




'''simple docstring'''


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()

class 			A    (		_a     ,_a     ,unittest.TestCase    ):
							lowercase_							=				StableDiffusionXLPipeline
							lowercase_							=				TEXT_TO_IMAGE_PARAMS
							lowercase_							=				TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_							=				TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase_							=				TEXT_TO_IMAGE_IMAGE_PARAMS







							def    __lowerCAmelCase			(  self       :  List[str]					)			->							List[Any]:


														"""simple docstring"""


														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''')							,			attention_head_dim=(2, 4)							,			use_linear_projection=lowerCAmelCase_							,			addition_embed_type='''text_time'''							,			addition_time_embed_dim=8							,			transformer_layers_per_block=(1, 2)							,			projection_class_embeddings_input_dim=80							,			cross_attention_dim=64							,			)
														_a	     =				EulerDiscreteScheduler(
														    beta_start=0.0_0_0_8_5							,			beta_end=0.0_1_2							,			steps_offset=1							,			beta_schedule='''scaled_linear'''							,			timestep_spacing='''leading'''							,			)
														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							,			sample_size=1_28							,			)
														torch.manual_seed(0					)
														_a	     =				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=10_00							,			hidden_act='''gelu'''							,			projection_dim=32							,			)
														_a	     =				CLIPTextModel(lowerCAmelCase_					)
														_a	     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=lowerCAmelCase_					)

														_a	     =				CLIPTextModelWithProjection(lowerCAmelCase_					)
														_a	     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=lowerCAmelCase_					)

														_a	     =				{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''text_encoder_2''': text_encoder_a,
														    '''tokenizer_2''': tokenizer_a,
														    # "safety_checker": None,
														    # "feature_extractor": None,
														}
														return components







							def    __lowerCAmelCase			(  self       :  str							,			lowerCAmelCase_       :  Optional[Any]							,			lowerCAmelCase_       :  Tuple=0					)			->							List[str]:


														"""simple docstring"""


														if str(lowerCAmelCase_					).startswith('''mps'''					):
																					_a	     =				torch.manual_seed(lowerCAmelCase_					)
														else:
																					_a	     =				torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
														_a	     =				{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 5.0,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def    __lowerCAmelCase			(  self       :  Dict					)			->							Any:


														"""simple docstring"""


														_a	     =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														_a	     =				self.get_dummy_components()
														_a	     =				StableDiffusionXLPipeline(**lowerCAmelCase_					)
														_a	     =				sd_pipe.to(lowerCAmelCase_					)
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

														assert image.shape == (1, 64, 64, 3)
														_a	     =				np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]					)

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







							def    __lowerCAmelCase			(  self       :  int					)			->							Any:


														"""simple docstring"""


														_a	     =				self.get_dummy_components()
														_a	     =				StableDiffusionXLPipeline(**lowerCAmelCase_					)
														_a	     =				sd_pipe.to(lowerCAmelCase_					)
														_a	     =				sd_pipe.to(lowerCAmelCase_					)
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

														# forward without prompt embeds
														_a	     =				self.get_dummy_inputs(lowerCAmelCase_					)
														_a	     =				3 * ['''this is a negative prompt''']
														_a	     =				negative_prompt
														_a	     =				3 * [inputs['''prompt''']]

														_a	     =				sd_pipe(**lowerCAmelCase_					)
														_a	     =				output.images[0, -3:, -3:, -1]

														# forward with prompt embeds
														_a	     =				self.get_dummy_inputs(lowerCAmelCase_					)
														_a	     =				3 * ['''this is a negative prompt''']
														_a	     =				3 * [inputs.pop('''prompt'''					)]

														(
														    (
														    _a
														)     ,							(
														    _a
														)     ,							(
														    _a
														)     ,							(
														    _a
														)     ,							
														)	     =				sd_pipe.encode_prompt(lowerCAmelCase_							,			negative_prompt=lowerCAmelCase_					)

														_a	     =				sd_pipe(
														    **lowerCAmelCase_							,			prompt_embeds=lowerCAmelCase_							,			negative_prompt_embeds=lowerCAmelCase_							,			pooled_prompt_embeds=lowerCAmelCase_							,			negative_pooled_prompt_embeds=lowerCAmelCase_							,			)
														_a	     =				output.images[0, -3:, -3:, -1]

														# make sure that it's equal
														assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()					).max() < 1e-4







							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Optional[Any]:


														"""simple docstring"""


														super().test_attention_slicing_forward_pass(expected_max_diff=3e-3					)







							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Optional[int]:


														"""simple docstring"""


														super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



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







							def    __lowerCAmelCase			(  self       :  Dict					)			->							str:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def    __lowerCAmelCase			(  self       :  Tuple							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  Dict="cpu"							,			lowerCAmelCase_       :  str=torch.floataa							,			lowerCAmelCase_       :  List[str]=0					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
														_a	     =				np.random.RandomState(lowerCAmelCase_					).standard_normal((1, 4, 64, 64)					)
														_a	     =				torch.from_numpy(lowerCAmelCase_					).to(device=lowerCAmelCase_							,			dtype=lowerCAmelCase_					)
														_a	     =				{
														    '''prompt''': '''a photograph of an astronaut riding a horse''',
														    '''latents''': latents,
														    '''generator''': generator,
														    '''num_inference_steps''': 3,
														    '''guidance_scale''': 7.5,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Tuple:


														"""simple docstring"""


														_a	     =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''					)
														pipe.to(lowerCAmelCase_					)
														pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a	     =				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]					)
														assert np.abs(image_slice - expected_slice					).max() < 7e-3







'''simple docstring'''


from __future__ import annotations

from enum import Enum

class 			A    (		_a    ):
							lowercase_							=				24
							lowercase_							=				21
							lowercase_							=				18
							lowercase_							=				15
							lowercase_							=				12
							lowercase_							=				9
							lowercase_							=				6
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1
							lowercase_							=				-1
							lowercase_							=				-2
							lowercase_							=				-3
							lowercase_							=				-6
							lowercase_							=				-9
							lowercase_							=				-12
							lowercase_							=				-15
							lowercase_							=				-18
							lowercase_							=				-21
							lowercase_							=				-24



class 			A    (		_a    ):
							lowercase_							=				8
							lowercase_							=				7
							lowercase_							=				6
							lowercase_							=				5
							lowercase_							=				4
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1



def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | SIUnit ,						UpperCamelCase       :  str | SIUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    10 ** (known_prefix.value - unknown_prefix.value)
							)
							return unknown_amount
def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | BinaryUnit ,						UpperCamelCase       :  str | BinaryUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
							)
							return unknown_amount


if __name__ == "__main__":
				import doctest

				doctest.testmod()

