import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class __snake_case							(		unittest.TestCase						):




   @property
   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 3
     SCREAMING_SNAKE_CASE__     				= 8
     SCREAMING_SNAKE_CASE__     				= 8

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

     return sample




   def       __a		(       self     :							Dict			,      _lowercase     :							Union[str, 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       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= VaeImageProcessor(do_resize=_lowerCamelCase			,      do_normalize=_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= self.dummy_sample
     SCREAMING_SNAKE_CASE__     				= self.to_np(_lowerCamelCase )

     for output_type in ["pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase )			,      output_type=_lowerCamelCase			,      )
       SCREAMING_SNAKE_CASE__     				= self.to_np(_lowerCamelCase )
       SCREAMING_SNAKE_CASE__     				= (input_np * 2_55).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       __a		(       self     :							Dict ):



     """simple docstring"""






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

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

       SCREAMING_SNAKE_CASE__     				= self.to_np(_lowerCamelCase )
       SCREAMING_SNAKE_CASE__     				= (input_np * 2_55).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       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= VaeImageProcessor(do_resize=_lowerCamelCase			,      do_normalize=_lowerCamelCase )

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

     for output_type in ["pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(image_processor.preprocess(_lowerCamelCase )			,      output_type=_lowerCamelCase )
       for i, o in zip(_lowerCamelCase			,      _lowerCamelCase ):
         SCREAMING_SNAKE_CASE__     				= np.array(_lowerCamelCase )
         SCREAMING_SNAKE_CASE__     				= self.to_np(_lowerCamelCase ) if output_type == """pil""" else (self.to_np(_lowerCamelCase ) * 2_55).round()
         assert (
             np.abs(in_np - out_np ).max() < 1E-6
         ), f"""decoded output does not match input for output_type {output_type}"""




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= VaeImageProcessor(do_resize=_lowerCamelCase			,      do_normalize=_lowerCamelCase )

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

     SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase )			,      output_type="""np"""			,      )
     SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase )			,      output_type="""np"""			,      )

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

     SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase )			,      output_type="""np"""			,      )
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= VaeImageProcessor(do_resize=_lowerCamelCase			,      do_normalize=_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= self.dummy_sample
     SCREAMING_SNAKE_CASE__     				= list(_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase )			,      output_type="""np"""			,      )

     SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase )			,      output_type="""np"""			,      )

     SCREAMING_SNAKE_CASE__     				= self.to_np(self.dummy_sample )
     list(_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase )			,      output_type="""np"""			,      )

     SCREAMING_SNAKE_CASE__     				= 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








"""simple docstring"""


from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


lowercase_								=	{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'Constraint',
           'ConstraintListState',
           'DisjunctiveConstraint',
           'PhrasalConstraint',
       ]
       lowercase_								=	[
           'BeamHypotheses',
           'BeamScorer',
           'BeamSearchScorer',
           'ConstrainedBeamSearchScorer',
       ]
       lowercase_								=	[
           'EpsilonLogitsWarper',
           'EtaLogitsWarper',
           'ForcedBOSTokenLogitsProcessor',
           'ForcedEOSTokenLogitsProcessor',
           'HammingDiversityLogitsProcessor',
           'InfNanRemoveLogitsProcessor',
           'LogitsProcessor',
           'LogitsProcessorList',
           'LogitsWarper',
           'MinLengthLogitsProcessor',
           'MinNewTokensLengthLogitsProcessor',
           'NoBadWordsLogitsProcessor',
           'NoRepeatNGramLogitsProcessor',
           'PrefixConstrainedLogitsProcessor',
           'RepetitionPenaltyLogitsProcessor',
           'SequenceBiasLogitsProcessor',
           'EncoderRepetitionPenaltyLogitsProcessor',
           'TemperatureLogitsWarper',
           'TopKLogitsWarper',
           'TopPLogitsWarper',
           'TypicalLogitsWarper',
           'EncoderNoRepeatNGramLogitsProcessor',
           'ExponentialDecayLengthPenalty',
           'LogitNormalization',
       ]
       lowercase_								=	[
           'MaxNewTokensCriteria',
           'MaxLengthCriteria',
           'MaxTimeCriteria',
           'StoppingCriteria',
           'StoppingCriteriaList',
           'validate_stopping_criteria',
       ]
       lowercase_								=	[
           'GenerationMixin',
           'top_k_top_p_filtering',
           'GreedySearchEncoderDecoderOutput',
           'GreedySearchDecoderOnlyOutput',
           'SampleEncoderDecoderOutput',
           'SampleDecoderOnlyOutput',
           'BeamSearchEncoderDecoderOutput',
           'BeamSearchDecoderOnlyOutput',
           'BeamSampleEncoderDecoderOutput',
           'BeamSampleDecoderOnlyOutput',
           'ContrastiveSearchEncoderDecoderOutput',
           'ContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'TFForcedBOSTokenLogitsProcessor',
           'TFForcedEOSTokenLogitsProcessor',
           'TFLogitsProcessor',
           'TFLogitsProcessorList',
           'TFLogitsWarper',
           'TFMinLengthLogitsProcessor',
           'TFNoBadWordsLogitsProcessor',
           'TFNoRepeatNGramLogitsProcessor',
           'TFRepetitionPenaltyLogitsProcessor',
           'TFTemperatureLogitsWarper',
           'TFTopKLogitsWarper',
           'TFTopPLogitsWarper',
           'TFForceTokensLogitsProcessor',
           'TFSuppressTokensAtBeginLogitsProcessor',
           'TFSuppressTokensLogitsProcessor',
       ]
       lowercase_								=	[
           'TFGenerationMixin',
           'tf_top_k_top_p_filtering',
           'TFGreedySearchDecoderOnlyOutput',
           'TFGreedySearchEncoderDecoderOutput',
           'TFSampleEncoderDecoderOutput',
           'TFSampleDecoderOnlyOutput',
           'TFBeamSearchEncoderDecoderOutput',
           'TFBeamSearchDecoderOnlyOutput',
           'TFBeamSampleEncoderDecoderOutput',
           'TFBeamSampleDecoderOnlyOutput',
           'TFContrastiveSearchEncoderDecoderOutput',
           'TFContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'FlaxForcedBOSTokenLogitsProcessor',
           'FlaxForcedEOSTokenLogitsProcessor',
           'FlaxLogitsProcessor',
           'FlaxLogitsProcessorList',
           'FlaxLogitsWarper',
           'FlaxMinLengthLogitsProcessor',
           'FlaxTemperatureLogitsWarper',
           'FlaxTopKLogitsWarper',
           'FlaxTopPLogitsWarper',
       ]
       lowercase_								=	[
           'FlaxGenerationMixin',
           'FlaxGreedySearchOutput',
           'FlaxSampleOutput',
           'FlaxBeamSearchOutput',
       ]

