


import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()


class 					lowerCAmelCase_ (       _a			,unittest.TestCase):
						lowerCamelCase_         =     LDMTextToImagePipeline
						lowerCamelCase_         =     TEXT_TO_IMAGE_PARAMS - {
						    'negative_prompt',
						    'negative_prompt_embeds',
						    'cross_attention_kwargs',
						    'prompt_embeds',
						}
						lowerCamelCase_         =     PipelineTesterMixin.required_optional_params - {
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						lowerCamelCase_         =     TEXT_TO_IMAGE_BATCH_PARAMS
						lowerCamelCase_         =     False




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Dict:






									"""simple docstring"""






									torch.manual_seed(0						)
									a__ :Union[str, Any]						  =    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")   ,     cross_attention_dim=32   ,     )
									a__ :Tuple						  =    DDIMScheduler(
									    beta_start=0.00_085   ,     beta_end=0.012   ,     beta_schedule="scaled_linear"   ,     clip_sample=__A   ,     set_alpha_to_one=__A   ,     )
									torch.manual_seed(0						)
									a__ :Any						  =    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   ,     )
									torch.manual_seed(0						)
									a__ :Optional[int]						  =    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=1000   ,     )
									a__ :Tuple						  =    CLIPTextModel(__A						)
									a__ :Optional[int]						  =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

									a__ :Union[str, Any]						  =    {
									    "unet": unet,
									    "scheduler": scheduler,
									    "vqvae": vae,
									    "bert": text_encoder,
									    "tokenizer": tokenizer,
									}
									return components




						def  _snake_case					(	self						:   Dict   ,     __A						:   Optional[int]   ,     __A						:   str=0						)      ->List[str]:






									"""simple docstring"""






									if str(__A						).startswith("mps"						):
												a__ :int						  =    torch.manual_seed(__A						)
									else:
												a__ :Any						  =    torch.Generator(device=__A						).manual_seed(__A						)
									a__ :List[Any]						  =    {
									    "prompt": "A painting of a squirrel eating a burger",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs






						def  _snake_case					(	self						:   List[str]						)      ->int:






									"""simple docstring"""






									a__ :Tuple						  =    "cpu"  # ensure determinism for the device-dependent torch.Generator

									a__ :Optional[Any]						  =    self.get_dummy_components()
									a__ :List[str]						  =    LDMTextToImagePipeline(**__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    self.get_dummy_inputs(__A						)
									a__ :Dict						  =    pipe(**__A						).images
									a__ :Dict						  =    image[0, -3:, -3:, -1]

									assert image.shape == (1, 16, 16, 3)
									a__ :str						  =    np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






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




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






									"""simple docstring"""






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




						def  _snake_case					(	self						:   str   ,     __A						:   Any   ,     __A						:   Any=torch.floataa   ,     __A						:   Optional[int]=0						)      ->Any:






									"""simple docstring"""






									a__ :List[str]						  =    torch.manual_seed(__A						)
									a__ :Optional[Any]						  =    np.random.RandomState(__A						).standard_normal((1, 4, 32, 32)						)
									a__ :str						  =    torch.from_numpy(__A						).to(device=__A   ,     dtype=__A						)
									a__ :Union[str, Any]						  =    {
									    "prompt": "A painting of a squirrel eating a burger",
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs






						def  _snake_case					(	self						:   int						)      ->List[Any]:






									"""simple docstring"""






									a__ :str						  =    LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"						).to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

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

									assert image.shape == (1, 256, 256, 3)
									a__ :str						  =    np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]						)
									a__ :Optional[Any]						  =    np.abs(expected_slice - image_slice						).max()
									assert max_diff < 1E-3






@nightly
@require_torch_gpu
class 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   Union[str, Any]   ,     __A						:   int=torch.floataa   ,     __A						:   Any=0						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    torch.manual_seed(__A						)
									a__ :Union[str, Any]						  =    np.random.RandomState(__A						).standard_normal((1, 4, 32, 32)						)
									a__ :List[Any]						  =    torch.from_numpy(__A						).to(device=__A   ,     dtype=__A						)
									a__ :Optional[int]						  =    {
									    "prompt": "A painting of a squirrel eating a burger",
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 50,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs






						def  _snake_case					(	self						:   Optional[int]						)      ->int:






									"""simple docstring"""






									a__ :Dict						  =    LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"						).to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :str						  =    self.get_inputs(__A						)
									a__ :int						  =    pipe(**__A						).images[0]

									a__ :Optional[int]						  =    load_numpy(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"						)
									a__ :str						  =    np.abs(expected_image - image						).max()
									assert max_diff < 1E-3






def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list[list]:





			"""simple docstring"""
			a__ :Any						  =    current_set.copy()
			for row_index, row in enumerate(a				):
						a__ :List[str]						  =    row[0]
						for column_index, column in enumerate(a				):
									if magnitude == 0:
												a__ :Tuple						  =    column
												continue
									a__ :Tuple						  =    column / magnitude
    # Subtract to cancel term
			a__ :Any						  =    current_set[0]
			a__ :Dict						  =    [first_row]
			a__ :Dict						  =    current_set[1::]
			for row in current_set:
						a__ :int						  =    []
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(a				)
									continue
						for column_index in range(len(a				)				):
									temp_row.append(first_row[column_index] - row[column_index]				)
						final_set.append(a				)
			# Create next recursion iteration set
			if len(final_set[0]				) != 3:
						a__ :List[str]						  =    final_set[0]
						a__ :Optional[Any]						  =    []
						a__ :List[Any]						  =    []
						for row in final_set[1::]:
									current_first_column.append(row[0]				)
									next_iteration.append(row[1::]				)
						a__ :Dict						  =    simplify(a				)
						for i in range(len(a				)				):
									resultant[i].insert(0	,  current_first_column[i]				)
						resultant.insert(0	,  a				)
						a__ :Optional[int]						  =    resultant
			return final_set


def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list:





			"""simple docstring"""
			if len(a				) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			a__ :Any						  =    len(a				) + 1
			if any(len(a				) != _length for item in equations				):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			for row in equations:
						if any(not isinstance(a	,  (int, float)				) for column in row				):
									raise ValueError("solve_simultaneous() requires lists of integers"				)
			if len(a				) == 1:
						return [equations[0][-1] / equations[0][0]]
			a__ :Union[str, Any]						  =    equations.copy()
			if any(0 in row for row in data_set				):
						a__ :int						  =    data_set.copy()
						a__ :Dict						  =    []
						for row_index, row in enumerate(a				):
									if 0 not in row:
												a__ :Union[str, Any]						  =    data_set.pop(a				)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
						data_set.insert(0	,  a				)
			a__ :Optional[int]						  =    data_set.copy()
			a__ :Any						  =    simplify(a				)
			a__ :str						  =    simplified[::-1]
			a__ :list						  =    []
			for row in simplified:
						a__ :Optional[int]						  =    row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0				)
												continue
									solutions.append(current_solution / row[-2]				)
									continue
						a__ :Dict						  =    row.copy()[: len(a				) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0				)
						if len(a				) == 0:
									solutions.append(0				)
									continue
						a__ :List[Any]						  =    temp_row[1::]
						a__ :Union[str, Any]						  =    temp_row[::-1]
						for column_index, column in enumerate(a				):
									current_solution -= column * solutions[column_index]
						solutions.append(a				)
			a__ :List[str]						  =    []
			for item in solutions:
						final.append(float(round(a	,  5				)				)				)
			return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		snake_case__			 =						[
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))


