

'''simple docstring'''
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					: str   					=	random.Random()


def 			_lowerCAmelCase   (		_UpperCamelCase		:	Optional[Any]				,					_UpperCamelCase		:	str=1.0				,					_UpperCamelCase		:	Optional[Any]=None				,					_UpperCamelCase		:	Dict=None							) ->     Dict:
     """simple docstring"""




     if rng is None:
          _SCREAMING_SNAKE_CASE				       =global_rng

     _SCREAMING_SNAKE_CASE				       =[]
     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    ):





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =parent
       _SCREAMING_SNAKE_CASE				       =batch_size
       _SCREAMING_SNAKE_CASE				       =min_seq_length
       _SCREAMING_SNAKE_CASE				       =max_seq_length
       _SCREAMING_SNAKE_CASE				       =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _SCREAMING_SNAKE_CASE				       =spectrogram_length
       _SCREAMING_SNAKE_CASE				       =feature_size
       _SCREAMING_SNAKE_CASE				       =num_audio_channels
       _SCREAMING_SNAKE_CASE				       =hop_length
       _SCREAMING_SNAKE_CASE				       =chunk_length
       _SCREAMING_SNAKE_CASE				       =sampling_rate





  def 		A    (						self :      List[Any]					)      ->       List[str]:





       '''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 		A    (						self :      Any				,   _a :      Optional[Any]=False				,   _a :      List[Any]=False					)      ->       Tuple:





       '''simple docstring'''






       def _flatten(_a :      List[str]					):
            return list(itertools.chain(*snake_case__					)					)

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







@require_torch
@require_torchaudio
class 				A__       (			lowerCamelCase_	,  unittest.TestCase    ):
  A__											=							TvltFeatureExtractor





  def 		A    (						self :      Any					)      ->       Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =TvltFeatureExtractionTester(self					)





  def 		A    (						self :      List[Any]					)      ->       Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =self.feature_extraction_class(**self.feat_extract_dict					)
       self.assertTrue(hasattr(snake_case__				,   'spectrogram_length'					)					)
       self.assertTrue(hasattr(snake_case__				,   'feature_size'					)					)
       self.assertTrue(hasattr(snake_case__				,   'num_audio_channels'					)					)
       self.assertTrue(hasattr(snake_case__				,   'hop_length'					)					)
       self.assertTrue(hasattr(snake_case__				,   'chunk_length'					)					)
       self.assertTrue(hasattr(snake_case__				,   'sampling_rate'					)					)





  def 		A    (						self :      Optional[int]					)      ->       Any:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE				       =feat_extract_first.to_dict()
       _SCREAMING_SNAKE_CASE				       =feat_extract_second.to_dict()
       _SCREAMING_SNAKE_CASE				       =dict_first.pop('mel_filters'					)
       _SCREAMING_SNAKE_CASE				       =dict_second.pop('mel_filters'					)
       self.assertTrue(np.allclose(snake_case__				,   snake_case__					)					)
       self.assertEqual(snake_case__				,   snake_case__					)





  def 		A    (						self :      Optional[int]					)      ->       str:





       '''simple docstring'''






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

       with tempfile.TemporaryDirectory() as tmpdirname:
            _SCREAMING_SNAKE_CASE				       =os.path.join(snake_case__				,   'feat_extract.json'					)
            feat_extract_first.to_json_file(snake_case__					)
            _SCREAMING_SNAKE_CASE				       =self.feature_extraction_class.from_json_file(snake_case__					)

       _SCREAMING_SNAKE_CASE				       =feat_extract_first.to_dict()
       _SCREAMING_SNAKE_CASE				       =feat_extract_second.to_dict()
       _SCREAMING_SNAKE_CASE				       =dict_first.pop('mel_filters'					)
       _SCREAMING_SNAKE_CASE				       =dict_second.pop('mel_filters'					)
       self.assertTrue(np.allclose(snake_case__				,   snake_case__					)					)
       self.assertEqual(snake_case__				,   snake_case__					)





  def 		A    (						self :      List[str]					)      ->       Any:





       '''simple docstring'''






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

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

       # Test not batched input
       _SCREAMING_SNAKE_CASE				       =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
       _SCREAMING_SNAKE_CASE				       =feature_extractor(snake_case__				,   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
       _SCREAMING_SNAKE_CASE				       =feature_extractor(
           snake_case__				,   return_tensors='np'				,   sampling_rate=4_4100				,   mask_audio=snake_case__					).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.
       _SCREAMING_SNAKE_CASE				       =[floats_list((1, x)					)[0] for x in (800, 800, 800)]
       _SCREAMING_SNAKE_CASE				       =np.asarray(snake_case__					)
       _SCREAMING_SNAKE_CASE				       =feature_extractor(snake_case__				,   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 		A    (						self :      List[str]				,   _a :      Dict					)      ->       int:





       '''simple docstring'''






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

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





  def 		A    (						self :      Dict					)      ->       Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =self._load_datasamples(1					)
       _SCREAMING_SNAKE_CASE				       =TvltFeatureExtractor()
       _SCREAMING_SNAKE_CASE				       =feature_extractor(snake_case__				,   return_tensors='pt'					).audio_values

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

       _SCREAMING_SNAKE_CASE				       =torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]					)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,   snake_case__				,   atol=1e-4					)					)






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FunnelConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FunnelBaseModel(lowerCamelCase_  ) if base_model else FunnelModel(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			torch.save(model.state_dict()  ,							lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[Any]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
						)
						UpperCamelCase__ :   Tuple						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)



