



"""simple docstring"""
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   # to overwrite at feature extractactor specific tests
   __lowerCAmelCase						:     Union[str, Any]		=		None
   __lowerCAmelCase						:     Any		=		None

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    return self.feat_extract_tester.prepare_feat_extract_dict()

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.feature_extraction_class(**self.feat_extract_dict		)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """feature_size"""		)		)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """sampling_rate"""		)		)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """padding_value"""		)		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			self.feat_extract_tester.prepare_inputs_for_common()
    UpperCAmelCase			:      Union[str, Any]			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      str			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Union[str, Any]			=			BatchFeature({input_name: speech_inputs}		)

    self.assertTrue(all(len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		) for x, y in zip(_SCREAMING_SNAKE_CASE   ,  processed_features[input_name]		)		)		)

    UpperCAmelCase			:      Optional[Any]			=			self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			BatchFeature({input_name: speech_inputs}   ,  tensor_type="""np"""		)

    UpperCAmelCase			:      List[str]			=			processed_features[input_name]

    if len(batch_features_input.shape		) < 3:
     UpperCAmelCase			:      Optional[Any]			=			batch_features_input[:, :, None]

    self.assertTrue(
        batch_features_input.shape
        == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)

   @require_torch
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      Any			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Any			=			BatchFeature({input_name: speech_inputs}   ,  tensor_type="""pt"""		)

    UpperCAmelCase			:      str			=			processed_features[input_name]

    if len(batch_features_input.shape		) < 3:
     UpperCAmelCase			:      Union[str, Any]			=			batch_features_input[:, :, None]

    self.assertTrue(
        batch_features_input.shape
        == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)

   @require_tf
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      int			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Any			=			BatchFeature({input_name: speech_inputs}   ,  tensor_type="""tf"""		)

    UpperCAmelCase			:      Any			=			processed_features[input_name]

    if len(batch_features_input.shape		) < 3:
     UpperCAmelCase			:      Tuple			=			batch_features_input[:, :, None]

    self.assertTrue(
        batch_features_input.shape
        == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=False		)						->   Dict:





    '''simple docstring'''




    def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      str			=			len(input[0]		)
     for input_slice in input[1:]:
      if len(_SCREAMING_SNAKE_CASE		) != length:
       return False
     return True

    def _inputs_are_equal(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
      return False

     for input_slice_a, input_slice_a in zip(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
      if not np.allclose(np.asarray(_SCREAMING_SNAKE_CASE		)   ,  np.asarray(_SCREAMING_SNAKE_CASE		)   ,  atol=1E-3		):
       return False
     return True

    UpperCAmelCase			:      int			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      Optional[int]			=			self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Tuple			=			BatchFeature({input_name: speech_inputs}		)

    UpperCAmelCase			:      Tuple			=			self.feat_extract_tester.seq_length_diff
    UpperCAmelCase			:      List[str]			=			self.feat_extract_tester.max_seq_length + pad_diff
    UpperCAmelCase			:      Any			=			self.feat_extract_tester.min_seq_length
    UpperCAmelCase			:      List[Any]			=			self.feat_extract_tester.batch_size
    UpperCAmelCase			:      Dict			=			self.feat_extract_tester.feature_size

    # test padding for List[int] + numpy
    UpperCAmelCase			:      Any			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			input_a[input_name]

    UpperCAmelCase			:      Tuple			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""		)
    UpperCAmelCase			:      int			=			input_a[input_name]

    UpperCAmelCase			:      List[Any]			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[-1]		)		)
    UpperCAmelCase			:      Dict			=			input_a[input_name]

    UpperCAmelCase			:      str			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      int			=			input_a[input_name]

    # max_length parameter has to be provided when setting `padding="max_length"`
    with self.assertRaises(_SCREAMING_SNAKE_CASE		):
     feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""		)[input_name]

    UpperCAmelCase			:      List[Any]			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      Optional[int]			=			input_a[input_name]

    self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(len(input_a[0]		) == pad_min_length		)
    self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
    self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
    self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

    if feature_size > 1:
     self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

    # test padding for `pad_to_multiple_of` for List[int] + numpy
    UpperCAmelCase			:      str			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  pad_to_multiple_of=10		)
    UpperCAmelCase			:      Optional[int]			=			input_a[input_name]

    UpperCAmelCase			:      List[str]			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  pad_to_multiple_of=10		)
    UpperCAmelCase			:      Any			=			input_a[input_name]

    UpperCAmelCase			:      Dict			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  pad_to_multiple_of=10   ,  max_length=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			input_a[input_name]

    UpperCAmelCase			:      Optional[Any]			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  pad_to_multiple_of=10   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_tensors="""np"""   ,  )
    UpperCAmelCase			:      Optional[int]			=			input_a[input_name]

    self.assertTrue(all(len(_SCREAMING_SNAKE_CASE		) % 10 == 0 for x in input_a		)		)
    self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)		)

