

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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase      ):
	_lowercase	: Any												=	StableDiffusionXLPipeline
	_lowercase	: Dict												=	TEXT_TO_IMAGE_PARAMS
	_lowercase	: int												=	TEXT_TO_IMAGE_BATCH_PARAMS
	_lowercase	: Tuple												=	TEXT_TO_IMAGE_IMAGE_PARAMS
	_lowercase	: List[str]												=	TEXT_TO_IMAGE_IMAGE_PARAMS







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						torch.manual_seed(0   )
						__magic_name__				=     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=__UpperCamelCase	,			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	,			)
						__magic_name__				=     EulerDiscreteScheduler(
						    beta_start=0.00085	,			beta_end=0.012	,			steps_offset=1	,			beta_schedule='scaled_linear'	,			timestep_spacing='leading'	,			)
						torch.manual_seed(0   )
						__magic_name__				=     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   )
						__magic_name__				=     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	,			)
						__magic_name__				=     CLIPTextModel(__UpperCamelCase   )
						__magic_name__				=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	,			local_files_only=__UpperCamelCase   )

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

						__magic_name__				=     {
						    '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 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str=0   ):

						'''simple docstring'''





						if str(__UpperCamelCase   ).startswith('mps'   ):
											__magic_name__				=     torch.manual_seed(__UpperCamelCase   )
						else:
											__magic_name__				=     torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
						__magic_name__				=     {
						    '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 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





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

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

						assert image.shape == (1, 64, 64, 3)
						__magic_name__				=     np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.get_dummy_components()
						__magic_name__				=     StableDiffusionXLPipeline(**__UpperCamelCase   )
						__magic_name__				=     sd_pipe.to(__UpperCamelCase   )
						__magic_name__				=     sd_pipe.to(__UpperCamelCase   )
						sd_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

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

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

						(
						    (
						    __magic_name__
						),						(
						    __magic_name__
						),						(
						    __magic_name__
						),						(
						    __magic_name__
						),						
						)				=     sd_pipe.encode_prompt(__UpperCamelCase	,			negative_prompt=__UpperCamelCase   )

						__magic_name__				=     sd_pipe(
						    **__UpperCamelCase	,			prompt_embeds=__UpperCamelCase	,			negative_prompt_embeds=__UpperCamelCase	,			pooled_prompt_embeds=__UpperCamelCase	,			negative_pooled_prompt_embeds=__UpperCamelCase	,			)
						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





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




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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			Optional[int]	,			__UpperCamelCase:			Optional[int]="cpu"	,			__UpperCamelCase:			List[Any]=torch.floataa	,			__UpperCamelCase:			int=0   ):

						'''simple docstring'''





						__magic_name__				=     torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
						__magic_name__				=     np.random.RandomState(__UpperCamelCase   ).standard_normal((1, 4, 64, 64)   )
						__magic_name__				=     torch.from_numpy(__UpperCamelCase   ).to(device=__UpperCamelCase	,			dtype=__UpperCamelCase   )
						__magic_name__				=     {
						    '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 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





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

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

						assert image.shape == (1, 5_12, 5_12, 3)
						__magic_name__				=     np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]   )
						assert np.abs(image_slice - expected_slice   ).max() < 7E-3







def        _lowercase  (					a_					:       str       ,a_					:       List[str]					)					->						Optional[Any]:




					'''simple docstring'''







					sequence.append(a_					)

					__magic_name__				=     0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(a_					):
										return None

					return index


if __name__ == "__main__":
						A__							=				input("Enter numbers separated by comma:\n").strip()
						A__							=				[int(item) for item in user_input.split(",")]

						A__							=				input("Enter a single number to be found in the list:\n")
						A__							=				int(target_input)
						A__							=				sentinel_linear_search(sequence, target)
						if result is not None:
												print(f'''{target} found at positions: {result}''')
						else:
												print("Not found")




