
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset

__lowerCAmelCase			:     Tuple           =   random.Random()

def a__    (       A_,     A_=1.0,     A_=None,     A_=None					):






  '''simple docstring'''





  if rng is None:
    __magic_name__				     =       global_rng

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

  return values




class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def __init__( self			: Optional[int]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Tuple=7			,							UpperCamelCase__			: int=400			,							UpperCamelCase__			: List[str]=2000			,							UpperCamelCase__			: Tuple=2048			,							UpperCamelCase__			: Union[str, Any]=128			,							UpperCamelCase__			: List[Any]=1			,							UpperCamelCase__			: Tuple=512			,							UpperCamelCase__			: int=30			,							UpperCamelCase__			: Optional[Any]=4_4100			,							)			->	int:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       min_seq_length
    __magic_name__				     =       max_seq_length
    __magic_name__				     =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    __magic_name__				     =       spectrogram_length
    __magic_name__				     =       feature_size
    __magic_name__				     =       num_audio_channels
    __magic_name__				     =       hop_length
    __magic_name__				     =       chunk_length
    __magic_name__				     =       sampling_rate
  def 							_lowercase							( self			: Optional[int]    )			->	int:







    """simple docstring"""






    return {
        "spectrogram_length": self.spectrogram_length,
        "feature_size": self.feature_size,
        "num_audio_channels": self.num_audio_channels,
        "hop_length": self.hop_length,
        "chunk_length": self.chunk_length,
        "sampling_rate": self.sampling_rate,
    }





  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: Tuple=False			,							UpperCamelCase__			: List[Any]=False    )			->	List[Any]:







    """simple docstring"""






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

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







@require_torch
@require_torchaudio
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		TvltFeatureExtractor
  def 							_lowercase							( self			: List[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       TvltFeatureExtractionTester(self    )
  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_dict    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""spectrogram_length"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""feature_size"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""num_audio_channels"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""hop_length"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""chunk_length"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""sampling_rate"""    )    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_dict    )

    with tempfile.TemporaryDirectory() as tmpdirname:
      __magic_name__				     =       feat_extract_first.save_pretrained(UpperCamelCase__    )[0]
      check_json_file_has_correct_format(UpperCamelCase__    )
      __magic_name__				     =       self.feature_extraction_class.from_pretrained(UpperCamelCase__    )

    __magic_name__				     =       feat_extract_first.to_dict()
    __magic_name__				     =       feat_extract_second.to_dict()
    __magic_name__				     =       dict_first.pop("""mel_filters"""    )
    __magic_name__				     =       dict_second.pop("""mel_filters"""    )
    self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__    )    )
    self.assertEqual(UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_dict    )

    with tempfile.TemporaryDirectory() as tmpdirname:
      __magic_name__				     =       os.path.join(UpperCamelCase__			,							"""feat_extract.json"""    )
      feat_extract_first.to_json_file(UpperCamelCase__    )
      __magic_name__				     =       self.feature_extraction_class.from_json_file(UpperCamelCase__    )

    __magic_name__				     =       feat_extract_first.to_dict()
    __magic_name__				     =       feat_extract_second.to_dict()
    __magic_name__				     =       dict_first.pop("""mel_filters"""    )
    __magic_name__				     =       dict_second.pop("""mel_filters"""    )
    self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__    )    )
    self.assertEqual(UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: List[Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_dict    )

    # create three inputs of length 800, 1000, and 1200
    __magic_name__				     =       [floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
    __magic_name__				     =       [np.asarray(UpperCamelCase__    ) for speech_input in speech_inputs]

    # Test not batched input
    __magic_name__				     =       feature_extractor(np_speech_inputs[0]			,							return_tensors="""np"""			,							sampling_rate=4_4100    ).audio_values

    self.assertTrue(encoded_audios.ndim == 4    )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

    # Test batched
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""			,							sampling_rate=4_4100    ).audio_values

    self.assertTrue(encoded_audios.ndim == 4    )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

    # Test audio masking
    __magic_name__				     =       feature_extractor(
        UpperCamelCase__			,							return_tensors="""np"""			,							sampling_rate=4_4100			,							mask_audio=UpperCamelCase__    ).audio_values

    self.assertTrue(encoded_audios.ndim == 4    )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

    # Test 2-D numpy arrays are batched.
    __magic_name__				     =       [floats_list((1, x)    )[0] for x in (800, 800, 800)]
    __magic_name__				     =       np.asarray(UpperCamelCase__    )
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""			,							sampling_rate=4_4100    ).audio_values
    self.assertTrue(encoded_audios.ndim == 4    )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: Tuple    )			->	List[Any]:







    """simple docstring"""






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

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





  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self._load_datasamples(1    )
    __magic_name__				     =       TvltFeatureExtractor()
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""pt"""    ).audio_values

    self.assertEquals(audio_values.shape			,							(1, 1, 192, 128)    )

    __magic_name__				     =       torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]    )
    self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,							UpperCamelCase__			,							atol=1E-4    )    )



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


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

