



import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)


_lowercase     							=						{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

_lowercase     							=						{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

_lowercase     							=						'</w>'
_lowercase     							=						'@@ '






def      _A				(UpperCamelCase		:      List[str] )  ->Optional[int]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		set()
			lowerCamelCase__ :      str								=		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char) )
						lowerCamelCase__ :      str								=		char
			return pairs


# Speech2Text2 has no max input length
_lowercase     							=						{'facebook/s2t-wav2vec2-large-en-de': 10_24}
class 				__A					( A_      ):
			UpperCamelCase					:List[str]     =			VOCAB_FILES_NAMES
			UpperCamelCase					:str     =			PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase					:Dict     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase					:Union[str, Any]     =			['''input_ids''', '''attention_mask''']





			def __init__(self							,    __magic_name__							,    __magic_name__="<s>"							,    __magic_name__="<pad>"							,    __magic_name__="</s>"							,    __magic_name__="<unk>"							,    __magic_name__=False							,    __magic_name__=None							,    **__magic_name__							,    ):
						super().__init__(
						    unk_token=__A							,    bos_token=__A							,    eos_token=__A							,    pad_token=__A							,    do_lower_case=__A							,    **__A							,    )

						lowerCamelCase__ :      int								=		do_lower_case

						with open(__A							,    encoding="""utf-8"""				) as vocab_handle:
									lowerCamelCase__ :      List[Any]								=		json.load(__A				)
						lowerCamelCase__ :      str								=		{v: k for k, v in self.encoder.items()}

						if merges_file is None:
									logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding."				)

									lowerCamelCase__ :      List[Any]								=		None
									lowerCamelCase__ :      str								=		None
						else:
									with open(__A							,    encoding="""utf-8"""				) as merges_handle:
												lowerCamelCase__ :      Optional[Any]								=		merges_handle.read().split("""\n"""				)[:-1]

									lowerCamelCase__ :      List[Any]								=		[tuple(merge.split()[:2]				) for merge in merges]
									lowerCamelCase__ :      List[Any]								=		dict(zip(__A							,    range(len(__A				)				)				)				)
									lowerCamelCase__ :      Tuple								=		{}





			@property
			def        _snake_case    (self				):
						return len(self.decoder				)





			def        _snake_case    (self				):
						return dict(self.encoder							,    **self.added_tokens_encoder				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Tuple								=		tuple(token[:-1]				) + (token[-1] + BPE_TOKEN_MERGES,)
						if token in self.cache:
									return self.cache[token]
						lowerCamelCase__ :      Optional[Any]								=		get_pairs(__A				)

						if not pairs:
									return token

						while True:
									lowerCamelCase__ :      Any								=		min(__A							,    key=lambda __magic_name__				: self.bpe_ranks.get(__A							,    float("""inf"""				)				)				)
									if bigram not in self.bpe_ranks:
												break
									lowerCamelCase__ :      Optional[Any]								=		bigram
									lowerCamelCase__ :      Optional[Any]								=		[]
									lowerCamelCase__ :      Any								=		0
									while i < len(__A				):
												try:
															lowerCamelCase__ :      Tuple								=		word.index(__A							,    __A				)
												except ValueError:
															new_word.extend(word[i:]				)
															break
												else:
															new_word.extend(word[i:j]				)
															lowerCamelCase__ :      Union[str, Any]								=		j

												if word[i] == first and i < len(__A				) - 1 and word[i + 1] == second:
															new_word.append(first + second				)
															i += 2
												else:
															new_word.append(word[i]				)
															i += 1
									lowerCamelCase__ :      List[str]								=		tuple(__A				)
									lowerCamelCase__ :      Dict								=		new_word
									if len(__A				) == 1:
												break
									else:
												lowerCamelCase__ :      List[Any]								=		get_pairs(__A				)
						lowerCamelCase__ :      Dict								=		" ".join(__A				)
						if word == "\n  " + BPE_TOKEN_MERGES:
									lowerCamelCase__ :      Any								=		"\n" + BPE_TOKEN_MERGES

						if word.endswith(__A				):
									lowerCamelCase__ :      List[str]								=		word.replace(__A							,    """"""				)

						lowerCamelCase__ :      List[Any]								=		word.replace(""" """							,    __A				)
						lowerCamelCase__ :      Tuple								=		word
						return word





			def        _snake_case    (self							,    __magic_name__				):

						if self.bpe_ranks is None:
									raise ValueError(
									    """This tokenizer was instantiated without a `merges.txt` file, so"""
									    """ that it can only be used for decoding, not for encoding."""
									    """Make sure to provide `merges.txt` file at instantiation to enable """
									    """encoding."""				)

						if self.do_lower_case:
									lowerCamelCase__ :      List[Any]								=		text.lower()

						lowerCamelCase__ :      Optional[int]								=		text.split()

						lowerCamelCase__ :      Optional[int]								=		[]
						for token in text:
									if token:
												split_tokens.extend(list(self.bpe(__A				).split(""" """				)				)				)

						return split_tokens





			def        _snake_case    (self							,    __magic_name__				):
						return self.encoder.get(__A							,    self.encoder.get(self.unk_token				)				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		self.decoder.get(__A							,    self.unk_token				)
						return result





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		" ".join(__A				)

						# make sure @@ tokens are concatenated
						lowerCamelCase__ :      int								=		"".join(string.split(__A				)				)

						return string





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None				):
						if not os.path.isdir(__A				):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
									return
						lowerCamelCase__ :      Union[str, Any]								=		os.path.join(
						    __A							,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
						lowerCamelCase__ :      Any								=		os.path.join(
						    __A							,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

						with open(__A							,    """w"""							,    encoding="""utf-8"""				) as f:
									f.write(json.dumps(self.encoder							,    indent=2							,    sort_keys=__A							,    ensure_ascii=__A				) + """\n"""				)

						lowerCamelCase__ :      Dict								=		0
						if self.bpe_ranks is None:
									return (vocab_file,)

						with open(__A							,    """w"""							,    encoding="""utf-8"""				) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,    key=lambda __magic_name__				: kv[1]				):
												if index != token_index:
															logger.warning(
															    f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
															    """ Please check that the tokenizer is not corrupted!"""				)
															lowerCamelCase__ :      Any								=		token_index
												writer.write(""" """.join(__A				) + """\n"""				)
												index += 1

						return (vocab_file, merges_file)






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

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


if __name__ == "__main__":
				_lowercase     							=						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 T5 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.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