    UpperCAmelCase			:      Optional[int]			=			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
    self.assertTrue(all(len(_SCREAMING_SNAKE_CASE		) == expected_mult_pad_length for x in input_a		)		)
    self.assertEqual(input_a.shape[:2]   ,  (batch_size, expected_mult_pad_length)		)

    if feature_size > 1:
     self.assertTrue(input_a.shape[2] == feature_size		)

    # Check padding value is correct
    UpperCAmelCase			:      Union[str, Any]			=			(np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
    self.assertTrue(
        abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
        < 1E-3		)
    self.assertTrue(
        abs(
            np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
            - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
        < 1E-3		)
    self.assertTrue(
        abs(
            np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
            - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
        < 1E-3		)
    self.assertTrue(
        abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1E-3		)
    self.assertTrue(
        abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
        < 1E-3		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=False		)						->   Optional[int]:





    '''simple docstring'''




    def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      str			=			len(input[0]		)
     for input_slice in input[1:]:
      if len(_SCREAMING_SNAKE_CASE		) != length:
       return False
     return True

    def _inputs_are_equal(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
      return False

     for input_slice_a, input_slice_a in zip(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
      if not np.allclose(np.asarray(_SCREAMING_SNAKE_CASE		)   ,  np.asarray(_SCREAMING_SNAKE_CASE		)   ,  atol=1E-3		):
       return False
     return True

    UpperCAmelCase			:      Optional[int]			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      List[Any]			=			self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      int			=			BatchFeature({input_name: speech_inputs}		)

    # truncate to smallest
    UpperCAmelCase			:      Optional[int]			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[0]		)   ,  truncation=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			input_a[input_name]

    UpperCAmelCase			:      str			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[0]		)		)
    UpperCAmelCase			:      Any			=			input_a[input_name]

    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)

    # truncate to smallest with np
    UpperCAmelCase			:      Optional[int]			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[0]		)   ,  return_tensors="""np"""   ,  truncation=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Optional[int]			=			input_a[input_name]

    UpperCAmelCase			:      int			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[0]		)   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      Optional[Any]			=			input_a[input_name]

    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

    # since truncation forces padding to be smaller than longest input
    # function can't return `np.ndarray`, but has to return list
    self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)

    # truncate to middle
    UpperCAmelCase			:      List[Any]			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[1]		)   ,  truncation=_SCREAMING_SNAKE_CASE   ,  return_tensors="""np"""   ,  )
    UpperCAmelCase			:      Union[str, Any]			=			input_a[input_name]

    UpperCAmelCase			:      Tuple			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[1]		)   ,  truncation=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			input_a[input_name]

    UpperCAmelCase			:      Union[str, Any]			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[1]		)   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      Optional[int]			=			input_a[input_name]

    self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)		)

    # since truncation forces padding to be smaller than longest input
    # function can't return `np.ndarray`, but has to return list
    self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

    # padding has to be max_length when setting `truncation=True`
    with self.assertRaises(_SCREAMING_SNAKE_CASE		):
     feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  truncation=_SCREAMING_SNAKE_CASE		)[input_name]

    # padding has to be max_length when setting `truncation=True`
    with self.assertRaises(_SCREAMING_SNAKE_CASE		):
     feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  truncation=_SCREAMING_SNAKE_CASE		)[input_name]

    # padding has to be max_length when setting `truncation=True`
    with self.assertRaises(_SCREAMING_SNAKE_CASE		):
     feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  truncation=_SCREAMING_SNAKE_CASE		)[input_name]

    # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
    with self.assertRaises(_SCREAMING_SNAKE_CASE		):
     feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  truncation=_SCREAMING_SNAKE_CASE		)[input_name]

    # test truncation for `pad_to_multiple_of` for List[int] + numpy
    UpperCAmelCase			:      Union[str, Any]			=			12
    UpperCAmelCase			:      int			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[0]		)   ,  pad_to_multiple_of=_SCREAMING_SNAKE_CASE   ,  truncation=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Optional[Any]			=			input_a[input_name]

    UpperCAmelCase			:      int			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=len(speech_inputs[0]		)   ,  pad_to_multiple_of=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			input_a[input_name]

    # retrieve expected_length as multiple of pad_to_multiple_of
    UpperCAmelCase			:      Any			=			len(speech_inputs[0]		)
    if expected_length % pad_to_multiple_of != 0:
     UpperCAmelCase			:      Union[str, Any]			=			((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

    self.assertTrue(len(input_a[0]		) == expected_length		)
    self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
    self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    self._check_padding(numpify=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    self._check_padding(numpify=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    self._check_truncation(numpify=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    self._check_truncation(numpify=_SCREAMING_SNAKE_CASE		)

   @require_torch
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      Union[str, Any]			=			self.feat_extract_tester.prepare_inputs_for_common()
    UpperCAmelCase			:      int			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Union[str, Any]			=			BatchFeature({input_name: speech_inputs}		)

    UpperCAmelCase			:      List[Any]			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  return_tensors="""np"""		)[input_name]
    UpperCAmelCase			:      Union[str, Any]			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  return_tensors="""pt"""		)[input_name]

    self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)

