
"""simple docstring"""





import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

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


enable_full_determinism()



@skip_mps
class __lowerCAmelCase				(					__lowerCAmelCase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				VideoToVideoSDPipeline
			snake_case	      =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}      ) - {"image", "width", "height"}
			snake_case	      =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}      ) - {"image"}
			snake_case	      =				PipelineTesterMixin.required_optional_params - {"latents"}
			snake_case	      =				False

			# No `output_type`.
			snake_case	      =				frozenset(
			    [
			        "num_inference_steps",
			        "generator",
			        "latents",
			        "return_dict",
			        "callback",
			        "callback_steps",
			    ]      )




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[str]:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						UNetaDConditionModel(
								    block_out_channels=(32, 64, 64, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")       ,   up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")       ,   cross_attention_dim=32       ,   attention_head_dim=4       ,   )
								A_				 =						DDIMScheduler(
								    beta_start=0.0_0_0_8_5       ,   beta_end=0.0_1_2       ,   beta_schedule="scaled_linear"       ,   clip_sample=lowerCamelCase__       ,   set_alpha_to_one=lowerCamelCase__       ,   )
								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=128       ,   )
								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=1_000       ,   hidden_act="gelu"       ,   projection_dim=512       ,   )
								A_				 =						CLIPTextModel(lowerCamelCase__	)
								A_				 =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

								A_				 =						{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								}
								return components




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Any       ,   _snake_case						:					str=0	)      -> str:







								"""simple docstring"""
								A_				 =						floats_tensor((1, 3, 3, 32, 32)       ,   rng=random.Random(lowerCamelCase__	)	).to(lowerCamelCase__	)

								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''',
								    '''video''': video,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''pt''',
								}
								return inputs




			def      lowerCamelCase__     (				self						:					List[str]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								A_				 =						self.get_dummy_components()
								A_				 =						VideoToVideoSDPipeline(**lowerCamelCase__	)
								A_				 =						sd_pipe.to(lowerCamelCase__	)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

								A_				 =						self.get_dummy_inputs(lowerCamelCase__	)
								A_				 =						'''np'''
								A_				 =						sd_pipe(**lowerCamelCase__	).frames
								A_				 =						frames[0][-3:, -3:, -1]

								assert frames[0].shape == (32, 32, 3)
								A_				 =						np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]	)

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




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()       ,   reason="XFormers attention is only available with CUDA and `xformers` installed"       ,   )
			def      lowerCamelCase__     (				self						:					Dict	)      -> str:







								"""simple docstring"""
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__       ,   expected_max_diff=5e-3	)




			@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."	)
			def      lowerCamelCase__     (				self						:					Any	)      -> Any:







								"""simple docstring"""
								pass




			@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."	)
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								pass




			@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."	)
			def      lowerCamelCase__     (				self						:					Any	)      -> Dict:







								"""simple docstring"""
								pass






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







								"""simple docstring"""
								return super().test_progress_bar()





@slow
@skip_mps
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







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







								"""simple docstring"""
								A_				 =						VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"       ,   torch_dtype=torch.floataa	)
								pipe.enable_model_cpu_offload()

								# 10 frames
								A_				 =						torch.Generator(device="cpu"	).manual_seed(0	)
								A_				 =						torch.randn((1, 10, 3, 1_024, 576)       ,   generator=lowerCamelCase__	)
								A_				 =						video.to("cuda"	)

								A_				 =						'''Spiderman is surfing'''

								A_				 =						pipe(lowerCamelCase__       ,   video=lowerCamelCase__       ,   generator=lowerCamelCase__       ,   num_inference_steps=3       ,   output_type="pt"	).frames

								A_				 =						np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]	)
								assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array	).sum() < 1e-2






"""simple docstring"""





def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						current_set.copy()
					for row_index, row in enumerate(__a				):
										A_				 =						row[0]
										for column_index, column in enumerate(__a				):
															if magnitude == 0:
																				A_				 =						column
																				continue
															A_				 =						column / magnitude
    # Subtract to cancel term
					A_				 =						current_set[0]
					A_				 =						[first_row]
					A_				 =						current_set[1::]
					for row in current_set:
										A_				 =						[]
										# 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_				 =						final_set[0]
										A_				 =						[]
										A_				 =						[]
										for row in final_set[1::]:
															current_first_column.append(row[0]				)
															next_iteration.append(row[1::]				)
										A_				 =						simplify(__a				)
										for i in range(len(__a				)				):
															resultant[i].insert(0				,   current_first_column[i]				)
										resultant.insert(0				,   __a				)
										A_				 =						resultant
					return final_set
def 							A_     (__a				):

					'''simple docstring'''





					if len(__a				) == 0:
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					A_				 =						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_				 =						equations.copy()
					if any(0 in row for row in data_set				):
										A_				 =						data_set.copy()
										A_				 =						[]
										for row_index, row in enumerate(__a				):
															if 0 not in row:
																				A_				 =						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_				 =						data_set.copy()
					A_				 =						simplify(__a				)
					A_				 =						simplified[::-1]
					A_				 =						[]
					for row in simplified:
										A_				 =						row[-1]
										if not solutions:
															if row[-2] == 0:
																				solutions.append(0				)
																				continue
															solutions.append(current_solution / row[-2]				)
															continue
										A_				 =						row.copy()[: len(__a				) - 1 :]
										while temp_row[0] == 0:
															temp_row.pop(0				)
										if len(__a				) == 0:
															solutions.append(0				)
															continue
										A_				 =						temp_row[1::]
										A_				 =						temp_row[::-1]
										for column_index, column in enumerate(__a				):
															current_solution -= column * solutions[column_index]
										solutions.append(__a				)
					A_				 =						[]
					for item in solutions:
										final.append(float(round(__a				,   5				)				)				)
					return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCamelCase_					:						List[Any]				= [
		    [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]]))




