



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 			lowercase__			(      _snake_case,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					KandinskyVaaInpaintPipeline
							_UpperCAmelCase      				=					['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
							_UpperCAmelCase      				=					[
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							    'mask_image',
							]
							_UpperCAmelCase      				=					[
							    'generator',
							    'height',
							    'width',
							    'latents',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							_UpperCAmelCase      				=					False






							@property
							def        lowerCamelCase_   (  self						)			->							Dict:
														return 32






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
														return 32






							@property
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
														return self.time_input_dim






							@property
							def        lowerCamelCase_   (  self						)			->							List[str]:
														return self.time_input_dim * 4






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
														return 100






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
														torch.manual_seed(0						)

														_UpperCAmelCase							 =		{
														    'in_channels': 9,
														    # Out channels is double in channels because predicts mean and variance
														    'out_channels': 8,
														    'addition_embed_type': 'image',
														    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
														    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
														    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
														    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
														    'layers_per_block': 1,
														    'encoder_hid_dim': self.text_embedder_hidden_size,
														    'encoder_hid_dim_type': 'image_proj',
														    'cross_attention_dim': self.cross_attention_dim,
														    'attention_head_dim': 4,
														    'resnet_time_scale_shift': 'scale_shift',
														    'class_embed_type': None,
														}

														_UpperCAmelCase							 =		UNetaDConditionModel(**snake_case						)
														return model






							@property
							def        lowerCamelCase_   (  self						)			->							int:
														return {
														    "block_out_channels": [32, 64],
														    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
														    "in_channels": 3,
														    "latent_channels": 4,
														    "layers_per_block": 1,
														    "norm_num_groups": 8,
														    "norm_type": "spatial",
														    "num_vq_embeddings": 12,
														    "out_channels": 3,
														    "up_block_types": [
														        "AttnUpDecoderBlock2D",
														        "UpDecoderBlock2D",
														    ],
														    "vq_embed_dim": 4,
														}






							@property
							def        lowerCamelCase_   (  self						)			->							Tuple:
														torch.manual_seed(0						)
														_UpperCAmelCase							 =		VQModel(**self.dummy_movq_kwargs						)
														return model






							def        lowerCamelCase_   (  self						)			->							str:
														_UpperCAmelCase							 =		self.dummy_unet
														_UpperCAmelCase							 =		self.dummy_movq

														_UpperCAmelCase							 =		DDIMScheduler(
														    num_train_timesteps=1000	,       beta_schedule='linear'	,       beta_start=0.00085	,       beta_end=0.012	,       clip_sample=snake_case	,       set_alpha_to_one=snake_case	,       steps_offset=1	,       prediction_type='epsilon'	,       thresholding=snake_case	,       )

														_UpperCAmelCase							 =		{
														    'unet': unet,
														    'scheduler': scheduler,
														    'movq': movq,
														}

														return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Any:
														_UpperCAmelCase							 =		floats_tensor((1, self.text_embedder_hidden_size)	,       rng=random.Random(snake_case						)						).to(snake_case						)
														_UpperCAmelCase							 =		floats_tensor((1, self.text_embedder_hidden_size)	,       rng=random.Random(seed + 1						)						).to(
														    snake_case						)
														# create init_image
														_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(snake_case						)						).to(snake_case						)
														_UpperCAmelCase							 =		image.cpu().permute(0	,       2	,       3	,       1						)[0]
														_UpperCAmelCase							 =		Image.fromarray(np.uinta(snake_case						)						).convert('RGB'						).resize((256, 256)						)
														# create mask
														_UpperCAmelCase							 =		np.ones((64, 64)	,       dtype=np.floataa						)
														_UpperCAmelCase							 =		0

														if str(snake_case						).startswith('mps'						):
																					_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
														else:
																					_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
														_UpperCAmelCase							 =		{
														    'image': init_image,
														    'mask_image': mask,
														    'image_embeds': image_embeds,
														    'negative_image_embeds': negative_image_embeds,
														    'generator': generator,
														    'height': 64,
														    'width': 64,
														    'num_inference_steps': 2,
														    'guidance_scale': 4.0,
														    'output_type': 'np',
														}
														return inputs






							def        lowerCamelCase_   (  self						)			->							Dict:
														_UpperCAmelCase							 =		'cpu'

														_UpperCAmelCase							 =		self.get_dummy_components()

														_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
														_UpperCAmelCase							 =		pipe.to(snake_case						)

														pipe.set_progress_bar_config(disable=snake_case						)

														_UpperCAmelCase							 =		pipe(**self.get_dummy_inputs(snake_case						)						)
														_UpperCAmelCase							 =		output.images

														_UpperCAmelCase							 =		pipe(
														    **self.get_dummy_inputs(snake_case						)	,       return_dict=snake_case	,       )[0]

														_UpperCAmelCase							 =		image[0, -3:, -3:, -1]
														_UpperCAmelCase							 =		image_from_tuple[0, -3:, -3:, -1]

														print(f'image.shape {image.shape}'						)

														assert image.shape == (1, 64, 64, 3)

														_UpperCAmelCase							 =		np.array(
														    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]						)

														assert (
														    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
														), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
														assert (
														    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
														), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






							def        lowerCamelCase_   (  self						)			->							Tuple:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							List[Any]:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							Any:
														_UpperCAmelCase							 =		load_numpy(
														    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
														    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'						)

														_UpperCAmelCase							 =		load_image(
														    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
														_UpperCAmelCase							 =		np.ones((768, 768)	,       dtype=np.floataa						)
														_UpperCAmelCase							 =		0

														_UpperCAmelCase							 =		'a hat'

														_UpperCAmelCase							 =		KandinskyVaaPriorPipeline.from_pretrained(
														    'kandinsky-community/kandinsky-2-2-prior'	,       torch_dtype=torch.floataa						)
														pipe_prior.to(snake_case						)

														_UpperCAmelCase							 =		KandinskyVaaInpaintPipeline.from_pretrained(
														    'kandinsky-community/kandinsky-2-2-decoder-inpaint'	,       torch_dtype=torch.floataa						)
														_UpperCAmelCase							 =		pipeline.to(snake_case						)
														pipeline.set_progress_bar_config(disable=snake_case						)

														_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
														_UpperCAmelCase ,					_UpperCAmelCase							 =		pipe_prior(
														    snake_case	,       generator=snake_case	,       num_inference_steps=5	,       negative_prompt=''	,       ).to_tuple()

														_UpperCAmelCase							 =		pipeline(
														    image=snake_case	,       mask_image=snake_case	,       image_embeds=snake_case	,       negative_image_embeds=snake_case	,       generator=snake_case	,       num_inference_steps=100	,       height=768	,       width=768	,       output_type='np'	,       )

														_UpperCAmelCase							 =		output.images[0]

														assert image.shape == (768, 768, 3)

														assert_mean_pixel_difference(snake_case	,       snake_case						)







"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

lowercase  							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_UpperCAmelCase							 =		[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(snake_case						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(snake_case						)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(snake_case						).exists()
											os.remove(Path(snake_case						)						)


