






import gc
import random
import unittest

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

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

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	StableDiffusionXLImgaImgPipeline
					a_			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
					a_			=	PipelineTesterMixin.required_optional_params - {"latents"}
					a_			=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					a_			=	IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_			=	IMAGE_TO_IMAGE_IMAGE_PARAMS



					def  _lowercase  (      self  :						str ):
						torch.manual_seed(0 )
						snake_case__				:   int     =						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")							,	attention_head_dim=(2, 4)							,	use_linear_projection=__A							,	addition_embed_type="text_time"							,	addition_time_embed_dim=8							,	transformer_layers_per_block=(1, 2)							,	projection_class_embeddings_input_dim=8_0							,	cross_attention_dim=6_4							,	)
						snake_case__				:   Union[str, Any]     =						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 )
						snake_case__				:   Any     =						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							,	sample_size=1_2_8							,	)
						torch.manual_seed(0 )
						snake_case__				:   Optional[int]     =						CLIPTextConfig(
						    bos_token_id=0							,	eos_token_id=2							,	hidden_size=3_2							,	intermediate_size=3_7							,	layer_norm_eps=1e-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1_0_0_0							,	hidden_act="gelu"							,	projection_dim=3_2							,	)
						snake_case__				:   Optional[int]     =						CLIPTextModel(__A )
						snake_case__				:   Any     =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							,	local_files_only=__A )

						snake_case__				:   Optional[Any]     =						CLIPTextModelWithProjection(__A )
						snake_case__				:   Tuple     =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							,	local_files_only=__A )

						snake_case__				:   Tuple     =						{
						    "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  _lowercase  (      self  :						Tuple							,	__A  :						str							,	__A  :						Optional[int]=0 ):
						snake_case__				:   List[str]     =						floats_tensor((1, 3, 3_2, 3_2)							,	rng=random.Random(__A ) ).to(__A )
						snake_case__				:   Union[str, Any]     =						image / 2 + 0.5
						if str(__A ).startswith("mps" ):
							snake_case__				:   Optional[int]     =						torch.manual_seed(__A )
						else:
							snake_case__				:   List[Any]     =						torch.Generator(device=__A ).manual_seed(__A )
						snake_case__				:   str     =						{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 5.0,
						    "output_type": "numpy",
						    "strength": 0.7_5,
						}
						return inputs



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Tuple     =						"cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   str     =						self.get_dummy_components()
						snake_case__				:   str     =						StableDiffusionXLImgaImgPipeline(**__A )
						snake_case__				:   List[str]     =						sd_pipe.to(__A )
						sd_pipe.set_progress_bar_config(disable=__A )

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

						assert image.shape == (1, 3_2, 3_2, 3)

						snake_case__				:   Any     =						np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )

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



					def  _lowercase  (      self  :						Tuple ):
						super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )



					def  _lowercase  (      self  :						Tuple ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3 )



					def  _lowercase  (      self  :						Optional[int] ):
						pass






					def  _lowercase  (      self  :						int ):
						snake_case__				:   List[str]     =						self.get_dummy_components()
						snake_case__				:   Dict     =						StableDiffusionXLImgaImgPipeline(**__A )
						snake_case__				:   Any     =						sd_pipe.to(__A )
						snake_case__				:   int     =						sd_pipe.to(__A )
						sd_pipe.set_progress_bar_config(disable=__A )

						# forward without prompt embeds
						snake_case__				:   int     =						self.get_dummy_inputs(__A )
						snake_case__				:   Tuple     =						3 * ["this is a negative prompt"]
						snake_case__				:   Optional[Any]     =						negative_prompt
						snake_case__				:   List[str]     =						3 * [inputs["prompt"]]

						snake_case__				:   str     =						sd_pipe(**__A )
						snake_case__				:   Optional[Any]     =						output.images[0, -3:, -3:, -1]

						# forward with prompt embeds
						snake_case__				:   Optional[Any]     =						self.get_dummy_inputs(__A )
						snake_case__				:   Union[str, Any]     =						3 * ["this is a negative prompt"]
						snake_case__				:   Union[str, Any]     =						3 * [inputs.pop("prompt" )]

						(
						    (
						    snake_case__
						),		(
						    snake_case__
						),		(
						    snake_case__
						),		(
						    snake_case__
						),		
						)				:   int     =						sd_pipe.encode_prompt(__A							,	negative_prompt=__A )

						snake_case__				:   List[str]     =						sd_pipe(
						    **__A							,	prompt_embeds=__A							,	negative_prompt_embeds=__A							,	pooled_prompt_embeds=__A							,	negative_pooled_prompt_embeds=__A							,	)
						snake_case__				:   Optional[Any]     =						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



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





					"""simple docstring"""






					def  _lowercase  (      self  :						Optional[Any] ):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						str							,	__A  :						int="cpu"							,	__A  :						str=torch.floataa							,	__A  :						Tuple=0 ):
						snake_case__				:   Tuple     =						torch.Generator(device=__A ).manual_seed(__A )
						snake_case__				:   Union[str, Any]     =						np.random.RandomState(__A ).standard_normal((1, 4, 6_4, 6_4) )
						snake_case__				:   Dict     =						torch.from_numpy(__A ).to(device=__A							,	dtype=__A )
						snake_case__				:   List[str]     =						{
						    "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  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Optional[Any]     =						DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base" )
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Optional[Any]     =						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











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Any     =						[0] * len(snake_case_     )

	for i in range(1  ,						len(snake_case_     )     ):
		# use last results for better performance - dynamic programming
		snake_case__				:   Union[str, Any]     =						prefix_result[i - 1]
		while j > 0 and input_string[i] != input_string[j]:
			snake_case__				:   str     =						prefix_result[j - 1]

		if input_string[i] == input_string[j]:
			j += 1
		snake_case__				:   int     =						j

	return prefix_result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	return max(prefix_function(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



