





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowercase__    :      Tuple						=    logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[Any]      =       feature_size
          __A						: int      =       sampling_rate
          __A						: Dict      =       padding_value

          __A						: Any      =       kwargs.pop('padding_side'						,	'right')
          __A						: Any      =       kwargs.pop('return_attention_mask'						,	_UpperCAmelCase)

          super().__init__(**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	):

          '''simple docstring'''






          if isinstance(_UpperCAmelCase						,	(list, tuple)) and isinstance(processed_features[0]						,	(dict, BatchFeature)):
               __A						: List[str]      =       {
                   key: [example[key] for example in processed_features] for key in processed_features[0].keys()
               }

          # The model's main input name, usually `input_values`, has be passed for padding
          if self.model_input_names[0] not in processed_features:
               raise ValueError(
                   'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                   F' to this method that includes {self.model_input_names[0]}, but you provided'
                   F' {list(processed_features.keys())}')

          __A						: str      =       processed_features[self.model_input_names[0]]
          __A						: Dict      =       (
              return_attention_mask if return_attention_mask is not None else self.return_attention_mask
          )

          if len(_UpperCAmelCase) == 0:
               if return_attention_mask:
                    __A						: Optional[Any]      =       []
               return processed_features

          # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
          # and rebuild them afterwards if no return_tensors is specified
          # Note that we lose the specific device the tensor may be on for PyTorch

          __A						: Tuple      =       required_input[0]
          if isinstance(_UpperCAmelCase						,	(list, tuple)):
               # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
               __A						: Union[str, Any]      =       0
               while len(required_input[index]) == 0:
                    index += 1
               if index < len(_UpperCAmelCase):
                    __A						: Optional[int]      =       required_input[index][0]

          if return_tensors is None:
               if is_tf_tensor(_UpperCAmelCase):
                    __A						: Dict      =       'tf'
               elif is_torch_tensor(_UpperCAmelCase):
                    __A						: Optional[Any]      =       'pt'
               elif isinstance(_UpperCAmelCase						,	(int, float, list, tuple, np.ndarray)):
                    __A						: Dict      =       'np'
               else:
                    raise ValueError(
                        F'type of {first_element} unknown: {type(_UpperCAmelCase)}. '
                        'Should be one of a python, numpy, pytorch or tensorflow object.')

          for key, value in processed_features.items():
               if isinstance(value[0]						,	(int, float)):
                    __A						: Dict      =       to_numpy(_UpperCAmelCase)
               else:
                    __A						: List[Any]      =       [to_numpy(_UpperCAmelCase) for v in value]

        # Convert padding_strategy in PaddingStrategy
          __A						: Union[str, Any]      =       self._get_padding_strategies(padding=_UpperCAmelCase						,	max_length=_UpperCAmelCase)

          __A						: Tuple      =       processed_features[self.model_input_names[0]]

          __A						: Tuple      =       len(_UpperCAmelCase)
          if not all(len(_UpperCAmelCase) == batch_size for v in processed_features.values()):
               raise ValueError('Some items in the output dictionary have a different batch size than others.')

          __A						: Union[str, Any]      =       []
          for i in range(_UpperCAmelCase):
               __A						: Any      =       {k: v[i] for k, v in processed_features.items()}
               # truncation
               __A						: Optional[Any]      =       self._truncate(
                   _UpperCAmelCase						,	max_length=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	)
               truncated_inputs.append(_UpperCAmelCase)

          if padding_strategy == PaddingStrategy.LONGEST:
               # make sure that `max_length` cannot be longer than the longest truncated length
               __A						: Any      =       max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
               __A						: str      =       PaddingStrategy.MAX_LENGTH

          __A						: Optional[Any]      =       {}
          for i in range(_UpperCAmelCase):
               # padding
               __A						: Union[str, Any]      =       self._pad(
                   truncated_inputs[i]						,	max_length=_UpperCAmelCase						,	padding_strategy=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	)

               for key, value in outputs.items():
                    if key not in batch_outputs:
                         __A						: List[Any]      =       []
                    if value.dtype is np.dtype(np.floataa):
                         __A						: Tuple      =       value.astype(np.floataa)
                    batch_outputs[key].append(_UpperCAmelCase)

          return BatchFeature(_UpperCAmelCase						,	tensor_type=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = PaddingStrategy.DO_NOT_PAD						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	):

