




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

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







			__lowerCamelCase        =       (IPNDMScheduler,)
			__lowerCamelCase        =       (('num_inference_steps', 50),)


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						Dict:
							'''simple docstring'''






							A__        =			{"""num_train_timesteps""": 1000}
							config.update(**lowercase_       )
							return config


			def      UpperCamelCase		(    self      ,      lowercase=0      ,      **lowercase       )  ->						int:
							'''simple docstring'''






							A__        =			dict(self.forward_default_kwargs       )
							A__        =			kwargs.pop("num_inference_steps"      ,      lowercase_       )
							A__        =			self.dummy_sample
							A__        =			0.1 * sample
							A__        =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
											A__        =			self.get_scheduler_config(**lowercase_       )
											A__        =			scheduler_class(**lowercase_       )
											scheduler.set_timesteps(lowercase_       )
											# copy over dummy past residuals
											A__        =			dummy_past_residuals[:]

											if time_step is None:
															A__        =			scheduler.timesteps[len(scheduler.timesteps       ) // 2]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(lowercase_       )
															A__        =			scheduler_class.from_pretrained(lowercase_       )
															new_scheduler.set_timesteps(lowercase_       )
															# copy over dummy past residuals
															A__        =			dummy_past_residuals[:]

											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample
											A__        =			new_scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample

											assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample
											A__        =			new_scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample

											assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self      ,      lowercase=0      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			dict(self.forward_default_kwargs       )
							A__        =			kwargs.pop("num_inference_steps"      ,      lowercase_       )
							A__        =			self.dummy_sample
							A__        =			0.1 * sample
							A__        =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
											A__        =			self.get_scheduler_config()
											A__        =			scheduler_class(**lowercase_       )
											scheduler.set_timesteps(lowercase_       )

											# copy over dummy past residuals (must be after setting timesteps)
											A__        =			dummy_past_residuals[:]

											if time_step is None:
															A__        =			scheduler.timesteps[len(scheduler.timesteps       ) // 2]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(lowercase_       )
															A__        =			scheduler_class.from_pretrained(lowercase_       )
															# copy over dummy past residuals
															new_scheduler.set_timesteps(lowercase_       )

															# copy over dummy past residual (must be after setting timesteps)
															A__        =			dummy_past_residuals[:]

											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample
											A__        =			new_scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample

											assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample
											A__        =			new_scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample

											assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			self.scheduler_classes[0]
							A__        =			self.get_scheduler_config(**lowercase_       )
							A__        =			scheduler_class(**lowercase_       )

							A__        =			10
							A__        =			self.dummy_model()
							A__        =			self.dummy_sample_deter
							scheduler.set_timesteps(lowercase_       )

							for i, t in enumerate(scheduler.timesteps       ):
											A__        =			model(lowercase_      ,      lowercase_       )
											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_       ).prev_sample

							for i, t in enumerate(scheduler.timesteps       ):
											A__        =			model(lowercase_      ,      lowercase_       )
											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_       ).prev_sample

							return sample


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			dict(self.forward_default_kwargs       )

							A__        =			kwargs.pop("num_inference_steps"      ,      lowercase_       )

							for scheduler_class in self.scheduler_classes:
											A__        =			self.get_scheduler_config()
											A__        =			scheduler_class(**lowercase_       )

											A__        =			self.dummy_sample
											A__        =			0.1 * sample

											if num_inference_steps is not None and hasattr(lowercase_      ,      "set_timesteps"       ):
															scheduler.set_timesteps(lowercase_       )
											elif num_inference_steps is not None and not hasattr(lowercase_      ,      "set_timesteps"       ):
															A__        =			num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											A__        =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
											A__        =			dummy_past_residuals[:]

											A__        =			scheduler.timesteps[5]
											A__        =			scheduler.timesteps[6]

											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample
											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample

											self.assertEqual(output_a.shape      ,      sample.shape       )
											self.assertEqual(output_a.shape      ,      output_a.shape       )

											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample
											A__        =			scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      **lowercase_       ).prev_sample

											self.assertEqual(output_a.shape      ,      sample.shape       )
											self.assertEqual(output_a.shape      ,      output_a.shape       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase_      ,      time_step=lowercase_       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							for t, num_inference_steps in zip([1, 5, 10]      ,      [10, 50, 100]       ):
											self.check_over_forward(num_inference_steps=lowercase_      ,      time_step=lowercase_       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			self.full_loop()
							A__        =			torch.mean(torch.abs(lowercase_       )       )

							assert abs(result_mean.item() - 2540529       ) < 10




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()

