'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





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


         """simple docstring"""







         a					:							Any							= 1
         a					:							int							= 3
         a					:							Union[str, Any]							= 8
         a					:							Dict							= 8

         a					:							Dict							= torch.rand((batch_size, num_channels, height, width))

         return sample





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						Any):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a					:							Dict							= VaeImageProcessor(do_resize=lowerCamelCase_							,							do_normalize=lowerCamelCase_)

         a					:							Union[str, Any]							= self.dummy_sample
         a					:							Optional[int]							= self.to_np(lowerCamelCase_)

         for output_type in ["pt", "np", "pil"]:
              a					:							List[str]							= image_processor.postprocess(
                  image_processor.preprocess(lowerCamelCase_)							,							output_type=lowerCamelCase_							,							)
              a					:							Optional[int]							= self.to_np(lowerCamelCase_)
              a					:							List[Any]							= (input_np * 2_5_5).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    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







         a					:							Any							= VaeImageProcessor(do_resize=lowerCamelCase_							,							do_normalize=lowerCamelCase_)
         a					:							Tuple							= self.dummy_sample.cpu().numpy().transpose(0							,							2							,							3							,							1)

         for output_type in ["pt", "np", "pil"]:
              a					:							List[str]							= image_processor.postprocess(image_processor.preprocess(lowerCamelCase_)							,							output_type=lowerCamelCase_)

              a					:							Union[str, Any]							= self.to_np(lowerCamelCase_)
              a					:							Dict							= (input_np * 2_5_5).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    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							Union[str, Any]							= VaeImageProcessor(do_resize=lowerCamelCase_							,							do_normalize=lowerCamelCase_)

         a					:							Tuple							= self.dummy_sample.cpu().numpy().transpose(0							,							2							,							3							,							1)
         a					:							List[str]							= image_processor.numpy_to_pil(lowerCamelCase_)

         for output_type in ["pt", "np", "pil"]:
              a					:							Tuple							= image_processor.postprocess(image_processor.preprocess(lowerCamelCase_)							,							output_type=lowerCamelCase_)
              for i, o in zip(lowerCamelCase_							,							lowerCamelCase_):
                   a					:							List[Any]							= np.array(lowerCamelCase_)
                   a					:							Optional[int]							= self.to_np(lowerCamelCase_) if output_type == 'pil' else (self.to_np(lowerCamelCase_) * 2_5_5).round()
                   assert (
                       np.abs(in_np - out_np).max() < 1e-6
                   ), f"""decoded output does not match input for output_type {output_type}"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         a					:							List[str]							= VaeImageProcessor(do_resize=lowerCamelCase_							,							do_normalize=lowerCamelCase_)

         a					:							Optional[Any]							= self.dummy_sample
         a					:							Union[str, Any]							= input_pt_ad.squeeze(0)

         a					:							List[str]							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							output_type='np'							,							)
         a					:							Tuple							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							output_type='np'							,							)

         a					:							Optional[Any]							= self.to_np(self.dummy_sample)
         a					:							Union[str, Any]							= input_np_ad.squeeze(0)

         a					:							Dict							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							output_type='np'							,							)
         a					:							List[str]							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							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    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							List[str]							= VaeImageProcessor(do_resize=lowerCamelCase_							,							do_normalize=lowerCamelCase_)

         a					:							Optional[Any]							= self.dummy_sample
         a					:							Union[str, Any]							= list(lowerCamelCase_)

         a					:							Union[str, Any]							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							output_type='np'							,							)

         a					:							Optional[int]							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							output_type='np'							,							)

         a					:							str							= self.to_np(self.dummy_sample)
         list(lowerCamelCase_)

         a					:							Optional[Any]							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							output_type='np'							,							)

         a					:							Optional[int]							= image_processor.postprocess(
             image_processor.preprocess(lowerCamelCase_)							,							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





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,
)


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

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

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

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''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

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

