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					=				{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__					=				['PegasusTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__					=				['PegasusTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__					=				[
        'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'PegasusForCausalLM',
        'PegasusForConditionalGeneration',
        'PegasusModel',
        'PegasusPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__					=				[
        'TFPegasusForConditionalGeneration',
        'TFPegasusModel',
        'TFPegasusPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__					=				[
        'FlaxPegasusForConditionalGeneration',
        'FlaxPegasusModel',
        'FlaxPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_pegasus import PegasusTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_pegasus_fast import PegasusTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_pegasus import (
            PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            PegasusForCausalLM,
            PegasusForConditionalGeneration,
            PegasusModel,
            PegasusPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_pegasus import (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
            FlaxPegasusPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__					=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      1
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

        lowerCAmelCase__  			=      torch.rand((batch_size, num_channels, height, width))

        return sample




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Any):







        '''simple docstring'''



        if isinstance(image[0]    ,					PIL.Image.Image):
               return np.stack([np.array(lowercase__) for i in image]    ,					axis=0)
        elif isinstance(lowercase__    ,					torch.Tensor):
               return image.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        return image




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      self.to_np(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(
                   image_processor.preprocess(lowercase__)    ,					output_type=lowercase__    ,					)
               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)
        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)

               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        lowerCAmelCase__  			=      image_processor.numpy_to_pil(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)
               for i, o in zip(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      np.array(lowercase__)
                      lowerCAmelCase__  			=      self.to_np(lowercase__) if output_type == 'pil' else (self.to_np(lowercase__) * 255).round()
                      assert (
                          np.abs(in_np - out_np).max() < 1e-6
                      ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      input_pt_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        lowerCAmelCase__  			=      input_np_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_ad).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad).max() < 1e-6




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_list).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list).max() < 1e-6
