


from typing import Any


def    __lowerCamelCase   (      A__						: list						,						A__						: list						,						A__						: dict						,						A__						: dict						,						A__						: dict						,						)     ->    list:
   _validation(
       A__						,						A__						,						A__						,						A__						,						A__						,						)
   # Creates data structures and fill initial step
   lowerCamelCase_						:   dict		= {}
   lowerCamelCase_						:   dict		= {}
   for state in states_space:
      lowerCamelCase_						:   List[Any]		= observations_space[0]
      lowerCamelCase_						:   Tuple		= (
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      lowerCamelCase_						:   Optional[int]		= None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1						,						len(A__					)					):
      lowerCamelCase_						:   List[Any]		= observations_space[o]
      lowerCamelCase_						:   str		= observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         lowerCamelCase_						:   Any		= """"""
         lowerCamelCase_						:   Tuple		= -1
         for k_state in states_space:
            lowerCamelCase_						:   List[Any]		= (
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               lowerCamelCase_						:   int		= probability
               lowerCamelCase_						:   Any		= k_state

            # Update probabilities and pointers dicts
         lowerCamelCase_						:   Union[str, Any]		= (
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         lowerCamelCase_						:   Any		= arg_max

    # The final observation
   lowerCamelCase_						:   int		= observations_space[len(A__					) - 1]

   # argmax for given final observation
   lowerCamelCase_						:   Optional[int]		= """"""
   lowerCamelCase_						:   Tuple		= -1
   for k_state in states_space:
      lowerCamelCase_						:   List[Any]		= probabilities[(k_state, final_observation)]
      if probability > max_probability:
         lowerCamelCase_						:   Dict		= probability
         lowerCamelCase_						:   int		= k_state
   lowerCamelCase_						:   List[str]		= arg_max

   # Process pointers backwards
   lowerCamelCase_						:   Any		= last_state
   lowerCamelCase_						:   Optional[Any]		= []
   for o in range(len(A__					) - 1						,						-1						,						-1					):
      result.append(A__					)
      lowerCamelCase_						:   List[Any]		= pointers[previous, observations_space[o]]
   result.reverse()

   return result


def    __lowerCamelCase   (      A__						: Any						,						A__						: Any						,						A__						: Any						,						A__						: Any						,						A__						: Any						,						)     ->    None:
   _validate_not_empty(
       A__						,						A__						,						A__						,						A__						,						A__						,						)
   _validate_lists(A__						,						A__					)
   _validate_dicts(
       A__						,						A__						,						A__					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: Any						,						A__						: Any						,						A__						: Any						,						A__						: Any						,						)     ->    None:
   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]					):
      raise ValueError("""There's an empty parameter"""					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: Any					)     ->    None:
   _validate_list(A__						,						"""observations_space"""					)
   _validate_list(A__						,						"""states_space"""					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: str					)     ->    None:
   if not isinstance(_object						,						A__					):
      lowerCamelCase_						:   List[Any]		= f'''{var_name} must be a list'''
      raise ValueError(A__					)
   else:
      for x in _object:
         if not isinstance(A__						,						A__					):
            lowerCamelCase_						:   int		= f'''{var_name} must be a list of strings'''
            raise ValueError(A__					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: Any						,						A__						: Any						,						)     ->    None:
   _validate_dict(A__						,						"""initial_probabilities"""						,						A__					)
   _validate_nested_dict(A__						,						"""transition_probabilities"""					)
   _validate_nested_dict(A__						,						"""emission_probabilities"""					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: str					)     ->    None:
   _validate_dict(_object						,						A__						,						A__					)
   for x in _object.values():
      _validate_dict(A__						,						A__						,						A__						,						A__					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: str						,						A__						: type						,						A__						: bool = False					)     ->    None:
   if not isinstance(_object						,						A__					):
      lowerCamelCase_						:   Optional[Any]		= f'''{var_name} must be a dict'''
      raise ValueError(A__					)
   if not all(isinstance(A__						,						A__					) for x in _object					):
      lowerCamelCase_						:   List[Any]		= f'''{var_name} all keys must be strings'''
      raise ValueError(A__					)
   if not all(isinstance(A__						,						A__					) for x in _object.values()					):
      lowerCamelCase_						:   Dict		= """nested dictionary """ if nested else """"""
      lowerCamelCase_						:   Optional[Any]		= f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
      raise ValueError(A__					)


if __name__ == "__main__":
      from doctest import testmod

      testmod()









from dataclasses import dataclass, field
from typing import Optional

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





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



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







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

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

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

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

   lowerCamelCase_						:   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=A__						,						decoder_config=A__						,						)

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

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

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= 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()





