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  __lowerCAmelCase	(							a_,						a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Dict					     =			StableDiffusionModelEditingPipeline
 _lowerCamelCase    :						List[str]					     =			TEXT_TO_IMAGE_PARAMS
 _lowerCamelCase    :						Any					     =			TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase    :						Optional[Any]					     =			TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowerCamelCase    :						Optional[int]					     =			TEXT_TO_IMAGE_IMAGE_PARAMS

 def        __snake_case       (      self							:		Any	)			->				Any:
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[Any]	    =	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=4							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=3_2							,      )
        _UpperCamelCase  :							Tuple	    =	DDIMScheduler()
        torch.manual_seed(0	)
        _UpperCamelCase  :							Tuple	    =	AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,      in_channels=3							,      out_channels=3							,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,      latent_channels=4							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							List[str]	    =	CLIPTextConfig(
            bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
        _UpperCamelCase  :							Dict	    =	CLIPTextModel(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

        _UpperCamelCase  :							List[Any]	    =	{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]=0	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[Any]	    =	torch.manual_seed(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	{
            '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        __snake_case       (      self							:		str	)			->				List[Any]:
        _UpperCamelCase  :							str	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Tuple	    =	self.get_dummy_components()
        _UpperCamelCase  :							Optional[Any]	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							Tuple	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	sd_pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Any	    =	image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							int	    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]	)

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

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        _UpperCamelCase  :							Optional[Any]	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Optional[int]	    =	self.get_dummy_components()
        _UpperCamelCase  :							Tuple	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							str	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	'french fries'
        _UpperCamelCase  :							List[str]	    =	sd_pipe(**lowerCamelCase							,      negative_prompt=lowerCamelCase	)
        _UpperCamelCase  :							int	    =	output.images
        _UpperCamelCase  :							List[Any]	    =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							str	    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]	)

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

 def        __snake_case       (      self							:		Any	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_components()
        _UpperCamelCase  :							str	    =	EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='scaled_linear'	)
        _UpperCamelCase  :							Optional[int]	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	sd_pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Union[str, Any]	    =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							List[Any]	    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]	)

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

 def        __snake_case       (      self							:		Optional[int]	)			->				List[str]:
        _UpperCamelCase  :							int	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_components()
        _UpperCamelCase  :							Optional[Any]	    =	PNDMScheduler()
        _UpperCamelCase  :							Tuple	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							List[str]	    =	self.get_dummy_inputs(lowerCamelCase	)
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowerCamelCase	):
               _UpperCamelCase  :							Optional[int]	    =	sd_pipe(**lowerCamelCase	).images

 def        __snake_case       (      self							:		str	)			->				List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=5e-3	)

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







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



 '''simple docstring'''

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any=0	)			->				Optional[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	torch.manual_seed(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	{
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

 def        __snake_case       (      self							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							Tuple	    =	'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase  :							Dict	    =	StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase							,      safety_checker=lowerCamelCase	)
        pipe.to(lowerCamelCase	)
        pipe.set_progress_bar_config(disable=lowerCamelCase	)
        pipe.enable_attention_slicing()

        _UpperCamelCase  :							Tuple	    =	self.get_inputs()
        _UpperCamelCase  :							Tuple	    =	pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Optional[int]	    =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        _UpperCamelCase  :							List[Any]	    =	np.array(
            [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]	)

        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'	)

        _UpperCamelCase  :							List[Any]	    =	pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Optional[int]	    =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

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

        _UpperCamelCase  :							int	    =	'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase  :							Optional[Any]	    =	DDIMScheduler.from_pretrained(lowerCamelCase							,      subfolder='scheduler'	)
        _UpperCamelCase  :							int	    =	StableDiffusionModelEditingPipeline.from_pretrained(
            lowerCamelCase							,      scheduler=lowerCamelCase							,      safety_checker=lowerCamelCase	)
        _UpperCamelCase  :							str	    =	pipe.to(lowerCamelCase	)
        pipe.set_progress_bar_config(disable=lowerCamelCase	)
        pipe.enable_attention_slicing(1	)
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase  :							Any	    =	self.get_inputs()
        _UpperCamelCase  :							Any	    =	pipe(**lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 1_0**9









'''simple docstring'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase						:   Any      						=							TypeVar('''T''', bound='''Enum''')

@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Union[str, Any]       =   8_0
 __snake_case						:  Optional[Any]       =   7_0
 __snake_case						:  List[str]       =   6_0
 __snake_case						:  str       =   5_0
 __snake_case						:  Tuple       =   4_0
 __snake_case						:  Union[str, Any]       =   3_0
 __snake_case						:  Dict       =   2_0
 __snake_case						:  str       =   1_0






@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Optional[Any]       =   2_4
 __snake_case						:  List[str]       =   2_1
 __snake_case						:  Any       =   1_8
 __snake_case						:  Union[str, Any]       =   1_5
 __snake_case						:  Dict       =   1_2
 __snake_case						:  Union[str, Any]       =   9
 __snake_case						:  List[str]       =   6
 __snake_case						:  Optional[int]       =   3
 __snake_case						:  Tuple       =   2
 __snake_case						:  Any       =   1
 __snake_case						:  Optional[int]       =   -1
 __snake_case						:  Optional[int]       =   -2
 __snake_case						:  str       =   -3
 __snake_case						:  Optional[Any]       =   -6
 __snake_case						:  Optional[int]       =   -9
 __snake_case						:  List[Any]       =   -1_2
 __snake_case						:  str       =   -1_5
 __snake_case						:  str       =   -1_8
 __snake_case						:  Dict       =   -2_1
 __snake_case						:  int       =   -2_4



 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value > 0}





 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value < 0}






def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		Tuple    =   SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
       for name_prefix, value_prefix in prefixes.items():
              lowercase	:		Dict    =   value / (10**value_prefix)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {name_prefix}"""
       return str(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       for prefix in BinaryUnit:
              lowercase	:		Dict    =   value / (2**prefix.value)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {prefix.name}"""
       return str(lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




