




'''simple docstring'''






import argparse

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


logging.set_verbosity_info()






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
						# Initialise PyTorch model
						snake_case__					:      List[str]  			=						BertConfig.from_pretrained(
						    'bert-large-cased'							,			vocab_size=__SCREAMING_SNAKE_CASE							,			max_position_embeddings=512							,			is_decoder=__SCREAMING_SNAKE_CASE							,			add_cross_attention=__SCREAMING_SNAKE_CASE							,			)
						snake_case__					:      Any  			=						bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						snake_case__					:      Union[str, Any]  			=						BertGenerationConfig(**__SCREAMING_SNAKE_CASE		)
						if is_encoder:
												snake_case__					:      Optional[Any]  			=						BertGenerationEncoder(__SCREAMING_SNAKE_CASE		)
						else:
												snake_case__					:      str  			=						BertGenerationDecoder(__SCREAMING_SNAKE_CASE		)
						print(f"Building PyTorch model from configuration: {config}"		)

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

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


if __name__ == "__main__":
						A_ 			=		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=50358, type=int, help="Vocab size of model")
						A_ 			=		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,
						)






'''simple docstring'''






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


A_ 			=		get_tests_dir("fixtures/test_sentencepiece.model")






@require_torch
@require_torchaudio
@require_sentencepiece
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      Dict  			=						tempfile.mkdtemp()

           snake_case__					:      Union[str, Any]  			=						['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
           snake_case__					:      str  			=						dict(zip(__lowerCamelCase    ,       range(len(__lowerCamelCase ) ) ) )
           snake_case__					:      Union[str, Any]  			=						Path(self.tmpdirname )
           save_json(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['vocab_file'] )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['spm_file'] )

           snake_case__					:      Optional[Any]  			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
           tokenizer.save_pretrained(self.tmpdirname )

           snake_case__					:      str  			=						{
               'feature_size': 24,
               'num_mel_bins': 24,
               'padding_value': 0.0,
               'sampling_rate': 16000,
               'return_attention_mask': False,
               'do_normalize': True,
           }
           save_json(__lowerCamelCase    ,       save_dir / FEATURE_EXTRACTOR_NAME )





     def  _lowerCAmelCase			(    self    :				str    ,       **__lowerCamelCase    :				int ):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				List[Any] ):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           shutil.rmtree(self.tmpdirname )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Dict  			=						self.get_tokenizer()
           snake_case__					:      Any  			=						self.get_feature_extractor()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           processor.save_pretrained(self.tmpdirname )
           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(self.tmpdirname )

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

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





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Any  			=						SpeechaTextProcessor(
               tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor() )
           processor.save_pretrained(self.tmpdirname )

           snake_case__					:      Tuple  			=						self.get_tokenizer(bos_token='(BOS)'    ,       eos_token='(EOS)' )
           snake_case__					:      int  			=						self.get_feature_extractor(do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(
               self.tmpdirname    ,       bos_token='(BOS)'    ,       eos_token='(EOS)'    ,       do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

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

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





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      int  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      List[str]  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      List[str]  			=						floats_list((3, 1000) )

           snake_case__					:      Tuple  			=						feature_extractor(__lowerCamelCase    ,       return_tensors='np' )
           snake_case__					:      str  			=						processor(__lowerCamelCase    ,       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  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						'This is a test string'

           snake_case__					:      int  			=						processor(text=__lowerCamelCase )

           snake_case__					:      Tuple  			=						tokenizer(__lowerCamelCase )

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





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      str  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      str  			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           snake_case__					:      List[str]  			=						processor.batch_decode(__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase )

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      List[str]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      int  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

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