





from typing import Any




def 	lowerCAmelCase_					(						__UpperCAmelCase:    list	,     __UpperCAmelCase:    list	,     __UpperCAmelCase:    dict	,     __UpperCAmelCase:    dict	,     __UpperCAmelCase:    dict	,     )   ->			list:
    _validation(
        __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     )
    # Creates data structures and fill initial step
    UpperCamelCase__							:       dict      =   {}
    UpperCamelCase__							:       dict      =   {}
    for state in states_space:
        UpperCamelCase__							:       Optional[int]      =   observations_space[0]
        UpperCamelCase__							:       Dict      =   (
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        UpperCamelCase__							:       Dict      =   None

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

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

            UpperCamelCase__							:       int      =   arg_max

    # The final observation
    UpperCamelCase__							:       Any      =   observations_space[len(__UpperCAmelCase   ) - 1]

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

    # Process pointers backwards
    UpperCamelCase__							:       Union[str, Any]      =   last_state
    UpperCamelCase__							:       List[str]      =   []
    for o in range(len(__UpperCAmelCase   ) - 1	,     -1	,     -1   ):
        result.append(__UpperCAmelCase   )
        UpperCamelCase__							:       List[str]      =   pointers[previous, observations_space[o]]
    result.reverse()

    return result




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any	,     )   ->			None:
    _validate_not_empty(
        __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     )
    _validate_lists(__UpperCAmelCase	,     __UpperCAmelCase   )
    _validate_dicts(
        __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )




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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any   )   ->			None:
    _validate_list(__UpperCAmelCase	,     '''observations_space'''   )
    _validate_list(__UpperCAmelCase	,     '''states_space'''   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    str   )   ->			None:
    if not isinstance(_object	,     __UpperCAmelCase   ):
        UpperCamelCase__							:       Dict      =   f"{var_name} must be a list"
        raise ValueError(__UpperCAmelCase   )
    else:
        for x in _object:
            if not isinstance(__UpperCAmelCase	,     __UpperCAmelCase   ):
                UpperCamelCase__							:       Any      =   f"{var_name} must be a list of strings"
                raise ValueError(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any	,     )   ->			None:
    _validate_dict(__UpperCAmelCase	,     '''initial_probabilities'''	,     __UpperCAmelCase   )
    _validate_nested_dict(__UpperCAmelCase	,     '''transition_probabilities'''   )
    _validate_nested_dict(__UpperCAmelCase	,     '''emission_probabilities'''   )




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




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


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

  testmod()








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Optional[int]:
    # Initialise PyTorch model.
    # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
    # TapasConfig to False.

    # initialize configuration from json file
    UpperCamelCase__							:       Any      =   TapasConfig.from_json_file(__UpperCAmelCase   )
    # set absolute/relative position embeddings parameter
    UpperCamelCase__							:       Optional[Any]      =   reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        UpperCamelCase__							:       List[Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   True
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   0.664694
        UpperCamelCase__							:       Union[str, Any]      =   0.207951
        UpperCamelCase__							:       Any      =   0.121194
        UpperCamelCase__							:       int      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   False
        UpperCamelCase__							:       int      =   0.0352513

        UpperCamelCase__							:       Dict      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   False
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   36.4519
        UpperCamelCase__							:       int      =   0.903421
        UpperCamelCase__							:       List[str]      =   222.088
        UpperCamelCase__							:       Dict      =   True
        UpperCamelCase__							:       List[str]      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       int      =   0.763141

        UpperCamelCase__							:       Union[str, Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "TABFACT":
        UpperCamelCase__							:       List[Any]      =   TapasForSequenceClassification(config=__UpperCAmelCase   )
    elif task == "MLM":
        UpperCamelCase__							:       Optional[Any]      =   TapasForMaskedLM(config=__UpperCAmelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCamelCase__							:       str      =   TapasModel(config=__UpperCAmelCase   )
    else:
        raise ValueError(f"Task {task} not supported."   )

    print(f"Building PyTorch model from configuration: {config}"   )
    # Load weights from tf checkpoint
    load_tf_weights_in_tapas(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    # Save pytorch-model (weights and configuration)
    print(f"Save PyTorch model to {pytorch_dump_path}"   )
    model.save_pretrained(__UpperCAmelCase   )

    # Save tokenizer files
    print(f"Save tokenizer files to {pytorch_dump_path}"   )
    UpperCamelCase__							:       str      =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''	,     model_max_length=512   )
    tokenizer.save_pretrained(__UpperCAmelCase   )

    print('''Used relative position embeddings:'''	,     model.config.reset_position_index_per_cell   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )

