


"""simple docstring"""







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    __A       ( snake_case_,					snake_case_,					snake_case_,					unittest.TestCase    ):






 """simple docstring"""




 __lowerCAmelCase				      =						StableDiffusionModelEditingPipeline
 __lowerCAmelCase				      =						TEXT_TO_IMAGE_PARAMS
 __lowerCAmelCase				      =						TEXT_TO_IMAGE_BATCH_PARAMS
 __lowerCAmelCase				      =						TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowerCAmelCase				      =						TEXT_TO_IMAGE_IMAGE_PARAMS


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     torch.manual_seed(0		)
     a					=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				,					)
     a					=DDIMScheduler()
     torch.manual_seed(0		)
     a					=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		)
     a					=CLIPTextConfig(
         bos_token_id=0				,					eos_token_id=2				,					hidden_size=32				,					intermediate_size=37				,					layer_norm_eps=1E-0_5				,					num_attention_heads=4				,					num_hidden_layers=5				,					pad_token_id=1				,					vocab_size=1000				,					)
     a					=CLIPTextModel(__A		)
     a					=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=0		)      ->  Optional[Any]:
     a					=torch.manual_seed(__A		)
     a					={
         '''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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
     a					='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a					=self.get_dummy_components()
     a					=StableDiffusionModelEditingPipeline(**__A		)
     a					=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

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

     a					=np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     a					='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a					=self.get_dummy_components()
     a					=StableDiffusionModelEditingPipeline(**__A		)
     a					=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

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

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

     a					=np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     a					='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a					=self.get_dummy_components()
     a					=EulerAncestralDiscreteScheduler(
         beta_start=0.00_085				,					beta_end=0.012				,					beta_schedule='''scaled_linear'''		)
     a					=StableDiffusionModelEditingPipeline(**__A		)
     a					=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

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

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

     a					=np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     a					='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a					=self.get_dummy_components()
     a					=PNDMScheduler()
     a					=StableDiffusionModelEditingPipeline(**__A		)
     a					=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     super().test_inference_batch_single_identical(expected_max_diff=5E-3		)





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)






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






 """simple docstring"""




 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 def    SCREAMING_SNAKE_CASE							(	self				,					__A=0		)      ->  int:
     a					=torch.manual_seed(__A		)
     a					={
         '''prompt''': '''A field of roses''',
         '''generator''': generator,
         '''num_inference_steps''': 3,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					='''CompVis/stable-diffusion-v1-4'''
     a					=StableDiffusionModelEditingPipeline.from_pretrained(__A				,					safety_checker=__A		)
     pipe.to(__A		)
     pipe.set_progress_bar_config(disable=__A		)
     pipe.enable_attention_slicing()

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

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

     a					=np.array(
         [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]		)

     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'''		)

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

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

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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

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

     a					=self.get_inputs()
     a					=pipe(**__A		)

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_     :     List[str]    		=						[
           """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DinatForImageClassification""",
           """DinatModel""",
           """DinatPreTrainedModel""",
           """DinatBackbone""",
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

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