
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

SCREAMING_SNAKE_CASE							:Optional[int]							 =				random.Random()


def 						UpperCAmelCase			(	a_		,   a_=1.0		,   a_=None		,   a_=None						)		->	int:







				"""simple docstring"""



				if rng is None:
								__A   			= global_rng

				__A   			= []
				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					: Any							,A					: Optional[int]							,A					: List[Any]=7							,A					: List[Any]=4_00							,A					: List[Any]=20_00							,A					: Union[str, Any]=20_48							,A					: int=1_28							,A					: Optional[Any]=1							,A					: int=5_12							,A					: Any=30							,A					: List[str]=4_41_00							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= min_seq_length
					__A   			= max_seq_length
					__A   			= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__A   			= spectrogram_length
					__A   			= feature_size
					__A   			= num_audio_channels
					__A   			= hop_length
					__A   			= chunk_length
					__A   			= sampling_rate






	def 				UpperCamelCase_  (       self					: Any ):
					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 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Any=False							,A					: str=False ):
					def _flatten(A					: Optional[Any] ):
									return list(itertools.chain(*A ) )

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





@require_torch
@require_torchaudio
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     TvltFeatureExtractor






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= TvltFeatureExtractionTester(self )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.feature_extraction_class(**self.feat_extract_dict )
					self.assertTrue(hasattr(A							,"spectrogram_length" ) )
					self.assertTrue(hasattr(A							,"feature_size" ) )
					self.assertTrue(hasattr(A							,"num_audio_channels" ) )
					self.assertTrue(hasattr(A							,"hop_length" ) )
					self.assertTrue(hasattr(A							,"chunk_length" ) )
					self.assertTrue(hasattr(A							,"sampling_rate" ) )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.feature_extraction_class(**self.feat_extract_dict )

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

					__A   			= feat_extract_first.to_dict()
					__A   			= feat_extract_second.to_dict()
					__A   			= dict_first.pop("mel_filters" )
					__A   			= dict_second.pop("mel_filters" )
					self.assertTrue(np.allclose(A							,A ) )
					self.assertEqual(A							,A )






	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= self.feature_extraction_class(**self.feat_extract_dict )

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

					__A   			= feat_extract_first.to_dict()
					__A   			= feat_extract_second.to_dict()
					__A   			= dict_first.pop("mel_filters" )
					__A   			= dict_second.pop("mel_filters" )
					self.assertTrue(np.allclose(A							,A ) )
					self.assertEqual(A							,A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					# Initialize feature_extractor
					__A   			= self.feature_extraction_class(**self.feat_extract_dict )

					# create three inputs of length 800, 1000, and 1200
					__A   			= [floats_list((1, x) )[0] for x in range(8_00							,14_00							,2_00 )]
					__A   			= [np.asarray(A ) for speech_input in speech_inputs]

					# Test not batched input
					__A   			= feature_extractor(np_speech_inputs[0]							,return_tensors="np"							,sampling_rate=4_41_00 ).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
					__A   			= feature_extractor(A							,return_tensors="np"							,sampling_rate=4_41_00 ).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
					__A   			= feature_extractor(
					    A							,return_tensors="np"							,sampling_rate=4_41_00							,mask_audio=A ).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.
					__A   			= [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
					__A   			= np.asarray(A )
					__A   			= feature_extractor(A							,return_tensors="np"							,sampling_rate=4_41_00 ).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 				UpperCamelCase_  (       self					: Optional[int]							,A					: Any ):
					__A   			= load_dataset("hf-internal-testing/librispeech_asr_dummy"							,"clean"							,split="validation" )
					# automatic decoding with librispeech
					__A   			= ds.sort("id" ).select(range(A ) )[:num_samples]["audio"]

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







	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= self._load_datasamples(1 )
					__A   			= TvltFeatureExtractor()
					__A   			= feature_extractor(A							,return_tensors="pt" ).audio_values

					self.assertEquals(audio_values.shape							,(1, 1, 1_92, 1_28) )

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







"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
  __A											=		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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
