






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 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				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 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		: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=__lowerCAmelCase       ,					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       ,					)
								__magic_name__		:Any        =      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__		:Optional[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     )
								__magic_name__		:int        =      CLIPTextConfig(
								    bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					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       ,					)
								__magic_name__		:str        =      CLIPTextModel(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,					local_files_only=__lowerCAmelCase     )

								__magic_name__		:List[str]        =      CLIPTextModelWithProjection(__lowerCAmelCase     )
								__magic_name__		:Tuple        =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,					local_files_only=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      {
								    '''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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=0     ):
								"""simple docstring"""
								__magic_name__		:int        =      floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
								__magic_name__		:int        =      image / 2 + 0.5
								if str(__lowerCAmelCase     ).startswith('''mps'''     ):
												__magic_name__		:int        =      torch.manual_seed(__lowerCAmelCase     )
								else:
												__magic_name__		:Optional[Any]        =      torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      {
								    '''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''': 0.75,
								}
								return inputs







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__magic_name__		:Any        =      self.get_dummy_components()
								__magic_name__		:int        =      StableDiffusionXLImgaImgPipeline(**__lowerCAmelCase     )
								__magic_name__		:str        =      sd_pipe.to(__lowerCAmelCase     )
								sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:List[str]        =      self.get_dummy_inputs(__lowerCAmelCase     )
								__magic_name__		:Optional[int]        =      sd_pipe(**__lowerCAmelCase     ).images
								__magic_name__		:List[str]        =      image[0, -3:, -3:, -1]

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

								__magic_name__		:Tuple        =      np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]     )

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







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_attention_slicing_forward_pass(expected_max_diff=3E-3     )







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_dummy_components()
								__magic_name__		:int        =      StableDiffusionXLImgaImgPipeline(**__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      sd_pipe.to(__lowerCAmelCase     )
								__magic_name__		:Any        =      sd_pipe.to(__lowerCAmelCase     )
								sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

								# forward without prompt embeds
								__magic_name__		:Union[str, Any]        =      self.get_dummy_inputs(__lowerCAmelCase     )
								__magic_name__		:Any        =      3 * ['''this is a negative prompt''']
								__magic_name__		:Dict        =      negative_prompt
								__magic_name__		:str        =      3 * [inputs['''prompt''']]

								__magic_name__		:Optional[int]        =      sd_pipe(**__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      output.images[0, -3:, -3:, -1]

								# forward with prompt embeds
								__magic_name__		:Optional[Any]        =      self.get_dummy_inputs(__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      3 * ['''this is a negative prompt''']
								__magic_name__		:Tuple        =      3 * [inputs.pop('''prompt'''     )]

								(
								    (
								    __magic_name__
								)     ,				(
								    __magic_name__
								)     ,				(
								    __magic_name__
								)     ,				(
								    __magic_name__
								)     ,				
								)		:List[Any]        =      sd_pipe.encode_prompt(__lowerCAmelCase       ,					negative_prompt=__lowerCAmelCase     )

								__magic_name__		:int        =      sd_pipe(
								    **__lowerCAmelCase       ,					prompt_embeds=__lowerCAmelCase       ,					negative_prompt_embeds=__lowerCAmelCase       ,					pooled_prompt_embeds=__lowerCAmelCase       ,					negative_pooled_prompt_embeds=__lowerCAmelCase       ,					)
								__magic_name__		:Dict        =      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 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase="cpu"       ,					__lowerCAmelCase=torch.floataa       ,					__lowerCAmelCase=0     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      np.random.RandomState(__lowerCAmelCase     ).standard_normal((1, 4, 6_4, 6_4)     )
								__magic_name__		:str        =      torch.from_numpy(__lowerCAmelCase     ).to(device=__lowerCAmelCase       ,					dtype=__lowerCAmelCase     )
								__magic_name__		:int        =      {
								    '''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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''     )
								pipe.to(__lowerCAmelCase     )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__magic_name__		:int        =      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














import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								self.assertEqual(x.component(0     )       ,					1     )
								self.assertEqual(x.component(2     )       ,					3     )
								__magic_name__		:Tuple        =      Vector()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 1]     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,0,0,0,0,1)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      Vector([1, 2, 3, 4]     )
								self.assertEqual(len(__lowerCAmelCase     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      Vector([1, 2]     )
								__magic_name__		:int        =      Vector([1, 2, 3, 4, 5]     )
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[Any]        =      Vector([1, -1, 1, -1, 2, -3, 4, -5]     )
								self.assertAlmostEqual(x.euclidean_length()       ,					2.236       ,					3     )
								self.assertAlmostEqual(y.euclidean_length()       ,					7.416       ,					3     )
								self.assertEqual(z.euclidean_length()       ,					0     )
								self.assertAlmostEqual(w.euclidean_length()       ,					7.616       ,					3     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								__magic_name__		:List[str]        =      Vector([1, 1, 1]     )
								self.assertEqual((x + y).component(0     )       ,					2     )
								self.assertEqual((x + y).component(1     )       ,					3     )
								self.assertEqual((x + y).component(2     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 2, 3]     )
								__magic_name__		:Union[str, Any]        =      Vector([1, 1, 1]     )
								self.assertEqual((x - y).component(0     )       ,					0     )
								self.assertEqual((x - y).component(1     )       ,					1     )
								self.assertEqual((x - y).component(2     )       ,					2     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Vector([1, 2, 3]     )
								__magic_name__		:Optional[int]        =      Vector([2, -1, 4]     )  # for test of dot product
								__magic_name__		:List[Any]        =      Vector([1, -2, -1]     )
								self.assertEqual(str(x * 3.0     )       ,					'''(3.0,6.0,9.0)'''     )
								self.assertEqual((a * b)       ,					0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(zero_vector(1_0     )     ).count('''0'''     )       ,					1_0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(unit_basis_vector(3       ,					1     )     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Vector([1, 2, 3]     )
								__magic_name__		:List[Any]        =      Vector([1, 0, 1]     )
								self.assertEqual(str(axpy(2       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )     )       ,					'''(3,4,7)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[int]        =      x.copy()
								self.assertEqual(str(__lowerCAmelCase     )       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0]     )
								x.change_component(0       ,					0     )
								x.change_component(1       ,					1     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:List[str]        =      [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(minors[x][y]       ,					a.minor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Any        =      [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(cofactors[x][y]       ,					a.cofactor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(-5       ,					a.determinant()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       ,					3       ,					3     )
								__magic_name__		:Any        =      Vector([1, 2, 3]     )
								self.assertEqual('''(14,32,50)'''       ,					str(a * x     )     )
								self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''       ,					str(a * 2     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								a.change_component(0       ,					2       ,					5     )
								self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(7       ,					a.component(2       ,					1     )       ,					0.01     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Optional[Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''       ,					str(a + b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''       ,					str(a - b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(
								    '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''       ,					str(square_zero_matrix(5     )     )       ,					)


if __name__ == "__main__":
					unittest.main()






