import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase     						=			False




@nightly
@require_torch_gpu
class 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        assert np.abs(image - new_image					).sum() < 1e-5, "Models don't have the same forward pass"

 def    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

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




from typing import TYPE_CHECKING

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


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''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     						=			[
     '''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     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



