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

UpperCAmelCase_			    =							random.Random()




def       lowerCAmelCase_     (		lowercase:							List[str]  ,					lowercase:							Tuple=1.0  ,					lowercase:							Any=None  ,					lowercase:							Any=None		)    ->				Tuple:







 '''simple docstring'''
 if rng is None:
  _UpperCamelCase:     Any							= global_rng

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

 return values




class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def __init__(	self					:  Dict		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  int=7		,  _lowercase					:  Optional[int]=400		,  _lowercase					:  Union[str, Any]=2_000		,  _lowercase					:  Union[str, Any]=2_048		,  _lowercase					:  str=128		,  _lowercase					:  Dict=1		,  _lowercase					:  Optional[Any]=512		,  _lowercase					:  List[Any]=30		,  _lowercase					:  Union[str, Any]=44_100		,  ):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= parent
    _UpperCamelCase:     Any							= batch_size
    _UpperCamelCase:     Union[str, Any]							= min_seq_length
    _UpperCamelCase:     Tuple							= max_seq_length
    _UpperCamelCase:     Optional[int]							= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    _UpperCamelCase:     Optional[int]							= spectrogram_length
    _UpperCamelCase:     List[str]							= feature_size
    _UpperCamelCase:     List[Any]							= num_audio_channels
    _UpperCamelCase:     Dict							= hop_length
    _UpperCamelCase:     Tuple							= chunk_length
    _UpperCamelCase:     Dict							= sampling_rate



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """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   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Any=False		,  _lowercase					:  Union[str, Any]=False							):



    """simple docstring"""



    def _flatten(_lowercase					:  Optional[int]							):
     return list(itertools.chain(*_lowercase							)							)

    if equal_length:
     _UpperCamelCase:     Any							= [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
    else:
     # make sure that inputs increase in size
     _UpperCamelCase:     Optional[Any]							= [
         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:     List[str]							= [np.asarray(_lowercase							) for x in speech_inputs]
    return speech_inputs




@require_torch
@require_torchaudio
class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Any  			=   TvltFeatureExtractor



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= TvltFeatureExtractionTester(self							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.feature_extraction_class(**self.feat_extract_dict							)
    self.assertTrue(hasattr(_lowercase		,  '''spectrogram_length'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''feature_size'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''num_audio_channels'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''hop_length'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''chunk_length'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''sampling_rate'''							)							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.feature_extraction_class(**self.feat_extract_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
     _UpperCamelCase:     Any							= feat_extract_first.save_pretrained(_lowercase							)[0]
     check_json_file_has_correct_format(_lowercase							)
     _UpperCamelCase:     Tuple							= self.feature_extraction_class.from_pretrained(_lowercase							)

    _UpperCamelCase:     Any							= feat_extract_first.to_dict()
    _UpperCamelCase:     List[Any]							= feat_extract_second.to_dict()
    _UpperCamelCase:     Tuple							= dict_first.pop('''mel_filters'''							)
    _UpperCamelCase:     Optional[Any]							= dict_second.pop('''mel_filters'''							)
    self.assertTrue(np.allclose(_lowercase		,  _lowercase							)							)
    self.assertEqual(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= self.feature_extraction_class(**self.feat_extract_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
     _UpperCamelCase:     List[Any]							= os.path.join(_lowercase		,  '''feat_extract.json'''							)
     feat_extract_first.to_json_file(_lowercase							)
     _UpperCamelCase:     int							= self.feature_extraction_class.from_json_file(_lowercase							)

    _UpperCamelCase:     List[str]							= feat_extract_first.to_dict()
    _UpperCamelCase:     str							= feat_extract_second.to_dict()
    _UpperCamelCase:     Tuple							= dict_first.pop('''mel_filters'''							)
    _UpperCamelCase:     str							= dict_second.pop('''mel_filters'''							)
    self.assertTrue(np.allclose(_lowercase		,  _lowercase							)							)
    self.assertEqual(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Any							= self.feature_extraction_class(**self.feat_extract_dict							)

    # create three inputs of length 800, 1000, and 1200
    _UpperCamelCase:     Optional[int]							= [floats_list((1, x)							)[0] for x in range(800		,  1_400		,  200							)]
    _UpperCamelCase:     List[str]							= [np.asarray(_lowercase							) for speech_input in speech_inputs]

    # Test not batched input
    _UpperCamelCase:     str							= feature_extractor(np_speech_inputs[0]		,  return_tensors='''np'''		,  sampling_rate=44_100							).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
    _UpperCamelCase:     Union[str, Any]							= feature_extractor(_lowercase		,  return_tensors='''np'''		,  sampling_rate=44_100							).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
    _UpperCamelCase:     Any							= feature_extractor(
        _lowercase		,  return_tensors='''np'''		,  sampling_rate=44_100		,  mask_audio=_lowercase							).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.
    _UpperCamelCase:     Tuple							= [floats_list((1, x)							)[0] for x in (800, 800, 800)]
    _UpperCamelCase:     Any							= np.asarray(_lowercase							)
    _UpperCamelCase:     Optional[Any]							= feature_extractor(_lowercase		,  return_tensors='''np'''		,  sampling_rate=44_100							).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   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     int							= load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,  '''clean'''		,  split='''validation'''							)
    # automatic decoding with librispeech
    _UpperCamelCase:     Union[str, Any]							= ds.sort('''id'''							).select(range(_lowercase							)							)[:num_samples]['''audio''']

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



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self._load_datasamples(1							)
    _UpperCamelCase:     List[Any]							= TvltFeatureExtractor()
    _UpperCamelCase:     List[str]							= feature_extractor(_lowercase		,  return_tensors='''pt'''							).audio_values

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

    _UpperCamelCase:     Optional[Any]							= torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]							)
    self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,  _lowercase		,  atol=1E-4							)							)
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					max_position_embeddings=1_024  ,					encoder_layers=args.encoder_layers  ,					decoder_layers=args.decoder_layers  ,					encoder_attention_heads=args.encoder_attention_heads  ,					decoder_attention_heads=args.decoder_attention_heads  ,					encoder_ffn_dim=args.encoder_ffn_embed_dim  ,					decoder_ffn_dim=args.decoder_ffn_embed_dim  ,					d_model=args.encoder_embed_dim  ,					encoder_layerdrop=args.encoder_layerdrop  ,					decoder_layerdrop=args.decoder_layerdrop  ,					dropout=args.dropout  ,					attention_dropout=args.attention_dropout  ,					activation_dropout=args.activation_dropout  ,					activation_function='''relu'''  ,					)

 _UpperCamelCase:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _UpperCamelCase:     Optional[Any]							= make_linear_from_emb(model.model.shared		)

 return model


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
  parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  UpperCAmelCase_			    =							parser.parse_args()
  UpperCAmelCase_			    =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)