

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42


			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=lowercase      ,      scheduler=lowercase       )


			@torch.no_grad()
			def __call__(    self      ,      lowercase = 1      ,      lowercase = 50      ,      lowercase = None      ,      lowercase = "pil"      ,      lowercase = True      ,      **lowercase      ,      )  ->						Union[Tuple, ImagePipelineOutput]:
							'''simple docstring'''






							A__        =			self.unet.config.sample_size
							A__        =			(batch_size, 3, img_size, img_size)

							A__        =			self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							A__        =			randn_tensor(lowercase      ,      generator=lowercase      ,      device=self.device       ) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(lowercase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
											# here sigma_t == t_i from the paper
											A__        =			self.scheduler.schedule[t]
											A__        =			self.scheduler.schedule[t - 1] if t > 0 else 0

											# 1. Select temporarily increased noise level sigma_hat
											# 2. Add new noise to move from sample_i to sample_hat
											A__    ,				A__        =			self.scheduler.add_noise_to_input(lowercase      ,      lowercase      ,      generator=lowercase       )

											# 3. Predict the noise residual given the noise magnitude `sigma_hat`
											# The model inputs and output are adjusted by following eq. (213) in [1].
											A__        =			(sigma_hat / 2) * model((sample_hat + 1) / 2      ,      sigma_hat / 2       ).sample

											# 4. Evaluate dx/dt at sigma_hat
											# 5. Take Euler step from sigma to sigma_prev
											A__        =			self.scheduler.step(lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )

											if sigma_prev != 0:
															# 6. Apply 2nd order correction
															# The model inputs and output are adjusted by following eq. (213) in [1].
															A__        =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,      sigma_prev / 2       ).sample
															A__        =			self.scheduler.step_correct(
															    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      step_output.prev_sample      ,      step_output["derivative"]      ,      )
											A__        =			step_output.prev_sample

							A__        =			(sample / 2 + 0.5).clamp(0      ,      1       )
							A__        =			sample.cpu().permute(0      ,      2      ,      3      ,      1       ).numpy()
							if output_type == "pil":
											A__        =			self.numpy_to_pil(lowercase       )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowercase       )




from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class 					a__						:
			"""simple docstring"""







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



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			HfArgumentParser((ModelArguments,)      )
				((A__)    ,				)        =			parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_config_name      )
				# Use pretrained encoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_config_name      )
				# Use pretrained decoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path      )

				# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
				A__        =			True
				A__        =			True

				A__        =			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=SCREAMING_SNAKE_CASE_	,       decoder_config=SCREAMING_SNAKE_CASE_	,       )

				# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
				A__        =			decoder_config.decoder_start_token_id
				A__        =			decoder_config.pad_token_id
				if decoder_start_token_id is None:
								A__        =			decoder_config.bos_token_id
				if pad_token_id is None:
								A__        =			decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				A__        =			decoder_config.eos_token_id
				A__        =			decoder_start_token_id
				A__        =			pad_token_id

				A__        =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

				A__        =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
				A__        =			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()