   @require_tf
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.feature_extraction_class(**self.feat_extract_dict		)
    UpperCAmelCase			:      Any			=			self.feat_extract_tester.prepare_inputs_for_common()
    UpperCAmelCase			:      Optional[Any]			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Union[str, Any]			=			BatchFeature({input_name: speech_inputs}		)

    UpperCAmelCase			:      str			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  return_tensors="""np"""		)[input_name]
    UpperCAmelCase			:      Dict			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  return_tensors="""tf"""		)[input_name]

    self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1E-2		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.feat_extract_dict
    UpperCAmelCase			:      Optional[Any]			=			True
    UpperCAmelCase			:      Tuple			=			self.feature_extraction_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			self.feat_extract_tester.prepare_inputs_for_common()
    UpperCAmelCase			:      str			=			[len(_SCREAMING_SNAKE_CASE		) for x in speech_inputs]
    UpperCAmelCase			:      Optional[Any]			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      List[str]			=			BatchFeature({input_name: speech_inputs}		)

    UpperCAmelCase			:      List[str]			=			feat_extract.pad(_SCREAMING_SNAKE_CASE   ,  padding="""longest"""   ,  return_tensors="""np"""		)
    self.assertIn("""attention_mask"""   ,  _SCREAMING_SNAKE_CASE		)
    self.assertListEqual(list(processed.attention_mask.shape		)   ,  list(processed[input_name].shape[:2]		)		)
    self.assertListEqual(processed.attention_mask.sum(-1		).tolist()   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.feat_extract_dict
    UpperCAmelCase			:      Optional[Any]			=			True
    UpperCAmelCase			:      int			=			self.feature_extraction_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.feat_extract_tester.prepare_inputs_for_common()
    UpperCAmelCase			:      Any			=			[len(_SCREAMING_SNAKE_CASE		) for x in speech_inputs]
    UpperCAmelCase			:      List[Any]			=			feat_extract.model_input_names[0]

    UpperCAmelCase			:      Any			=			BatchFeature({input_name: speech_inputs}		)
    UpperCAmelCase			:      Any			=			min(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			feat_extract.pad(
        _SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  truncation=_SCREAMING_SNAKE_CASE   ,  return_tensors="""np"""		)
    self.assertIn("""attention_mask"""   ,  _SCREAMING_SNAKE_CASE		)
    self.assertListEqual(
        list(processed_pad.attention_mask.shape		)   ,  [processed_pad[input_name].shape[0], max_length]		)
    self.assertListEqual(
        processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()   ,  [max_length for x in speech_inputs]		)





"""simple docstring"""
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A:     List[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'AutoTokenizer'
   __lowerCAmelCase						:     str		=		['tokenizer']

   __lowerCAmelCase						:     Any		=		{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   int:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			speaker_embeddings

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if speaker_embeddings_dict_path is not None:
     UpperCAmelCase			:      Any			=			get_file_from_repo(
         _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if speaker_embeddings_path is None:
      logger.warning(
          F"`{os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."		)
      UpperCAmelCase			:      Optional[int]			=			None
     else:
      with open(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			None

    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    return cls(tokenizer=_SCREAMING_SNAKE_CASE   ,  speaker_embeddings=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings"   ,  _SCREAMING_SNAKE_CASE = False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  """v2"""		)   ,  exist_ok=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      str			=			{}

     UpperCAmelCase			:      Union[str, Any]			=			save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       UpperCAmelCase			:      Optional[Any]			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

       UpperCAmelCase			:      Any			=			{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""]   ,  _SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}"		)   ,  voice_preset[key]   ,  allow_pickle=_SCREAMING_SNAKE_CASE   ,  )
        UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}.npy"		)

       UpperCAmelCase			:      Tuple			=			tmp_dict

     with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  """w"""		) as fp:
      json.dump(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    super().save_pretrained(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.speaker_embeddings[voice_preset]

    UpperCAmelCase			:      List[Any]			=			{}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."		)

     UpperCAmelCase			:      List[str]			=			get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path"""   ,  """/"""		)   ,  voice_preset_paths[key]   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if path is None:
      raise ValueError(
          F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'   ,  '/'		)   ,  voice_preset_paths[key]		)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

     UpperCAmelCase			:      List[str]			=			np.load(_SCREAMING_SNAKE_CASE		)

    return voice_preset_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[str]:





    '''simple docstring'''




    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(F"Voice preset unrecognized, missing {key} as a key."		)

     if not isinstance(voice_preset[key]   ,  np.ndarray		):
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)

     if len(voice_preset[key].shape		) != self.preset_shape[key]:
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)




   def __call__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="pt"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''simple docstring'''




    if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if (
         isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      UpperCAmelCase			:      Dict			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

     else:
      if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) and not voice_preset.endswith(""".npz"""		):
       UpperCAmelCase			:      Tuple			=			voice_preset + """.npz"""

      UpperCAmelCase			:      Union[str, Any]			=			np.load(_SCREAMING_SNAKE_CASE		)

    if voice_preset is not None:
     self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			self.tokenizer(
        _SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_attention_mask=_SCREAMING_SNAKE_CASE   ,  return_token_type_ids=_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    if voice_preset is not None:
     UpperCAmelCase			:      List[Any]			=			voice_preset

    return encoded_text
