






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


snake_case__      :	str       	=      random.Random()

if is_torch_available():
 import torch






def     _snake_case    (__lowercase    ,      __lowercase=1.0    ,      __lowercase=None    ,      __lowercase=None				):
   if rng is None:
      UpperCamelCase_									=     global_rng

   UpperCamelCase_									=     []
   for batch_idx in range(shape[0]				):
      values.append([]				)
      for _ in range(shape[1]				):
         values[-1].append(rng.random() * scale				)

   return values


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=7		,		_UpperCAmelCase=400		,		_UpperCAmelCase=2000		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=16000		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		)			-> Optional[int]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     min_seq_length
     UpperCamelCase_									=     max_seq_length
     UpperCamelCase_									=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     UpperCamelCase_									=     feature_size
     UpperCamelCase_									=     padding_value
     UpperCamelCase_									=     sampling_rate
     UpperCamelCase_									=     return_attention_mask
     UpperCamelCase_									=     do_normalize




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False )			-> int:
     def _flatten(_UpperCAmelCase ):
        return list(itertools.chain(*_UpperCAmelCase ) )

     if equal_length:
        UpperCamelCase_									=     floats_list((self.batch_size, self.max_seq_length) )
     else:
        # make sure that inputs increase in size
        UpperCamelCase_									=     [
            _flatten(floats_list((x, self.feature_size) ) )
            for x in range(self.min_seq_length		,		self.max_seq_length		,		self.seq_length_diff )
        ]

     if numpify:
        UpperCamelCase_									=     [np.asarray(_UpperCAmelCase ) for x in speech_inputs]

     return speech_inputs




@require_torch
@require_torchaudio
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   ASTFeatureExtractor




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     ASTFeatureExtractionTester(self )




  def    _UpperCAmelCase     (     self )			-> str:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     UpperCamelCase_									=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     # create three inputs of length 800, 1000, and 1200
     UpperCamelCase_									=     [floats_list((1, x) )[0] for x in range(800		,		1400		,		200 )]
     UpperCamelCase_									=     [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]

     # Test not batched input
     UpperCamelCase_									=     feat_extract(speech_inputs[0]		,		return_tensors='np' ).input_values
     UpperCamelCase_									=     feat_extract(np_speech_inputs[0]		,		return_tensors='np' ).input_values
     self.assertTrue(np.allclose(_UpperCAmelCase		,		_UpperCAmelCase		,		atol=1e-3 ) )

     # Test batched
     UpperCamelCase_									=     feat_extract(_UpperCAmelCase		,		padding=_UpperCAmelCase		,		return_tensors='np' ).input_values
     UpperCamelCase_									=     feat_extract(_UpperCAmelCase		,		padding=_UpperCAmelCase		,		return_tensors='np' ).input_values
     for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,		_UpperCAmelCase ):
        self.assertTrue(np.allclose(_UpperCAmelCase		,		_UpperCAmelCase		,		atol=1e-3 ) )

     # Test 2-D numpy arrays are batched.
     UpperCamelCase_									=     [floats_list((1, x) )[0] for x in (800, 800, 800)]
     UpperCamelCase_									=     np.asarray(_UpperCAmelCase )
     UpperCamelCase_									=     feat_extract(_UpperCAmelCase		,		return_tensors='np' ).input_values
     UpperCamelCase_									=     feat_extract(_UpperCAmelCase		,		return_tensors='np' ).input_values
     for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,		_UpperCAmelCase ):
        self.assertTrue(np.allclose(_UpperCAmelCase		,		_UpperCAmelCase		,		atol=1e-3 ) )




  @require_torch
  def    _UpperCAmelCase     (     self )			-> List[str]:
     import torch

     UpperCamelCase_									=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     UpperCamelCase_									=     np.random.rand(100 ).astype(np.floataa )
     UpperCamelCase_									=     np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
        UpperCamelCase_									=     feature_extractor.pad([{'input_values': inputs}]		,		return_tensors='np' )
        self.assertTrue(np_processed.input_values.dtype == np.floataa )
        UpperCamelCase_									=     feature_extractor.pad([{'input_values': inputs}]		,		return_tensors='pt' )
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Dict:
     from datasets import load_dataset