          '''simple docstring'''






          __A						: Any      =       processed_features[self.model_input_names[0]]

          if padding_strategy == PaddingStrategy.LONGEST:
               __A						: List[Any]      =       len(_UpperCAmelCase)

          if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
               __A						: List[str]      =       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          __A						: Tuple      =       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCAmelCase) < max_length

          if return_attention_mask and "attention_mask" not in processed_features:
               __A						: int      =       np.ones(len(_UpperCAmelCase)						,	dtype=np.intaa)

          if needs_to_be_padded:
               __A						: Union[str, Any]      =       max_length - len(_UpperCAmelCase)
               if self.padding_side == "right":
                    if return_attention_mask:
                         __A						: Tuple      =       np.pad(
                             processed_features['attention_mask']						,	(0, difference))
                    __A						: Optional[int]      =       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    __A						: Optional[Any]      =       np.pad(
                        _UpperCAmelCase						,	_UpperCAmelCase						,	'constant'						,	constant_values=self.padding_value)
               elif self.padding_side == "left":
                    if return_attention_mask:
                         __A						: Union[str, Any]      =       np.pad(
                             processed_features['attention_mask']						,	(difference, 0))
                    __A						: int      =       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    __A						: List[Any]      =       np.pad(
                        _UpperCAmelCase						,	_UpperCAmelCase						,	'constant'						,	constant_values=self.padding_value)
               else:
                    raise ValueError('Invalid padding strategy:' + str(self.padding_side))

          return processed_features


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	):

          '''simple docstring'''






          if not truncation:
               return processed_features
          elif truncation and max_length is None:
               raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.')

          __A						: int      =       processed_features[self.model_input_names[0]]

          # find `max_length` that fits `pad_to_multiple_of`
          if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
               __A						: Optional[Any]      =       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          __A						: Dict      =       len(_UpperCAmelCase) > max_length

          if needs_to_be_truncated:
               __A						: Optional[Any]      =       processed_features[self.model_input_names[0]][:max_length]
               if "attention_mask" in processed_features:
                    __A						: Any      =       processed_features['attention_mask'][:max_length]

          return processed_features


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None):

          '''simple docstring'''






          if padding is not False:
               if padding is True:
                    __A						: List[Any]      =       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
               elif not isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: List[Any]      =       PaddingStrategy(_UpperCAmelCase)
               elif isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: int      =       padding
          else:
               __A						: Any      =       PaddingStrategy.DO_NOT_PAD

          # Set max length if needed
          if max_length is None:
               if padding_strategy == PaddingStrategy.MAX_LENGTH:
                    raise ValueError(
                        F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined')

        # Test if we have a padding value
          if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
               raise ValueError(
                   'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                   ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.')

          return padding_strategy






'''simple docstring'''




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     return {key.lstrip('-'			): value for key, value in zip(unknown_args[::2] ,							unknown_args[1::2]			)}






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: int      =       ArgumentParser(
         'HuggingFace Datasets CLI tool' ,							usage='datasets-cli <command> [<args>]' ,							allow_abbrev=__snake_case			)
     __A						: Optional[Any]      =       parser.add_subparsers(help='datasets-cli command helpers'			)
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__snake_case			)
     EnvironmentCommand.register_subcommand(__snake_case			)
     TestCommand.register_subcommand(__snake_case			)
     RunBeamCommand.register_subcommand(__snake_case			)
     DummyDataCommand.register_subcommand(__snake_case			)

     # Parse args
     __A ,__A						: Optional[Any]      =       parser.parse_known_args()
     if not hasattr(__snake_case ,							'func'			):
          parser.print_help()
          exit(1			)
     __A						: Any      =       parse_unknown_args(__snake_case			)

     # Run
     __A						: List[Any]      =       args.func(__snake_case ,							**__snake_case			)
     service.run()


if __name__ == "__main__":
  main()