






'''simple docstring'''




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '''--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'''
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/













'''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


lowerCamelCase_							      =      logging.get_logger(__name__)

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		['''input_values''', '''attention_mask''']

   def __init__(	self				:			int   ,   __UpperCAmelCase				:			Optional[int]=1   ,   __UpperCAmelCase				:			List[str]=16000   ,   __UpperCAmelCase				:			Optional[Any]=0.0   ,   __UpperCAmelCase				:			Union[str, Any]=False   ,   __UpperCAmelCase				:			Dict=True   ,   **__UpperCAmelCase				:			int   ,   ):





         '''simple docstring'''





         super().__init__(feature_size=__UpperCAmelCase   ,   sampling_rate=__UpperCAmelCase   ,   padding_value=__UpperCAmelCase   ,   **__UpperCAmelCase	)
         _A      =			return_attention_mask
         _A      =			do_normalize

   @staticmethod
   def 		lowerCAmelCase				(	__UpperCAmelCase				:			List[np.ndarray]   ,   __UpperCAmelCase				:			List[np.ndarray]   ,   __UpperCAmelCase				:			float = 0.0	):





         '''simple docstring'''





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

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

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

         return normed_input_values

   def __call__(	self				:			Dict   ,   __UpperCAmelCase				:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,   __UpperCAmelCase				:			Union[bool, str, PaddingStrategy] = False   ,   __UpperCAmelCase				:			Optional[int] = None   ,   __UpperCAmelCase				:			bool = False   ,   __UpperCAmelCase				:			Optional[int] = None   ,   __UpperCAmelCase				:			Optional[bool] = None   ,   __UpperCAmelCase				:			Optional[Union[str, TensorType]] = None   ,   __UpperCAmelCase				:			Optional[int] = None   ,   **__UpperCAmelCase				:			str   ,   ):





         '''simple docstring'''





         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."	)

         _A      =			isinstance(__UpperCAmelCase   ,   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}'''	)
         _A      =			is_batched_numpy or (
             isinstance(__UpperCAmelCase   ,   (list, tuple)	) and (isinstance(raw_speech[0]   ,   (np.ndarray, tuple, list)	))
         )

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

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

         _A      =			self.pad(
             __UpperCAmelCase   ,   padding=__UpperCAmelCase   ,   max_length=__UpperCAmelCase   ,   truncation=__UpperCAmelCase   ,   pad_to_multiple_of=__UpperCAmelCase   ,   return_attention_mask=__UpperCAmelCase   ,   )

         # convert input values to correct format
         _A      =			padded_inputs["input_values"]
         if not isinstance(input_values[0]   ,   np.ndarray	):
               _A      =			[np.asarray(__UpperCAmelCase   ,   dtype=np.floataa	) for array in input_values]
         elif (
             not isinstance(__UpperCAmelCase   ,   np.ndarray	)
             and isinstance(input_values[0]   ,   np.ndarray	)
             and input_values[0].dtype is np.dtype(np.floataa	)
         ):
               _A      =			[array.astype(np.floataa	) for array in input_values]
         elif isinstance(__UpperCAmelCase   ,   np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
               _A      =			input_values.astype(np.floataa	)

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

         # zero-mean and unit-variance normalization
         if self.do_normalize:
               _A      =			(
                   attention_mask
                   if self._get_padding_strategies(__UpperCAmelCase   ,   max_length=__UpperCAmelCase	) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               _A      =			self.zero_mean_unit_var_norm(
                   padded_inputs["input_values"]   ,   attention_mask=__UpperCAmelCase   ,   padding_value=self.padding_value	)

         if return_tensors is not None:
               _A      =			padded_inputs.convert_to_tensors(__UpperCAmelCase	)

         return padded_inputs





