



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCAmelCase__		:					str							=logging.get_logger(__name__)

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Any      ='''linear'''
						UpperCamelCase__      :			Any      ='''cosine'''
						UpperCamelCase__      :			Optional[int]      ='''cosine_with_restarts'''
						UpperCamelCase__      :			Union[str, Any]      ='''polynomial'''
						UpperCamelCase__      :			List[str]      ='''constant'''
						UpperCamelCase__      :			str      ='''constant_with_warmup'''
						UpperCamelCase__      :			Dict      ='''piecewise_constant'''







def      __lowercase       (			a__							,       a__ = -1       )    ->			Optional[Any]:
							return LambdaLR(a__							,       lambda a__       : 1							,       last_epoch=a__       )






def      __lowercase       (			a__							,       a__							,       a__ = -1       )    ->			List[Any]:

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1.0							,       a__       )       )
														return 1.0

							return LambdaLR(a__							,       a__							,       last_epoch=a__       )






def      __lowercase       (			a__							,       a__							,       a__ = -1       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= step_rules.split(','       )
							for rule_str in rule_list[:-1]:
														__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= rule_str.split(':'       )
														__SCREAMING_SNAKE_CASE									= int(a__       )
														__SCREAMING_SNAKE_CASE									= float(a__       )
														__SCREAMING_SNAKE_CASE									= value
							__SCREAMING_SNAKE_CASE									= float(rule_list[-1]       )

							def create_rules_function(a__							,       a__       ):
														def rule_func(a__       ) -> float:
																					__SCREAMING_SNAKE_CASE									= sorted(rules_dict.keys()       )
																					for i, sorted_step in enumerate(a__       ):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							__SCREAMING_SNAKE_CASE									= create_rules_function(a__							,       a__       )

							return LambdaLR(a__							,       a__							,       last_epoch=a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__=-1       )    ->			Optional[Any]:

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1							,       a__       )       )
														return max(
														    0.0							,       float(num_training_steps - current_step       ) / float(max(1							,       num_training_steps - num_warmup_steps       )       )       )

							return LambdaLR(a__							,       a__							,       a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__ = 0.5							,       a__ = -1       )    ->			Optional[Any]:

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1							,       a__       )       )
														__SCREAMING_SNAKE_CASE									= float(current_step - num_warmup_steps       ) / float(max(1							,       num_training_steps - num_warmup_steps       )       )
														return max(0.0							,       0.5 * (1.0 + math.cos(math.pi * float(a__       ) * 2.0 * progress       ))       )

							return LambdaLR(a__							,       a__							,       a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__ = 1							,       a__ = -1       )    ->			Union[str, Any]:

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1							,       a__       )       )
														__SCREAMING_SNAKE_CASE									= float(current_step - num_warmup_steps       ) / float(max(1							,       num_training_steps - num_warmup_steps       )       )
														if progress >= 1.0:
																					return 0.0
														return max(0.0							,       0.5 * (1.0 + math.cos(math.pi * ((float(a__       ) * progress) % 1.0)       ))       )

							return LambdaLR(a__							,       a__							,       a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__=1E-7							,       a__=1.0							,       a__=-1       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= optimizer.defaults['lr']
							if not (lr_init > lr_end):
														raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""       )

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1							,       a__       )       )
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					__SCREAMING_SNAKE_CASE									= lr_init - lr_end
																					__SCREAMING_SNAKE_CASE									= num_training_steps - num_warmup_steps
																					__SCREAMING_SNAKE_CASE									= 1 - (current_step - num_warmup_steps) / decay_steps
																					__SCREAMING_SNAKE_CASE									= lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(a__							,       a__							,       a__       )


lowerCAmelCase__		:					Dict							={
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def      __lowercase       (			a__							,       a__							,       a__ = None							,       a__ = None							,       a__ = None							,       a__ = 1							,       a__ = 1.0							,       a__ = -1							,       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= SchedulerType(a__       )
							__SCREAMING_SNAKE_CASE									= TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(a__							,       last_epoch=a__       )

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(a__							,       step_rules=a__							,       last_epoch=a__       )

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""       )

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(a__							,       num_warmup_steps=a__							,       last_epoch=a__       )

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""       )

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    a__							,       num_warmup_steps=a__							,       num_training_steps=a__							,       num_cycles=a__							,       last_epoch=a__							,       )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    a__							,       num_warmup_steps=a__							,       num_training_steps=a__							,       power=a__							,       last_epoch=a__							,       )

							return schedule_func(
							    a__							,       num_warmup_steps=a__							,       num_training_steps=a__							,       last_epoch=a__       )











from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




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







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

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

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

							__SCREAMING_SNAKE_CASE									= 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
							__SCREAMING_SNAKE_CASE									= decoder_config.decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= decoder_config.pad_token_id
							if decoder_start_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.bos_token_id
							if pad_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id

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

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

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






