



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class      __magic_name__						(   lowerCAmelCase__      ,						unittest.TestCase       ):
							lowerCAmelCase	:   List[str]           = DDIMPipeline
							lowerCAmelCase	:   Dict           = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
							lowerCAmelCase	:   Union[str, Any]           = PipelineTesterMixin.required_optional_params - {
							    "num_images_per_prompt",
							    "latents",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase	:   Tuple           = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase	:   int           = False







							def 							__lowercase      (							self :	List[str]		):
									torch.manual_seed(0		)
									_a					:				Tuple				=							UNetaDModel(
									    block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=3     ,out_channels=3     ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')     ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')     ,)
									_a					:				List[Any]				=							DDIMScheduler()
									_a					:				Dict				=							{'unet': unet, 'scheduler': scheduler}
									return components







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Optional[int]=0		):
									if str(a__		).startswith('mps'		):
											_a					:				Optional[Any]				=							torch.manual_seed(a__		)
									else:
											_a					:				int				=							torch.Generator(device=a__		).manual_seed(a__		)
									_a					:				str				=							{
									    'batch_size': 1,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[str]				=							'cpu'

									_a					:				Any				=							self.get_dummy_components()
									_a					:				Dict				=							self.pipeline_class(**a__		)
									pipe.to(a__		)
									pipe.set_progress_bar_config(disable=a__		)

									_a					:				Dict				=							self.get_dummy_inputs(a__		)
									_a					:				str				=							pipe(**a__		).images
									_a					:				Union[str, Any]				=							image[0, -3:, -3:, -1]

									self.assertEqual(image.shape     ,(1, 32, 32, 3)		)
									_a					:				List[str]				=							np.array(
									    [1.0_00E00, 5.7_17E-01, 4.7_17E-01, 1.0_00E00, 0.0_00E00, 1.0_00E00, 3.0_00E-04, 0.0_00E00, 9.0_00E-04]		)
									_a					:				Any				=							np.abs(image_slice.flatten() - expected_slice		).max()
									self.assertLessEqual(a__     ,1E-3		)







							def 							__lowercase      (							self :	Any		):
									super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)







							def 							__lowercase      (							self :	Dict		):
									super().test_save_load_local(expected_max_difference=3E-3		)







							def 							__lowercase      (							self :	List[Any]		):
									super().test_save_load_optional_components(expected_max_difference=3E-3		)







							def 							__lowercase      (							self :	List[Any]		):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3		)

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







							def 							__lowercase      (							self :	str		):
									_a					:				List[Any]				=							'google/ddpm-cifar10-32'

									_a					:				Tuple				=							UNetaDModel.from_pretrained(a__		)
									_a					:				List[str]				=							DDIMScheduler()

									_a					:				List[Any]				=							DDIMPipeline(unet=a__     ,scheduler=a__		)
									ddim.to(a__		)
									ddim.set_progress_bar_config(disable=a__		)

									_a					:				Optional[Any]				=							torch.manual_seed(0		)
									_a					:				Any				=							ddim(generator=a__     ,eta=0.0     ,output_type='numpy'		).images

									_a					:				List[str]				=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_a					:				int				=							np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]		)

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







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Tuple				=							'google/ddpm-ema-bedroom-256'

									_a					:				int				=							UNetaDModel.from_pretrained(a__		)
									_a					:				Optional[Any]				=							DDIMScheduler.from_pretrained(a__		)

									_a					:				int				=							DDIMPipeline(unet=a__     ,scheduler=a__		)
									ddpm.to(a__		)
									ddpm.set_progress_bar_config(disable=a__		)

									_a					:				Union[str, Any]				=							torch.manual_seed(0		)
									_a					:				str				=							ddpm(generator=a__     ,output_type='numpy'		).images

									_a					:				List[Any]				=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_a					:				Any				=							np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]		)

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









'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   str           = field(
							    metadata={'help': 'The output directory where the model will be written.'}      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The encoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train an encoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The decoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train a decoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Any				=							HfArgumentParser((ModelArguments,)  )
		((_a)	, )					:				Dict				=							parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_config_name  )
		# Use pretrained encoder model's config
		else:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

		# Use explicit specified decoder config
		if model_args.decoder_config_name:
				_a					:				List[str]				=							AutoConfig.from_pretrained(model_args.decoder_config_name  )
		# Use pretrained decoder model's config
		else:
				_a					:				Optional[int]				=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

		# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
		_a					:				List[Any]				=							True
		_a					:				int				=							True

		_a					:				Any				=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
		    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,      encoder_config=lowerCAmelCase_						,      decoder_config=lowerCAmelCase_						,      )

		# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
		_a					:				List[str]				=							decoder_config.decoder_start_token_id
		_a					:				Optional[int]				=							decoder_config.pad_token_id
		if decoder_start_token_id is None:
				_a					:				Tuple				=							decoder_config.bos_token_id
		if pad_token_id is None:
				_a					:				List[Any]				=							decoder_config.eos_token_id

		# This is necessary to make Flax's generate() work
		_a					:				Any				=							decoder_config.eos_token_id
		_a					:				Tuple				=							decoder_start_token_id
		_a					:				Any				=							pad_token_id

		_a					:				Dict				=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

		_a					:				Dict				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
		_a					:				int				=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

		model.save_pretrained(model_args.output_dir  )
		image_processor.save_pretrained(model_args.output_dir  )
		tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
							main()




