"""simple docstring"""







import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @property
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					1
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					(32, 32)

      __lowerCamelCase    		=					floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0	)	).to(_SCREAMING_SNAKE_CASE	)
      return image







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					UNetaDModel(
          block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=6 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
      return model







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					VQModel(
          block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
      return model







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					'cpu'
      __lowerCamelCase    		=					self.dummy_uncond_unet
      __lowerCamelCase    		=					DDIMScheduler()
      __lowerCamelCase    		=					self.dummy_vq_model

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

      __lowerCamelCase    		=					image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __lowerCamelCase    		=					np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]	)

      assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2







   @unittest.skipIf(torch_device != 'cuda' ,				'This test requires a GPU'	)
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.dummy_uncond_unet
      __lowerCamelCase    		=					DDIMScheduler()
      __lowerCamelCase    		=					self.dummy_vq_model

      # put models in fp16
      __lowerCamelCase    		=					unet.half()
      __lowerCamelCase    		=					vqvae.half()

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					ldm(_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

      assert image.shape == (1, 64, 64, 3)






@slow
@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/vq_diffusion/teddy_bear_pool.png'	)
      __lowerCamelCase    		=					init_image.resize((64, 64) ,				resample=PIL_INTERPOLATION['lanczos']	)

      __lowerCamelCase    		=					LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution' ,				device_map='auto'	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=20 ,				output_type='numpy'	).images

      __lowerCamelCase    		=					image[0, -3:, -3:, -1]

      assert image.shape == (1, 256, 256, 3)
      __lowerCamelCase    		=					np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]	)

      assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2










'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase     :Optional[Any]												=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)