



"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							KandinskyInpaintPipeline
						__UpperCamelCase		    =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						__UpperCamelCase		    =							[
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						__UpperCamelCase		    =							[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						__UpperCamelCase		    =							False






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






						@property
						def      UpperCAmelCase__					( self			:str )    ->      List[str]:
												return 32






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






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												return self.time_input_dim * 4






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[Any]:
												return 1_00






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
												return tokenizer






						@property
						def      UpperCAmelCase__					( self			:Any )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			MCLIPConfig(
												    numDims=self.cross_attention_dim       ,  transformerDimensions=self.text_embedder_hidden_size       ,  hidden_size=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  vocab_size=10_05       ,  )

												UpperCAmelCase					=			MultilingualCLIP(lowercase_ )
												UpperCAmelCase					=			text_encoder.eval()

												return text_encoder






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Optional[int]:
												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': 'text_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': 'text_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(**lowercase_ )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												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      UpperCAmelCase__					( self			:Optional[Any] )    ->      Optional[int]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			VQModel(**self.dummy_movq_kwargs )
												return model






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[Any]:
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			self.dummy_tokenizer
												UpperCAmelCase					=			self.dummy_unet
												UpperCAmelCase					=			self.dummy_movq

												UpperCAmelCase					=			DDIMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='linear'       ,  beta_start=0.0_0085       ,  beta_end=0.012       ,  clip_sample=lowercase_       ,  set_alpha_to_one=lowercase_       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=lowercase_       ,  )

												UpperCAmelCase					=			{
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:List[str]       ,  lowercase_			:List[Any]=0 )    ->      Dict:
												UpperCAmelCase					=			floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(lowercase_ ) ).to(lowercase_ )
												UpperCAmelCase					=			floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(seed + 1 ) ).to(lowercase_ )
												# create init_image
												UpperCAmelCase					=			floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(lowercase_ ) ).to(lowercase_ )
												UpperCAmelCase					=			image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((2_56, 2_56) )
												# create mask
												UpperCAmelCase					=			np.ones((64, 64)       ,  dtype=np.floataa )
												UpperCAmelCase					=			0

												if str(lowercase_ ).startswith('mps' ):
																		UpperCAmelCase					=			torch.manual_seed(lowercase_ )
												else:
																		UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
												UpperCAmelCase					=			{
												    'prompt': 'horse',
												    '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      UpperCAmelCase__					( self			:List[str] )    ->      List[str]:
												UpperCAmelCase					=			'cpu'

												UpperCAmelCase					=			self.get_dummy_components()

												UpperCAmelCase					=			self.pipeline_class(**lowercase_ )
												UpperCAmelCase					=			pipe.to(lowercase_ )

												pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			pipe(**self.get_dummy_inputs(lowercase_ ) )
												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			pipe(
												    **self.get_dummy_inputs(lowercase_ )       ,  return_dict=lowercase_       ,  )[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(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )

												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      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:int )    ->      str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[int]:
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinsky/kandinsky_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((7_68, 7_68)       ,  dtype=np.floataa )
												UpperCAmelCase					=			0

												UpperCAmelCase					=			'a hat'

												UpperCAmelCase					=			KandinskyPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-1-prior'       ,  torch_dtype=torch.floataa )
												pipe_prior.to(lowercase_ )

												UpperCAmelCase					=			KandinskyInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-1-inpaint'       ,  torch_dtype=torch.floataa )
												UpperCAmelCase					=			pipeline.to(lowercase_ )
												pipeline.set_progress_bar_config(disable=lowercase_ )

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

												UpperCAmelCase					=			pipeline(
												    lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  image_embeds=lowercase_       ,  negative_image_embeds=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type='np'       ,  )

												UpperCAmelCase					=			output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(lowercase_       ,  lowercase_ )










"""simple docstring"""


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			ArgumentParser(
						    description=(
						        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
						    )						)

						# Optional arguments for the launch helper
						parser.add_argument('--num_cores'		,     type=lowercase_		,     default=1		,     help='Number of TPU cores to use (1 or 8).'						)

						# positional
						parser.add_argument(
						    'training_script'		,     type=lowercase_		,     help=(
						        'The full path to the single TPU training '
						        'program/script to be launched in parallel, '
						        'followed by all the arguments for the '
						        'training script'
						    )		,     )

						# rest from the training program
						parser.add_argument('training_script_args'		,     nargs=lowercase_						)

						return parser.parse_args()

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			parse_args()

						# Import training_script as a module.
						UpperCAmelCase					=			Path(args.training_script						)
						sys.path.append(str(script_fpath.parent.resolve()						)						)
						UpperCAmelCase					=			script_fpath.stem
						UpperCAmelCase					=			importlib.import_module(lowercase_						)

						# Patch sys.argv
						UpperCAmelCase					=			[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores						)]

						xmp.spawn(mod._mp_fn		,     args=()		,     nprocs=args.num_cores						)


if __name__ == "__main__":
				main()





