



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  snake_case__						(	lowerCAmelCase_    ,					lowerCAmelCase_    ,					lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     StableDiffusionModelEditingPipeline
     _SCREAMING_SNAKE_CASE            =     TEXT_TO_IMAGE_PARAMS
     _SCREAMING_SNAKE_CASE            =     TEXT_TO_IMAGE_BATCH_PARAMS
     _SCREAMING_SNAKE_CASE            =     TEXT_TO_IMAGE_IMAGE_PARAMS
     _SCREAMING_SNAKE_CASE            =     TEXT_TO_IMAGE_IMAGE_PARAMS






     def        lowercase_       (	self  :  List[Any] )       ->Union[str, Any]:
            torch.manual_seed(0 )
            snake_case__							:     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,			)
            snake_case__							:     Dict            =     DDIMScheduler()
            torch.manual_seed(0 )
            snake_case__							:     List[str]            =     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 )
            snake_case__							:     Dict            =     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,			)
            snake_case__							:     str            =     CLIPTextModel(_snake_case )
            snake_case__							:     Any            =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

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






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

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

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

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

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

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

            snake_case__							:     List[Any]            =     self.get_dummy_inputs(_snake_case )
            snake_case__							:     int            =     sd_pipe(**_snake_case ).images
            snake_case__							:     Optional[int]            =     image[0, -3:, -3:, -1]

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

            snake_case__							:     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        lowercase_       (	self  :  Any )       ->Union[str, Any]:
            snake_case__							:     int            =     'cpu'  # ensure determinism for the device-dependent torch.Generator
            snake_case__							:     str            =     self.get_dummy_components()
            snake_case__							:     Union[str, Any]            =     PNDMScheduler()
            snake_case__							:     Union[str, Any]            =     StableDiffusionModelEditingPipeline(**_snake_case )
            snake_case__							:     List[str]            =     sd_pipe.to(_snake_case )
            sd_pipe.set_progress_bar_config(disable=_snake_case )

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






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






     def        lowercase_       (	self  :  int )       ->List[Any]:
            super().test_attention_slicing_forward_pass(expected_max_diff=5e-3 )




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






     """simple docstring"""


     def        lowercase_       (	self  :  int )       ->Optional[int]:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  int=0 )       ->Dict:
            snake_case__							:     Dict            =     torch.manual_seed(_snake_case )
            snake_case__							:     str            =     {
                'prompt': 'A field of roses',
                'generator': generator,
                'num_inference_steps': 3,
                'guidance_scale': 7.5,
                'output_type': 'numpy',
            }
            return inputs






     def        lowercase_       (	self  :  int )       ->int:
            snake_case__							:     Union[str, Any]            =     'CompVis/stable-diffusion-v1-4'
            snake_case__							:     Tuple            =     StableDiffusionModelEditingPipeline.from_pretrained(_snake_case,			safety_checker=_snake_case )
            pipe.to(_snake_case )
            pipe.set_progress_bar_config(disable=_snake_case )
            pipe.enable_attention_slicing()

            snake_case__							:     Optional[Any]            =     self.get_inputs()
            snake_case__							:     Optional[Any]            =     pipe(**_snake_case ).images
            snake_case__							:     str            =     image[0, -3:, -3:, -1].flatten()

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

            snake_case__							:     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' )

            snake_case__							:     Tuple            =     pipe(**_snake_case ).images
            snake_case__							:     Optional[Any]            =     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        lowercase_       (	self  :  Dict )       ->Any:
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

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

            snake_case__							:     Dict            =     self.get_inputs()
            snake_case__							:     Any            =     pipe(**_snake_case )

            snake_case__							:     Dict            =     torch.cuda.max_memory_allocated()
            # make sure that less than 4.4 GB is allocated
            assert mem_bytes < 4.4 * 1_0**9











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
 import doctest
 import time

 doctest.testmod()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






