
"""simple docstring"""

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class __lowerCAmelCase						( unittest.TestCase     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
      __UpperCamelCase							    =			dict(zip(__UpperCAmelCase      ,	range(len(__UpperCAmelCase ) ) ) )

      __UpperCamelCase							    =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      __UpperCamelCase							    =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6000,
          'return_attention_mask': False,
          'do_normalize': True,
      }

      __UpperCamelCase							    =			tempfile.mkdtemp()
      __UpperCamelCase							    =			os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['vocab_file'] )
      __UpperCamelCase							    =			os.path.join(self.tmpdirname      ,	__UpperCAmelCase )
      with open(self.vocab_file      ,	'w'      ,	encoding='utf-8' ) as fp:
       fp.write(json.dumps(__UpperCAmelCase ) + '\n' )

      with open(self.feature_extraction_file      ,	'w'      ,	encoding='utf-8' ) as fp:
       fp.write(json.dumps(__UpperCAmelCase ) + '\n' )





     def 		UpperCAmelCase   (      self      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.add_kwargs_tokens_map.copy()
      kwargs.update(__UpperCAmelCase )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,	**__UpperCAmelCase )





     def 		UpperCAmelCase   (      self      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,	**__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      shutil.rmtree(self.tmpdirname )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.get_tokenizer()
      __UpperCamelCase							    =			self.get_feature_extractor()

      __UpperCamelCase							    =			WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,	feature_extractor=__UpperCAmelCase )

      processor.save_pretrained(self.tmpdirname )
      __UpperCamelCase							    =			WavaVecaProcessor.from_pretrained(self.tmpdirname )

      self.assertEqual(processor.tokenizer.get_vocab()      ,	tokenizer.get_vocab() )
      self.assertIsInstance(processor.tokenizer      ,	__UpperCAmelCase )

      self.assertEqual(processor.feature_extractor.to_json_string()      ,	feature_extractor.to_json_string() )
      self.assertIsInstance(processor.feature_extractor      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,	feature_extractor=self.get_feature_extractor() )
      processor.save_pretrained(self.tmpdirname )

      __UpperCamelCase							    =			self.get_tokenizer(bos_token='(BOS)'      ,	eos_token='(EOS)' )
      __UpperCamelCase							    =			self.get_feature_extractor(do_normalize=__UpperCAmelCase      ,	padding_value=1.0 )

      __UpperCamelCase							    =			WavaVecaProcessor.from_pretrained(
          self.tmpdirname      ,	bos_token='(BOS)'      ,	eos_token='(EOS)'      ,	do_normalize=__UpperCAmelCase      ,	padding_value=1.0 )

      self.assertEqual(processor.tokenizer.get_vocab()      ,	tokenizer_add_kwargs.get_vocab() )
      self.assertIsInstance(processor.tokenizer      ,	__UpperCAmelCase )

      self.assertEqual(processor.feature_extractor.to_json_string()      ,	feature_extractor_add_kwargs.to_json_string() )
      self.assertIsInstance(processor.feature_extractor      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.get_feature_extractor()
      __UpperCamelCase							    =			self.get_tokenizer()

      __UpperCamelCase							    =			WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,	feature_extractor=__UpperCAmelCase )

      __UpperCamelCase							    =			floats_list((3, 1000) )

      __UpperCamelCase							    =			feature_extractor(__UpperCAmelCase      ,	return_tensors='np' )
      __UpperCamelCase							    =			processor(__UpperCAmelCase      ,	return_tensors='np' )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()      ,	input_processor[key].sum()      ,	delta=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.get_feature_extractor()
      __UpperCamelCase							    =			self.get_tokenizer()

      __UpperCamelCase							    =			WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,	feature_extractor=__UpperCAmelCase )

      __UpperCamelCase							    =			'This is a test string'

      __UpperCamelCase							    =			processor(text=__UpperCAmelCase )

      __UpperCamelCase							    =			tokenizer(__UpperCAmelCase )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]      ,	encoded_processor[key] )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.get_feature_extractor()
      __UpperCamelCase							    =			self.get_tokenizer()

      __UpperCamelCase							    =			WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,	feature_extractor=__UpperCAmelCase )

      __UpperCamelCase							    =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __UpperCamelCase							    =			processor.batch_decode(__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer.batch_decode(__UpperCAmelCase )

      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.get_feature_extractor()
      __UpperCamelCase							    =			self.get_tokenizer()

      __UpperCamelCase							    =			WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,	feature_extractor=__UpperCAmelCase )

      self.assertListEqual(
          processor.model_input_names      ,	feature_extractor.model_input_names      ,	msg='`processor` and `feature_extractor` model input names do not match'      ,	)





"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