if TYPE_CHECKING:
       from .configuration_utils import GenerationConfig
       from .streamers import TextIteratorStreamer, TextStreamer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
              from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
              from .logits_process import (
                  EncoderNoRepeatNGramLogitsProcessor,
                  EncoderRepetitionPenaltyLogitsProcessor,
                  EpsilonLogitsWarper,
                  EtaLogitsWarper,
                  ExponentialDecayLengthPenalty,
                  ForcedBOSTokenLogitsProcessor,
                  ForcedEOSTokenLogitsProcessor,
                  HammingDiversityLogitsProcessor,
                  InfNanRemoveLogitsProcessor,
                  LogitNormalization,
                  LogitsProcessor,
                  LogitsProcessorList,
                  LogitsWarper,
                  MinLengthLogitsProcessor,
                  MinNewTokensLengthLogitsProcessor,
                  NoBadWordsLogitsProcessor,
                  NoRepeatNGramLogitsProcessor,
                  PrefixConstrainedLogitsProcessor,
                  RepetitionPenaltyLogitsProcessor,
                  SequenceBiasLogitsProcessor,
                  TemperatureLogitsWarper,
                  TopKLogitsWarper,
                  TopPLogitsWarper,
                  TypicalLogitsWarper,
              )
              from .stopping_criteria import (
                  MaxLengthCriteria,
                  MaxNewTokensCriteria,
                  MaxTimeCriteria,
                  StoppingCriteria,
                  StoppingCriteriaList,
                  validate_stopping_criteria,
              )
              from .utils import (
                  BeamSampleDecoderOnlyOutput,
                  BeamSampleEncoderDecoderOutput,
                  BeamSearchDecoderOnlyOutput,
                  BeamSearchEncoderDecoderOutput,
                  ContrastiveSearchDecoderOnlyOutput,
                  ContrastiveSearchEncoderDecoderOutput,
                  GenerationMixin,
                  GreedySearchDecoderOnlyOutput,
                  GreedySearchEncoderDecoderOutput,
                  SampleDecoderOnlyOutput,
                  SampleEncoderDecoderOutput,
                  top_k_top_p_filtering,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tf_logits_process import (
                  TFForcedBOSTokenLogitsProcessor,
                  TFForcedEOSTokenLogitsProcessor,
                  TFForceTokensLogitsProcessor,
                  TFLogitsProcessor,
                  TFLogitsProcessorList,
                  TFLogitsWarper,
                  TFMinLengthLogitsProcessor,
                  TFNoBadWordsLogitsProcessor,
                  TFNoRepeatNGramLogitsProcessor,
                  TFRepetitionPenaltyLogitsProcessor,
                  TFSuppressTokensAtBeginLogitsProcessor,
                  TFSuppressTokensLogitsProcessor,
                  TFTemperatureLogitsWarper,
                  TFTopKLogitsWarper,
                  TFTopPLogitsWarper,
              )
              from .tf_utils import (
                  TFBeamSampleDecoderOnlyOutput,
                  TFBeamSampleEncoderDecoderOutput,
                  TFBeamSearchDecoderOnlyOutput,
                  TFBeamSearchEncoderDecoderOutput,
                  TFContrastiveSearchDecoderOnlyOutput,
                  TFContrastiveSearchEncoderDecoderOutput,
                  TFGenerationMixin,
                  TFGreedySearchDecoderOnlyOutput,
                  TFGreedySearchEncoderDecoderOutput,
                  TFSampleDecoderOnlyOutput,
                  TFSampleEncoderDecoderOutput,
                  tf_top_k_top_p_filtering,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .flax_logits_process import (
                  FlaxForcedBOSTokenLogitsProcessor,
                  FlaxForcedEOSTokenLogitsProcessor,
                  FlaxLogitsProcessor,
                  FlaxLogitsProcessorList,
                  FlaxLogitsWarper,
                  FlaxMinLengthLogitsProcessor,
                  FlaxTemperatureLogitsWarper,
                  FlaxTopKLogitsWarper,
                  FlaxTopPLogitsWarper,
              )
              from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
       import sys

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




