from dataclasses import dataclass, field
from typing import Optional

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





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






def    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							HfArgumentParser((ModelArguments,)		)
						(SCREAMING_SNAKE_CASE						)							:					int           =							parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

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

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

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

						SCREAMING_SNAKE_CASE							:					List[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=_lowercase	,						decoder_config=_lowercase	,						)

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

						# This is necessary to make Flax's generate() work
						SCREAMING_SNAKE_CASE							:					List[str]           =							decoder_config.eos_token_id
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							decoder_start_token_id
						SCREAMING_SNAKE_CASE							:					Tuple           =							pad_token_id

						SCREAMING_SNAKE_CASE							:					int           =							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path		)

						SCREAMING_SNAKE_CASE							:					Tuple           =							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							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()

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int = 1000					,	UpperCamelCase__			:       Optional[Union[np.ndarray, List[float]]] = None		):





												'''simple docstring'''





												self.set_timesteps(UpperCamelCase__		)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					str           =							1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												SCREAMING_SNAKE_CASE							:					Tuple           =							4

												# running values
												SCREAMING_SNAKE_CASE							:					int           =							[]


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, torch.device] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.linspace(1					,	0					,	num_inference_steps + 1		)[:-1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cat([steps, torch.tensor([0.0]		)]		)

												if self.config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(self.config.trained_betas					,	dtype=torch.floataa		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sin(steps * math.pi / 2		) ** 2

												SCREAMING_SNAKE_CASE							:					Dict           =							(1.0 - self.betas**2) ** 0.5

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(torch.atana(self.betas					,	self.alphas		) / math.pi * 2)[:-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							timesteps.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       bool = True					,	):





												'''simple docstring'''





												if self.num_inference_steps is None:
																		raise ValueError(
																		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.timesteps == timestep).nonzero().item()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timestep_index + 1

												SCREAMING_SNAKE_CASE							:					int           =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(UpperCamelCase__		)

												if len(self.ets		) == 1:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.ets[-1]
												elif len(self.ets		) == 2:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets		) == 3:
																		SCREAMING_SNAKE_CASE							:					str           =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._get_prev_sample(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       torch.FloatTensor					,	*UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return sample


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.alphas[timestep_index]
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.betas[timestep_index]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.alphas[prev_timestep_index]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.betas[prev_timestep_index]

												SCREAMING_SNAKE_CASE							:					Dict           =							(sample - sigma * ets) / max(UpperCamelCase__					,	1E-8		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							next_alpha * pred + ets * next_sigma

												return prev_sample



						def __len__( self			:       int		):





												'''simple docstring'''





												return self.config.num_train_timesteps





