
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





@skip_mps
class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =StableDiffusionModelEditingPipeline
       UpperCAmelCase_				       =TEXT_TO_IMAGE_PARAMS
       UpperCAmelCase_				       =TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCAmelCase_				       =TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCAmelCase_				       =TEXT_TO_IMAGE_IMAGE_PARAMS



       def     _UpperCamelCase	(							self	)				->				List[str]:
          torch.manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      UNetaDConditionModel(
              block_out_channels=(32, 64) ,     layers_per_block=2 ,     sample_size=32 ,     in_channels=4 ,     out_channels=4 ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,     cross_attention_dim=32 ,     )
          SCREAMING_SNAKE_CASE_	=      DDIMScheduler()
          torch.manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      AutoencoderKL(
              block_out_channels=[32, 64] ,     in_channels=3 ,     out_channels=3 ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,     latent_channels=4 ,     )
          torch.manual_seed(0	)
          SCREAMING_SNAKE_CASE_	=      CLIPTextConfig(
              bos_token_id=0 ,     eos_token_id=2 ,     hidden_size=32 ,     intermediate_size=37 ,     layer_norm_eps=1E-05 ,     num_attention_heads=4 ,     num_hidden_layers=5 ,     pad_token_id=1 ,     vocab_size=1000 ,     )
          SCREAMING_SNAKE_CASE_	=      CLIPTextModel(_A	)
          SCREAMING_SNAKE_CASE_	=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

          SCREAMING_SNAKE_CASE_	=      {
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components



       def     _UpperCamelCase	(							self ,     _A ,     _A=0	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      torch.manual_seed(_A	)
          SCREAMING_SNAKE_CASE_	=      {
              '''prompt''': '''A field of roses''',
              '''generator''': generator,
              # Setting height and width to None to prevent OOMs on CPU.
              '''height''': None,
              '''width''': None,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 6.0,
              '''output_type''': '''numpy''',
          }
          return inputs



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          SCREAMING_SNAKE_CASE_	=      self.get_dummy_components()
          SCREAMING_SNAKE_CASE_	=      StableDiffusionModelEditingPipeline(**_A	)
          SCREAMING_SNAKE_CASE_	=      sd_pipe.to(_A	)
          sd_pipe.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      self.get_dummy_inputs(_A	)
          SCREAMING_SNAKE_CASE_	=      sd_pipe(**_A	).images
          SCREAMING_SNAKE_CASE_	=      image[0, -3:, -3:, -1]
          assert image.shape == (1, 64, 64, 3)

          SCREAMING_SNAKE_CASE_	=      np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]	)

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



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          SCREAMING_SNAKE_CASE_	=      self.get_dummy_components()
          SCREAMING_SNAKE_CASE_	=      StableDiffusionModelEditingPipeline(**_A	)
          SCREAMING_SNAKE_CASE_	=      sd_pipe.to(_A	)
          sd_pipe.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      self.get_dummy_inputs(_A	)
          SCREAMING_SNAKE_CASE_	=      '''french fries'''
          SCREAMING_SNAKE_CASE_	=      sd_pipe(**_A ,     negative_prompt=_A	)
          SCREAMING_SNAKE_CASE_	=      output.images
          SCREAMING_SNAKE_CASE_	=      image[0, -3:, -3:, -1]

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

          SCREAMING_SNAKE_CASE_	=      np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]	)

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



       def     _UpperCamelCase	(							self	)				->				int:
          SCREAMING_SNAKE_CASE_	=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          SCREAMING_SNAKE_CASE_	=      self.get_dummy_components()
          SCREAMING_SNAKE_CASE_	=      EulerAncestralDiscreteScheduler(
              beta_start=0.0_0085 ,     beta_end=0.012 ,     beta_schedule='''scaled_linear'''	)
          SCREAMING_SNAKE_CASE_	=      StableDiffusionModelEditingPipeline(**_A	)
          SCREAMING_SNAKE_CASE_	=      sd_pipe.to(_A	)
          sd_pipe.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      self.get_dummy_inputs(_A	)
          SCREAMING_SNAKE_CASE_	=      sd_pipe(**_A	).images
          SCREAMING_SNAKE_CASE_	=      image[0, -3:, -3:, -1]

