



import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor

lowerCAmelCase: Optional[int]							=						random.Random()
def 				lowerCamelCase__	(						_A			,      _A=1.0			,      _A=None			,      _A=None    ):
  if rng is None:
    a						: Optional[Any]					       =					global_rng

  a						: Union[str, Any]					       =					[]
  for batch_idx in range(shape[0]    ):
    values.append([]    )
    for _ in range(shape[1]    ):
      values[-1].append(rng.random() * scale    )

  return values

@require_torch
@require_torchaudio
class 							a__( unittest.TestCase     ):







    def __init__(				self			:  Any						,      __snake_case			:  Optional[int]						,      __snake_case			:  Dict=7						,      __snake_case			:  str=4_00						,      __snake_case			:  Any=20_00						,      __snake_case			:  List[str]=24						,      __snake_case			:  Optional[Any]=24						,      __snake_case			:  Tuple=0.0						,      __snake_case			:  List[Any]=1_60_00						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  Optional[int]=True						,      ):
      a						: int					       =					parent
      a						: Any					       =					batch_size
      a						: Optional[int]					       =					min_seq_length
      a						: Any					       =					max_seq_length
      a						: Union[str, Any]					       =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      a						: List[Any]					       =					feature_size
      a						: List[Any]					       =					num_mel_bins
      a						: Optional[int]					       =					padding_value
      a						: Any					       =					sampling_rate
      a						: Any					       =					return_attention_mask
      a						: Union[str, Any]					       =					do_normalize







    def       lowercase_	(				self			:  Dict     ):
      return {
          "feature_size": self.feature_size,
          "num_mel_bins": self.num_mel_bins,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  str=False						,      __snake_case			:  Dict=False     ):


      def _flatten(__snake_case			:  Tuple     ):
        return list(itertools.chain(*__snake_case     )     )

      if equal_length:
        a						: str					       =					[floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
      else:
        # make sure that inputs increase in size
        a						: int					       =					[
            floats_list((x, self.feature_size)     )
            for x in range(self.min_seq_length						,      self.max_seq_length						,      self.seq_length_diff     )
        ]
      if numpify:
        a						: int					       =					[np.asarray(__snake_case     ) for x in speech_inputs]
      return speech_inputs

@require_torch
@require_torchaudio
class 							a__( _lowerCamelCase		,			unittest.TestCase     ):
    lowercase__			=   SpeechaTextFeatureExtractor if is_speech_available() else None







    def       lowercase_	(				self			:  int     ):
      a						: Union[str, Any]					       =					SpeechaTextFeatureExtractionTester(self     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Optional[int]     ):
      self.assertTrue(np.all(np.mean(__snake_case						,      axis=0     ) < 1e-3     )     )
      self.assertTrue(np.all(np.abs(np.var(__snake_case						,      axis=0     ) - 1     ) < 1e-3     )     )







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[int]					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      # create three inputs of length 800, 1000, and 1200
      a						: str					       =					[floats_list((1, x)     )[0] for x in range(8_00						,      14_00						,      2_00     )]
      a						: Dict					       =					[np.asarray(__snake_case     ) for speech_input in speech_inputs]

      # Test feature size
      a						: int					       =					feature_extractor(__snake_case						,      padding=__snake_case						,      return_tensors='np'     ).input_features
      self.assertTrue(input_features.ndim == 3     )
      self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size     )

      # Test not batched input
      a						: str					       =					feature_extractor(speech_inputs[0]						,      return_tensors='np'     ).input_features
      a						: Union[str, Any]					       =					feature_extractor(np_speech_inputs[0]						,      return_tensors='np'     ).input_features
      self.assertTrue(np.allclose(__snake_case						,      __snake_case						,      atol=1e-3     )     )

      # Test batched
      a						: List[str]					       =					feature_extractor(__snake_case						,      return_tensors='np'     ).input_features
      a						: List[str]					       =					feature_extractor(__snake_case						,      return_tensors='np'     ).input_features
      for enc_seq_a, enc_seq_a in zip(__snake_case						,      __snake_case     ):
        self.assertTrue(np.allclose(__snake_case						,      __snake_case						,      atol=1e-3     )     )

