
"""simple docstring"""
from typing import Any





def    _lowerCAmelCase    (     lowerCamelCase__							:							list,						lowerCamelCase__							:							list,						lowerCamelCase__							:							dict,						lowerCamelCase__							:							dict,						lowerCamelCase__							:							dict,						)  ->    list:
  _validation(
      lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						)
  # Creates data structures and fill initial step
  _SCREAMING_SNAKE_CASE   :				dict							    = {}
  _SCREAMING_SNAKE_CASE   :				dict							    = {}
  for state in states_space:
    _SCREAMING_SNAKE_CASE   :				Any							    = observations_space[0]
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = (
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    _SCREAMING_SNAKE_CASE   :				str							    = None

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

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

      _SCREAMING_SNAKE_CASE   :				List[str]							    = arg_max

    # The final observation
  _SCREAMING_SNAKE_CASE   :				Dict							    = observations_space[len(lowerCamelCase__				) - 1]

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

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

  return result





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any,						)  ->    None:
  _validate_not_empty(
      lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						)
  _validate_lists(lowerCamelCase__,						lowerCamelCase__				)
  _validate_dicts(
      lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)





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





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any				)  ->    None:
  _validate_list(lowerCamelCase__,						"observations_space"				)
  _validate_list(lowerCamelCase__,						"states_space"				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							str				)  ->    None:
  if not isinstance(_object,						lowerCamelCase__				):
    _SCREAMING_SNAKE_CASE   :				str							    = f'''{var_name} must be a list'''
    raise ValueError(lowerCamelCase__				)
  else:
    for x in _object:
      if not isinstance(lowerCamelCase__,						lowerCamelCase__				):
        _SCREAMING_SNAKE_CASE   :				Any							    = f'''{var_name} must be a list of strings'''
        raise ValueError(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Any,						)  ->    None:
  _validate_dict(lowerCamelCase__,						"initial_probabilities",						lowerCamelCase__				)
  _validate_nested_dict(lowerCamelCase__,						"transition_probabilities"				)
  _validate_nested_dict(lowerCamelCase__,						"emission_probabilities"				)





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





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


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

      testmod()





"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowercase_       :						List[str]     					=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 			UpperCamelCase      (							datasets.BuilderConfig  ):
     A__      =			10000
     A__      =			None
     A__      =			None
class 			UpperCamelCase      (							datasets.ArrowBasedBuilder  ):
     A__      =			ParquetConfig
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.DatasetInfo(features=self.config.features				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if not self.config.data_files:
         raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = dl_manager.download_and_extract(self.config.data_files				)
       if isinstance(snake_case__  ,     (str, list, tuple)				):
         _SCREAMING_SNAKE_CASE   :				int							    = data_files
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [dl_manager.iter_files(snake_case__				) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,     gen_kwargs={"files": files}				)]
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       for split_name, files in data_files.items():
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [dl_manager.iter_files(snake_case__				) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(snake_case__				):
             with open(snake_case__  ,     "rb"				) as f:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = datasets.Features.from_arrow_schema(pq.read_schema(snake_case__				)				)
             break
         splits.append(datasets.SplitGenerator(name=snake_case__  ,     gen_kwargs={"files": files}				)				)
       return splits
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = table_cast(snake_case__  ,     self.info.features.arrow_schema				)
       return pa_table




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema				) != sorted(self.config.columns				):
           raise ValueError(
               F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''				)
       for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
         with open(snake_case__  ,     "rb"				) as f:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = pq.ParquetFile(snake_case__				)
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size  ,     columns=self.config.columns				)				):
               _SCREAMING_SNAKE_CASE   :				int							    = pa.Table.from_batches([record_batch]				)
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F'''{file_idx}_{batch_idx}''', self._cast_table(snake_case__				)
           except ValueError as e:
             logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case__				)}: {e}'''				)
             raise