     UpperCamelCase_									=     load_dataset('hf-internal-testing/librispeech_asr_dummy'		,		'clean'		,		split='validation' )
     # automatic decoding with librispeech
     UpperCamelCase_									=     ds.sort('id' ).select(range(_UpperCAmelCase ) )[:num_samples]['audio']

     return [x["array"] for x in speech_samples]




  @require_torch
  def    _UpperCAmelCase     (     self )			-> List[str]:
     # fmt: off
     UpperCamelCase_									=     torch.tensor(
         [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
          -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
          -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
          -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9] )
     # fmt: on

     UpperCamelCase_									=     self._load_datasamples(1 )
     UpperCamelCase_									=     ASTFeatureExtractor()
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase		,		return_tensors='pt' ).input_values
     self.assertEquals(input_values.shape		,		(1, 1024, 128) )
     self.assertTrue(torch.allclose(input_values[0, 0, :30]		,		_UpperCAmelCase		,		atol=1e-4 ) )













from typing import Any






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validation(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   # Creates data structures and fill initial step
   UpperCamelCase_									=     {}
   UpperCamelCase_									=     {}
   for state in states_space:
      UpperCamelCase_									=     observations_space[0]
      UpperCamelCase_									=     (
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      UpperCamelCase_									=     None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1    ,      len(__lowercase)):
      UpperCamelCase_									=     observations_space[o]
      UpperCamelCase_									=     observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         UpperCamelCase_									=     ''
         UpperCamelCase_									=     -1
         for k_state in states_space:
            UpperCamelCase_									=     (
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               UpperCamelCase_									=     probability
               UpperCamelCase_									=     k_state

            # Update probabilities and pointers dicts
         UpperCamelCase_									=     (
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         UpperCamelCase_									=     arg_max

    # The final observation
   UpperCamelCase_									=     observations_space[len(__lowercase) - 1]

   # argmax for given final observation
   UpperCamelCase_									=     ''
   UpperCamelCase_									=     -1
   for k_state in states_space:
      UpperCamelCase_									=     probabilities[(k_state, final_observation)]
      if probability > max_probability:
         UpperCamelCase_									=     probability
         UpperCamelCase_									=     k_state
   UpperCamelCase_									=     arg_max

   # Process pointers backwards
   UpperCamelCase_									=     last_state
   UpperCamelCase_									=     []
   for o in range(len(__lowercase) - 1    ,      -1    ,      -1):
      result.append(__lowercase)
      UpperCamelCase_									=     pointers[previous, observations_space[o]]
   result.reverse()

   return result






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_not_empty(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   _validate_lists(__lowercase    ,      __lowercase)
   _validate_dicts(
       __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]):
      raise ValueError('There\'s an empty parameter')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_list(__lowercase    ,      'observations_space')
   _validate_list(__lowercase    ,      'states_space')






def     _snake_case    (__lowercase    ,      __lowercase):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a list"""
      raise ValueError(__lowercase)
   else:
      for x in _object:
         if not isinstance(__lowercase    ,      __lowercase):
            UpperCamelCase_									=     f"""{var_name} must be a list of strings"""
            raise ValueError(__lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_dict(__lowercase    ,      'initial_probabilities'    ,      __lowercase)
   _validate_nested_dict(__lowercase    ,      'transition_probabilities')
   _validate_nested_dict(__lowercase    ,      'emission_probabilities')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_dict(_object    ,      __lowercase    ,      __lowercase)
   for x in _object.values():
      _validate_dict(__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase = False):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a dict"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object):
      UpperCamelCase_									=     f"""{var_name} all keys must be strings"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object.values()):
      UpperCamelCase_									=     'nested dictionary ' if nested else ''
      UpperCamelCase_									=     f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
      raise ValueError(__lowercase)


if __name__ == "__main__":
 from doctest import testmod

 testmod()





