


"""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 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=KandinskyInpaintPipeline
		lowerCamelCase__				=['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		lowerCamelCase__				=[
		    'prompt',
		    'negative_prompt',
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		lowerCamelCase__				=[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		lowerCamelCase__				=False



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.time_input_dim



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.time_input_dim * 4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1_00



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
			return tokenizer



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Any						=				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				,       )

			__snake_case	:						List[Any]						=				MultilingualCLIP(a_					)
			__snake_case	:						Any						=				text_encoder.eval()

			return text_encoder



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)

			__snake_case	:						Union[str, Any]						=				{
			    '''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,
			}

			__snake_case	:						Union[str, Any]						=				UNetaDConditionModel(**a_					)
			return model



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Tuple						=				VQModel(**self.dummy_movq_kwargs					)
			return model



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self.dummy_text_encoder
			__snake_case	:						Union[str, Any]						=				self.dummy_tokenizer
			__snake_case	:						Union[str, Any]						=				self.dummy_unet
			__snake_case	:						List[str]						=				self.dummy_movq

			__snake_case	:						Optional[int]						=				DDIMScheduler(
			    num_train_timesteps=10_00				,       beta_schedule='''linear'''				,       beta_start=0.0_0085				,       beta_end=0.012				,       clip_sample=a_				,       set_alpha_to_one=a_				,       steps_offset=1				,       prediction_type='''epsilon'''				,       thresholding=a_				,       )

			__snake_case	:						Tuple						=				{
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''unet''': unet,
			    '''scheduler''': scheduler,
			    '''movq''': movq,
			}

			return components



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=0					):





			'''simple docstring'''

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

			if str(a_					).startswith('''mps'''					):
				__snake_case	:						Optional[Any]						=				torch.manual_seed(a_					)
			else:
				__snake_case	:						Any						=				torch.Generator(device=a_					).manual_seed(a_					)
			__snake_case	:						Any						=				{
			    '''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				'''cpu'''

			__snake_case	:						List[Any]						=				self.get_dummy_components()

			__snake_case	:						List[Any]						=				self.pipeline_class(**a_					)
			__snake_case	:						Optional[Any]						=				pipe.to(a_					)

			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				pipe(**self.get_dummy_inputs(a_					)					)
			__snake_case	:						Any						=				output.images

			__snake_case	:						str						=				pipe(
			    **self.get_dummy_inputs(a_					)				,       return_dict=a_				,       )[0]

			__snake_case	:						str						=				image[0, -3:, -3:, -1]
			__snake_case	:						Union[str, Any]						=				image_from_tuple[0, -3:, -3:, -1]

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

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

			__snake_case	:						Dict						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

			__snake_case	:						int						=				load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
			__snake_case	:						Any						=				np.ones((7_68, 7_68)				,       dtype=np.floataa					)
			__snake_case	:						Optional[Any]						=				0

			__snake_case	:						Tuple						=				'''a hat'''

			__snake_case	:						Union[str, Any]						=				KandinskyPriorPipeline.from_pretrained(
			    '''kandinsky-community/kandinsky-2-1-prior'''				,       torch_dtype=torch.floataa					)
			pipe_prior.to(a_					)

			__snake_case	:						List[Any]						=				KandinskyInpaintPipeline.from_pretrained(
			    '''kandinsky-community/kandinsky-2-1-inpaint'''				,       torch_dtype=torch.floataa					)
			__snake_case	:						List[str]						=				pipeline.to(a_					)
			pipeline.set_progress_bar_config(disable=a_					)

			__snake_case	:						List[Any]						=				torch.Generator(device='''cpu'''					).manual_seed(0					)
			__snake_case	,							__snake_case	:						List[Any]						=				pipe_prior(
			    a_				,       generator=a_				,       num_inference_steps=5				,       negative_prompt=''''''				,       ).to_tuple()

			__snake_case	:						Dict						=				pipeline(
			    a_				,       image=a_				,       mask_image=a_				,       image_embeds=a_				,       negative_image_embeds=a_				,       generator=a_				,       num_inference_steps=1_00				,       height=7_68				,       width=7_68				,       output_type='''np'''				,       )

			__snake_case	:						Union[str, Any]						=				output.images[0]

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

			assert_mean_pixel_difference(a_				,       a_					)








"""simple docstring"""





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )						,     )

	if subparsers is not None:
		parser.set_defaults(func=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