      # Test 2-D numpy arrays are batched.
      a						: Any					       =					[floats_list((1, x)     )[0] for x in (8_00, 8_00, 8_00)]
      a						: int					       =					np.asarray(__snake_case     )
      a						: Union[str, Any]					       =					feature_extractor(__snake_case						,      return_tensors='np'     ).input_features
      a						: Optional[Any]					       =					feature_extractor(__snake_case						,      return_tensors='np'     ).input_features
      for enc_seq_a, enc_seq_a in zip(__snake_case						,      __snake_case     ):
        self.assertTrue(np.allclose(__snake_case						,      __snake_case						,      atol=1e-3     )     )







    def       lowercase_	(				self			:  Dict     ):
      a						: int					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      a						: Union[str, Any]					       =					[floats_list((1, x)     )[0] for x in range(8_00						,      14_00						,      2_00     )]

      a						: List[Any]					       =					['longest', 'max_length', 'do_not_pad']
      a						: Tuple					       =					[None, 16, None]
      for max_length, padding in zip(__snake_case						,      __snake_case     ):
        a						: Dict					       =					feature_extractor(
            __snake_case						,      padding=__snake_case						,      max_length=__snake_case						,      return_attention_mask=__snake_case     )
        a						: str					       =					inputs.input_features
        a						: str					       =					inputs.attention_mask
        a						: str					       =					[np.sum(__snake_case     ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: Optional[Any]					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      a						: Union[str, Any]					       =					[floats_list((1, x)     )[0] for x in range(8_00						,      14_00						,      2_00     )]

      a						: Union[str, Any]					       =					['longest', 'max_length', 'do_not_pad']
      a						: Tuple					       =					[None, 16, None]
      for max_length, padding in zip(__snake_case						,      __snake_case     ):
        a						: Optional[int]					       =					feature_extractor(
            __snake_case						,      max_length=__snake_case						,      padding=__snake_case						,      return_tensors='np'						,      return_attention_mask=__snake_case     )
        a						: Union[str, Any]					       =					inputs.input_features
        a						: Optional[int]					       =					inputs.attention_mask
        a						: Dict					       =					[np.sum(__snake_case     ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6     )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6     )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: List[str]					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      a						: Optional[int]					       =					[floats_list((1, x)     )[0] for x in range(8_00						,      14_00						,      2_00     )]
      a						: Optional[Any]					       =					feature_extractor(
          __snake_case						,      padding='max_length'						,      max_length=4						,      truncation=__snake_case						,      return_tensors='np'						,      return_attention_mask=__snake_case						,      )
      a						: List[Any]					       =					inputs.input_features
      a						: List[str]					       =					inputs.attention_mask
      a						: Any					       =					np.sum(attention_mask == 1						,      axis=1     )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
      self._check_zero_mean_unit_variance(input_features[1]     )
      self._check_zero_mean_unit_variance(input_features[2]     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Tuple					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      a						: List[Any]					       =					[floats_list((1, x)     )[0] for x in range(8_00						,      14_00						,      2_00     )]
      a						: int					       =					feature_extractor(
          __snake_case						,      padding='longest'						,      max_length=4						,      truncation=__snake_case						,      return_tensors='np'						,      return_attention_mask=__snake_case						,      )
      a						: Tuple					       =					inputs.input_features
      a						: str					       =					inputs.attention_mask
      a						: Union[str, Any]					       =					np.sum(attention_mask == 1						,      axis=1     )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
      self._check_zero_mean_unit_variance(input_features[2]     )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape						,      (3, 4, 24)     )

      a						: Any					       =					[floats_list((1, x)     )[0] for x in range(8_00						,      14_00						,      2_00     )]
      a						: Optional[Any]					       =					feature_extractor(
          __snake_case						,      padding='longest'						,      max_length=16						,      truncation=__snake_case						,      return_tensors='np'						,      return_attention_mask=__snake_case						,      )
      a						: Union[str, Any]					       =					inputs.input_features
      a						: List[Any]					       =					inputs.attention_mask
      a						: List[str]					       =					np.sum(attention_mask == 1						,      axis=1     )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
      self._check_zero_mean_unit_variance(input_features[2]     )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape						,      (3, 6, 24)     )







    def       lowercase_	(				self			:  List[Any]     ):
      import torch

      a						: str					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      a						: Union[str, Any]					       =					np.random.rand(1_00						,      32     ).astype(np.floataa     )
      a						: int					       =					np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
        a						: Optional[int]					       =					feature_extractor.pad([{'input_features': inputs}]						,      return_tensors='np'     )
        self.assertTrue(np_processed.input_features.dtype == np.floataa     )
        a						: Union[str, Any]					       =					feature_extractor.pad([{'input_features': inputs}]						,      return_tensors='pt'     )
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  List[str]     ):
      from datasets import load_dataset

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

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







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					np.array([
          -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
          -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
          -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
      ]     )
      # fmt: on

      a						: Tuple					       =					self._load_datasamples(1     )
      a						: Optional[Any]					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
      a						: Tuple					       =					feature_extractor(__snake_case						,      return_tensors='pt'     ).input_features
      self.assertEquals(input_features.shape						,      (1, 5_84, 24)     )
      self.assertTrue(np.allclose(input_features[0, 0, :30]						,      __snake_case						,      atol=1e-4     )     )




'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output