


"""simple docstring"""






import copy
import random

from transformers import CLIPTokenizer


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          super().__init__(*__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				{}


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Union[str, Any]:
          _a    	=				super().add_tokens(__UpperCAmelCase    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)
          if num_added_tokens == 0:
               raise ValueError(
                   F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
                   ''' `placeholder_token` that is not already in the tokenizer.'''							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				*__UpperCAmelCase    ,				__UpperCAmelCase=1    ,				**__UpperCAmelCase							)       ->							List[Any]:
          _a    	=				[]
          if num_vec_per_token == 1:
               self.try_adding_tokens(__UpperCAmelCase    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)
               output.append(__UpperCAmelCase							)
          else:
               _a    	=				[]
               for i in range(__UpperCAmelCase							):
                    _a    	=				placeholder_token + F'_{i}'
                    self.try_adding_tokens(__UpperCAmelCase    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)
                    output.append(__UpperCAmelCase							)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
          for token in self.token_map:
               if token in placeholder_token:
                    raise ValueError(
                        F'The tokenizer already has placeholder token {token} that can get confused with'
                        F' {placeholder_token}keep placeholder tokens independent'							)
          _a    	=				output


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=1.0							)       ->							Any:
          if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							):
               _a    	=				[]
               for i in range(len(__UpperCAmelCase							)							):
                    output.append(self.replace_placeholder_tokens_in_text(text[i]    ,				vector_shuffle=__UpperCAmelCase							)							)
               return output
          for placeholder_token in self.token_map:
               if placeholder_token in text:
                    _a    	=				self.token_map[placeholder_token]
                    _a    	=				tokens[: 1 + int(len(__UpperCAmelCase							) * prop_tokens_to_load							)]
                    if vector_shuffle:
                         _a    	=				copy.copy(__UpperCAmelCase							)
                         random.shuffle(__UpperCAmelCase							)
                    _a    	=				text.replace(__UpperCAmelCase    ,				''' '''.join(__UpperCAmelCase							)							)
          return text


     def __call__(      self    ,				__UpperCAmelCase    ,				*__UpperCAmelCase    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=1.0    ,				**__UpperCAmelCase							)       ->							str:
          return super().__call__(
              self.replace_placeholder_tokens_in_text(
                  __UpperCAmelCase    ,				vector_shuffle=__UpperCAmelCase    ,				prop_tokens_to_load=__UpperCAmelCase							)    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				*__UpperCAmelCase    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=1.0    ,				**__UpperCAmelCase							)       ->							Any:
          return super().encode(
              self.replace_placeholder_tokens_in_text(
                  __UpperCAmelCase    ,				vector_shuffle=__UpperCAmelCase    ,				prop_tokens_to_load=__UpperCAmelCase							)    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)



"""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


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							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 audio 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.'''							)

          if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

          if is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							).T for audio in raw_audio]
          elif not is_batched and not isinstance(__UpperCAmelCase    ,				np.ndarray							):
               _a    	=				np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							)
          elif isinstance(__UpperCAmelCase    ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
               _a    	=				raw_audio.astype(np.floataa							)

          # always return batch
          if not is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase							).T]

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

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

          return padded_inputs