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

          SCREAMING_SNAKE_CASE_	=      np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]	)

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



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          SCREAMING_SNAKE_CASE_	=      self.get_dummy_components()
          SCREAMING_SNAKE_CASE_	=      PNDMScheduler()
          SCREAMING_SNAKE_CASE_	=      StableDiffusionModelEditingPipeline(**_A	)
          SCREAMING_SNAKE_CASE_	=      sd_pipe.to(_A	)
          sd_pipe.set_progress_bar_config(disable=_A	)

          SCREAMING_SNAKE_CASE_	=      self.get_dummy_inputs(_A	)
          # the pipeline does not expect pndm so test if it raises error.
          with self.assertRaises(_A	):
             SCREAMING_SNAKE_CASE_	=      sd_pipe(**_A	).images



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          super().test_inference_batch_single_identical(expected_max_diff=5E-3	)




       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=5E-3	)







@slow
@require_torch_gpu
class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       def     _UpperCamelCase	(							self	)				->				List[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



       def     _UpperCamelCase	(							self ,     _A=0	)				->				str:
          SCREAMING_SNAKE_CASE_	=      torch.manual_seed(_A	)
          SCREAMING_SNAKE_CASE_	=      {
              '''prompt''': '''A field of roses''',
              '''generator''': generator,
              '''num_inference_steps''': 3,
              '''guidance_scale''': 7.5,
              '''output_type''': '''numpy''',
          }
          return inputs



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      '''CompVis/stable-diffusion-v1-4'''
          SCREAMING_SNAKE_CASE_	=      StableDiffusionModelEditingPipeline.from_pretrained(_A ,     safety_checker=_A	)
          pipe.to(_A	)
          pipe.set_progress_bar_config(disable=_A	)
          pipe.enable_attention_slicing()

          SCREAMING_SNAKE_CASE_	=      self.get_inputs()
          SCREAMING_SNAKE_CASE_	=      pipe(**_A	).images
          SCREAMING_SNAKE_CASE_	=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)

          SCREAMING_SNAKE_CASE_	=      np.array(
              [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]	)

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

          # make sure image changes after editing
          pipe.edit_model('''A pack of roses''' ,     '''A pack of blue roses'''	)

          SCREAMING_SNAKE_CASE_	=      pipe(**_A	).images
          SCREAMING_SNAKE_CASE_	=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(expected_slice - image_slice	).max() > 1E-1




       def     _UpperCamelCase	(							self	)				->				List[str]:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          SCREAMING_SNAKE_CASE_	=      '''CompVis/stable-diffusion-v1-4'''
          SCREAMING_SNAKE_CASE_	=      DDIMScheduler.from_pretrained(_A ,     subfolder='''scheduler'''	)
          SCREAMING_SNAKE_CASE_	=      StableDiffusionModelEditingPipeline.from_pretrained(
              _A ,     scheduler=_A ,     safety_checker=_A	)
          SCREAMING_SNAKE_CASE_	=      pipe.to(_A	)
          pipe.set_progress_bar_config(disable=_A	)
          pipe.enable_attention_slicing(1	)
          pipe.enable_sequential_cpu_offload()

          SCREAMING_SNAKE_CASE_	=      self.get_inputs()
          SCREAMING_SNAKE_CASE_	=      pipe(**_A	)

          SCREAMING_SNAKE_CASE_	=      torch.cuda.max_memory_allocated()
          # make sure that less than 4.4 GB is allocated
          assert mem_bytes < 4.4 * 10**9




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase   						=     {
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     [
       "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DonutSwinModel",
       "DonutSwinPreTrainedModel",
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     ["DonutFeatureExtractor"]
   __UpperCAmelCase   						=     ["DonutImageProcessor"]


if TYPE_CHECKING:
   from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
   from .processing_donut import DonutProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_donut_swin import (
          DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          DonutSwinModel,
          DonutSwinPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_donut import DonutFeatureExtractor
      from .image_processing_donut import DonutImageProcessor

else:
   import sys

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


