




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					(DDIMParallelScheduler,)
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(("""eta""", 0.0), ("""num_inference_steps""", 50))





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      **__lowerCamelCase				:     List[str]							)					->     Dict:
							a    =			{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0_001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(      self				:     Any					,      **__lowerCamelCase				:     Union[str, Any]							)					->     Optional[int]:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config(**__lowerCamelCase							)
							a    =			scheduler_class(**__lowerCamelCase							)

							a    ,		a    =			10, 0.0

							a    =			self.dummy_model()
							a    =			self.dummy_sample_deter

							scheduler.set_timesteps(__lowerCamelCase							)

							for t in scheduler.timesteps:
										a    =			model(__lowerCamelCase					,      __lowerCamelCase							)
										a    =			scheduler.step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							).prev_sample

							return sample





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							for timesteps in [1_00, 5_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     Union[str, Any]:
							for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=__lowerCamelCase							)

							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config(steps_offset=1							)
							a    =			scheduler_class(**__lowerCamelCase							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps					,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]							)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Optional[Any]:
							for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,      [0.002, 0.02, 0.2, 2]							):
										self.check_over_configs(beta_start=__lowerCamelCase					,      beta_end=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Optional[int]:
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Union[str, Any]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Tuple:
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							for timestep_spacing in ["trailing", "leading"]:
										self.check_over_configs(timestep_spacing=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     Tuple:
							for rescale_betas_zero_snr in [True, False]:
										self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[int]:
							self.check_over_configs(thresholding=__lowerCamelCase							)
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=__lowerCamelCase					,      prediction_type=__lowerCamelCase					,      sample_max_value=__lowerCamelCase					,      )





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[int]:
							for t in [1, 10, 49]:
										self.check_over_forward(time_step=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[Any]:
							for t, num_inference_steps in zip([1, 10, 50]					,      [10, 50, 5_00]							):
										self.check_over_forward(time_step=__lowerCamelCase					,      num_inference_steps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Dict:
							for t, eta in zip([1, 10, 49]					,      [0.0, 0.5, 1.0]							):
										self.check_over_forward(time_step=__lowerCamelCase					,      eta=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     str:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							assert torch.sum(torch.abs(scheduler._get_variance(0					,      0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_20					,      4_00							) - 0.14_771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_80					,      9_60							) - 0.32_460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0					,      0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87					,      4_86							) - 0.00_979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99					,      9_98							) - 0.02							)							) < 1e-5





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    ,		a    =			10, 0.0
							scheduler.set_timesteps(__lowerCamelCase							)

							a    =			self.dummy_model()
							a    =			self.dummy_sample_deter
							a    =			self.dummy_sample_deter + 0.1
							a    =			self.dummy_sample_deter - 0.1

							a    =			samplea.shape[0]
							a    =			torch.stack([samplea, samplea, samplea]					,      dim=0							)
							a    =			torch.arange(__lowerCamelCase							)[0:3, None].repeat(1					,      __lowerCamelCase							)

							a    =			model(samples.flatten(0					,      1							)					,      timesteps.flatten(0					,      1							)							)
							a    =			scheduler.batch_step_no_noise(__lowerCamelCase					,      timesteps.flatten(0					,      1							)					,      samples.flatten(0					,      1							)					,      __lowerCamelCase							)

							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 1_147.7_904							) < 1e-2
							assert abs(result_mean.item() - 0.4_982							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     int							)					->     Union[str, Any]:
							a    =			self.full_loop()

							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 172.0_067							) < 1e-2
							assert abs(result_mean.item() - 0.223_967							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							a    =			self.full_loop(prediction_type="v_prediction"							)

							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 52.5_302							) < 1e-2
							assert abs(result_mean.item() - 0.0_684							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							# We specify different beta, so that the first alpha is 0.99
							a    =			self.full_loop(set_alpha_to_one=__lowerCamelCase					,      beta_start=0.01							)
							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 149.8_295							) < 1e-2
							assert abs(result_mean.item() - 0.1_951							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     int							)					->     Optional[int]:
							# We specify different beta, so that the first alpha is 0.99
							a    =			self.full_loop(set_alpha_to_one=__lowerCamelCase					,      beta_start=0.01							)
							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 149.0_784							) < 1e-2
							assert abs(result_mean.item() - 0.1_941							) < 1e-3












from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class     snake_case__							:
				"""simple docstring"""



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




def 			__magic_name__				( ):






			'''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=A,  decoder_config=A,  )

			# 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()






