


"""simple docstring"""



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__snake_case         =					logging.get_logger(__name__)


__snake_case         =					{name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						List[str]				)			->		Optional[Any]:


    """simple docstring"""





    if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
        raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()				)}.'				)

    if tokenizer_name is None:
        snake_case					:   Optional[int]         =					TOKENIZER_CLASSES
    else:
        snake_case					:   List[str]         =					{tokenizer_name: getattr(lowercase		,							tokenizer_name + "Fast"				)}

    logger.info(F'Loading tokenizer classes: {tokenizer_names}'				)

    for tokenizer_name in tokenizer_names:
        snake_case					:   Any         =					TOKENIZER_CLASSES[tokenizer_name]

        snake_case					:   Union[str, Any]         =					True
        if checkpoint_name is None:
            snake_case					:   Optional[int]         =					list(tokenizer_class.max_model_input_sizes.keys()				)
        else:
            snake_case					:   Tuple         =					[checkpoint_name]

        logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'				)

        for checkpoint in checkpoint_names:
            logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}'				)

            # Load tokenizer
            snake_case					:   str         =					tokenizer_class.from_pretrained(lowercase		,							force_download=lowercase				)

            # Save fast tokenizer
            logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'				)

            # For organization names we create sub-directories
            if "/" in checkpoint:
                snake_case					:   List[Any]         =					checkpoint.split("/"				)
                snake_case					:   List[Any]         =					os.path.join(lowercase		,							lowercase				)
            elif add_prefix:
                snake_case					:   Tuple         =					checkpoint
                snake_case					:   Dict         =					dump_path
            else:
                snake_case					:   Optional[Any]         =					None
                snake_case					:   List[str]         =					dump_path

            logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'				)

            if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()				)[0]:
                snake_case					:   int         =					list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                snake_case					:   Any         =					file_path.split(lowercase				)[-1][0]
                if next_char == "/":
                    snake_case					:   List[Any]         =					os.path.join(lowercase		,							lowercase				)
                    snake_case					:   Optional[int]         =					None

                logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'				)

            snake_case					:   Dict         =					tokenizer.save_pretrained(
                lowercase		,							legacy_format=lowercase		,							filename_prefix=lowercase				)
            logger.info(F'=> File names {file_names}'				)

            for file_name in file_names:
                if not file_name.endswith("tokenizer.json"				):
                    os.remove(lowercase				)
                    logger.info(F'=> removing {file_name}'				)


if __name__ == "__main__":
  __snake_case         =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
  )
  parser.add_argument(
      """--tokenizer_name""",
      default=None,
      type=str,
      help=(
          F'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
          """download and convert all the checkpoints from AWS."""
      ),
  )
  parser.add_argument(
      """--checkpoint_name""",
      default=None,
      type=str,
      help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
  )
  parser.add_argument(
      """--force_download""",
      action="""store_true""",
      help="""Re-download checkpoints.""",
  )
  __snake_case         =					parser.parse_args()

  convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)









"""simple docstring"""



from typing import Any







def 						__lowerCAmelCase		(		lowercase						:						list		,							lowercase						:						list		,							lowercase						:						dict		,							lowercase						:						dict		,							lowercase						:						dict		,							)			->		list:


    """simple docstring"""





    _validation(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    # Creates data structures and fill initial step
    snake_case					:   dict         =					{}
    snake_case					:   dict         =					{}
    for state in states_space:
        snake_case					:   int         =					observations_space[0]
        snake_case					:   Any         =					(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case					:   Union[str, Any]         =					None

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

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

            snake_case					:   List[Any]         =					arg_max

    # The final observation
    snake_case					:   Dict         =					observations_space[len(lowercase				) - 1]

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

    # Process pointers backwards
    snake_case					:   List[str]         =					last_state
    snake_case					:   Optional[int]         =					[]
    for o in range(len(lowercase				) - 1		,							-1		,							-1				):
        result.append(lowercase				)
        snake_case					:   List[str]         =					pointers[previous, observations_space[o]]
    result.reverse()

    return result







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_not_empty(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    _validate_lists(lowercase		,							lowercase				)
    _validate_dicts(
        lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]				):
        raise ValueError("There's an empty parameter"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any				)			->		None:


    """simple docstring"""





    _validate_list(lowercase		,							"observations_space"				)
    _validate_list(lowercase		,							"states_space"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   List[str]         =					F'{var_name} must be a list'
        raise ValueError(lowercase				)
    else:
        for x in _object:
            if not isinstance(lowercase		,							lowercase				):
                snake_case					:   Tuple         =					F'{var_name} must be a list of strings'
                raise ValueError(lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_dict(lowercase		,							"initial_probabilities"		,							lowercase				)
    _validate_nested_dict(lowercase		,							"transition_probabilities"				)
    _validate_nested_dict(lowercase		,							"emission_probabilities"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    _validate_dict(_object		,							lowercase		,							lowercase				)
    for x in _object.values():
        _validate_dict(lowercase		,							lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str		,							lowercase						:						type		,							lowercase						:						bool = False				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   int         =					F'{var_name} must be a dict'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object				):
        snake_case					:   Optional[Any]         =					F'{var_name} all keys must be strings'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object.values()				):
        snake_case					:   Optional[int]         =					"nested dictionary " if nested else ""
        snake_case					:   int         =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
        raise ValueError(lowercase				)


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

  testmod()





