
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a__				:      Optional[int]    		=     logging.get_logger(__name__)






class     a_       (			a__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Tuple						=  ['input_values', 'attention_mask']






       def __init__(		self				,       _lowerCamelCase=1				,       _lowerCamelCase=1_6000				,       _lowerCamelCase=0.0				,       _lowerCamelCase=False				,       _lowerCamelCase=True				,       **_lowerCamelCase				,       )					->Union[str, Any]:
           super().__init__(feature_size=_lowerCamelCase				,       sampling_rate=_lowerCamelCase				,       padding_value=_lowerCamelCase				,       **_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = return_attention_mask
           SCREAMING_SNAKE_CASE				:				Dict				      = do_normalize






       @staticmethod
       def 	__lowerCAmelCase     (		_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase = 0.0   )					->List[np.ndarray]:
           if attention_mask is not None:
               SCREAMING_SNAKE_CASE				:				int				      = np.array(_lowerCamelCase				,       np.intaa   )
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = []

               for vector, length in zip(_lowerCamelCase				,       attention_mask.sum(-1   )   ):
                   SCREAMING_SNAKE_CASE				:				Optional[int]				      = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
                   if length < normed_slice.shape[0]:
                       SCREAMING_SNAKE_CASE				:				Any				      = padding_value

                   normed_input_values.append(_lowerCamelCase   )
           else:
               SCREAMING_SNAKE_CASE				:				List[Any]				      = [(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

           return normed_input_values






       def __call__(		self				,       _lowerCamelCase				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       _lowerCamelCase = None				,       _lowerCamelCase = None				,       _lowerCamelCase = None				,       **_lowerCamelCase				,       )					->BatchFeature:

           if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                       F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
                       F""" {self.sampling_rate} and not {sampling_rate}."""   )
           else:
               logger.warning(
                   '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
                   '''Failing to do so can result in silent errors that might be hard to debug.'''   )

           SCREAMING_SNAKE_CASE				:				str				      = isinstance(_lowerCamelCase				,       np.ndarray   ) and len(raw_speech.shape   ) > 1
           if is_batched_numpy and len(raw_speech.shape   ) > 2:
               raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = is_batched_numpy or (
               isinstance(_lowerCamelCase				,       (list, tuple)   ) and (isinstance(raw_speech[0]				,       (np.ndarray, tuple, list)   ))
           )

           # always return batch
           if not is_batched:
               SCREAMING_SNAKE_CASE				:				Tuple				      = [raw_speech]

           # convert into correct format for padding
           SCREAMING_SNAKE_CASE				:				List[str]				      = BatchFeature({'''input_values''': raw_speech}   )

           SCREAMING_SNAKE_CASE				:				Dict				      = self.pad(
               _lowerCamelCase				,       padding=_lowerCamelCase				,       max_length=_lowerCamelCase				,       truncation=_lowerCamelCase				,       pad_to_multiple_of=_lowerCamelCase				,       return_attention_mask=_lowerCamelCase				,       )

           # convert input values to correct format
           SCREAMING_SNAKE_CASE				:				Tuple				      = padded_inputs['''input_values''']
           if not isinstance(input_values[0]				,       np.ndarray   ):
               SCREAMING_SNAKE_CASE				:				int				      = [np.asarray(_lowerCamelCase				,       dtype=np.floataa   ) for array in input_values]
           elif (
               not isinstance(_lowerCamelCase				,       np.ndarray   )
               and isinstance(input_values[0]				,       np.ndarray   )
               and input_values[0].dtype is np.dtype(np.floataa   )
           ):
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = [array.astype(np.floataa   ) for array in input_values]
           elif isinstance(_lowerCamelCase				,       np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = input_values.astype(np.floataa   )

           # convert attention_mask to correct format
           SCREAMING_SNAKE_CASE				:				List[Any]				      = padded_inputs.get('''attention_mask'''   )
           if attention_mask is not None:
               SCREAMING_SNAKE_CASE				:				str				      = [np.asarray(_lowerCamelCase				,       dtype=np.intaa   ) for array in attention_mask]

           # zero-mean and unit-variance normalization
           if self.do_normalize:
               SCREAMING_SNAKE_CASE				:				int				      = (
                   attention_mask
                   if self._get_padding_strategies(_lowerCamelCase				,       max_length=_lowerCamelCase   ) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               SCREAMING_SNAKE_CASE				:				List[Any]				      = self.zero_mean_unit_var_norm(
                   padded_inputs['''input_values''']				,       attention_mask=_lowerCamelCase				,       padding_value=self.padding_value   )

           if return_tensors is not None:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = padded_inputs.convert_to_tensors(_lowerCamelCase   )

           return padded_inputs


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				:      List[Any]    		=     {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     [
          '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DecisionTransformerGPT2Model''',
          '''DecisionTransformerGPT2PreTrainedModel''',
          '''DecisionTransformerModel''',
          '''DecisionTransformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      a__				:      str    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
