
'''simple docstring'''



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


UpperCamelCase__ :						str    							=					logging.get_logger(__name__)

class 				_a			(__UpperCAmelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					["""input_values""", """attention_mask"""]






       def __init__(							self    ,							A__=1    ,							A__=1_60_00    ,							A__=0.0    ,							A__=False    ,							A__=True    ,							**A__    ,							)      ->     Union[str, Any]:
          super().__init__(feature_size=lowerCamelCase__    ,							sampling_rate=lowerCamelCase__    ,							padding_value=lowerCamelCase__    ,							**lowerCamelCase__ )
          _SCREAMING_SNAKE_CASE       	=			return_attention_mask
          _SCREAMING_SNAKE_CASE       	=			do_normalize






       @staticmethod
       def     UpperCamelCase			(							A__    ,							A__    ,							A__ = 0.0 )      ->     List[np.ndarray]:



          if attention_mask is not None:
             _SCREAMING_SNAKE_CASE       	=			np.array(lowerCamelCase__    ,							np.intaa )
             _SCREAMING_SNAKE_CASE       	=			[]

             for vector, length in zip(lowerCamelCase__    ,							attention_mask.sum(-1 ) ):
                _SCREAMING_SNAKE_CASE       	=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
                if length < normed_slice.shape[0]:
                   _SCREAMING_SNAKE_CASE       	=			padding_value

                normed_input_values.append(lowerCamelCase__ )
          else:
             _SCREAMING_SNAKE_CASE       	=			[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

          return normed_input_values






       def __call__(							self    ,							A__    ,							A__ = False    ,							A__ = None    ,							A__ = False    ,							A__ = None    ,							A__ = None    ,							A__ = None    ,							A__ = None    ,							**A__    ,							)      ->     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       	=			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       	=			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       	=			[raw_speech]

          # convert into correct format for padding
          _SCREAMING_SNAKE_CASE       	=			BatchFeature({"""input_values""": raw_speech} )

          _SCREAMING_SNAKE_CASE       	=			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       	=			padded_inputs["""input_values"""]
          if not isinstance(input_values[0]    ,							np.ndarray ):
             _SCREAMING_SNAKE_CASE       	=			[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       	=			[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       	=			input_values.astype(np.floataa )

          # convert attention_mask to correct format
          _SCREAMING_SNAKE_CASE       	=			padded_inputs.get("""attention_mask""" )
          if attention_mask is not None:
             _SCREAMING_SNAKE_CASE       	=			[np.asarray(lowerCamelCase__    ,							dtype=np.intaa ) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if self.do_normalize:
             _SCREAMING_SNAKE_CASE       	=			(
                 attention_mask
                 if self._get_padding_strategies(lowerCamelCase__    ,							max_length=lowerCamelCase__ ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             _SCREAMING_SNAKE_CASE       	=			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       	=			padded_inputs.convert_to_tensors(lowerCamelCase__ )

          return padded_inputs








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__ :						List[str]    							=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						Optional[Any]    							=					[
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
         "GPTJForQuestionAnswering",
         "GPTJForSequenceClassification",
         "GPTJModel",
         "GPTJPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "TFGPTJForCausalLM",
         "TFGPTJForQuestionAnswering",
         "TFGPTJForSequenceClassification",
         "TFGPTJModel",
         "TFGPTJPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[Any]    							=					[
         "FlaxGPTJForCausalLM",
         "FlaxGPTJModel",
         "FlaxGPTJPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase__ :						Any    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






