from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


_UpperCAmelCase						=		logging.get_logger(__name__)

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

_UpperCAmelCase						=		{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCAmelCase						=		{
    'facebook/blenderbot_small-90M': 512,
}





class UpperCAmelCase						(					lowerCAmelCase__  ):


				'''simple docstring'''






				lowerCamelCase_    =					VOCAB_FILES_NAMES
				lowerCamelCase_    =					PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    =					BlenderbotSmallTokenizer
				def __init__(						self		,	lowercase=None		,	lowercase=None		,	lowercase="<|endoftext|>"		,	lowercase="<|endoftext|>"		,	lowercase="<|endoftext|>"		,	lowercase=False		,	lowercase=True		,	**lowercase		,	):



						"""simple docstring"""





						super().__init__(
						    ByteLevelBPETokenizer(
						        vocab=UpperCamelCase__		,	merges=UpperCamelCase__		,	add_prefix_space=UpperCamelCase__		,	trim_offsets=UpperCamelCase__		,	)		,	bos_token=UpperCamelCase__		,	eos_token=UpperCamelCase__		,	unk_token=UpperCamelCase__		,	**UpperCamelCase__		,	)
						A_		:				List[str]     =      add_prefix_space
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase=None					):



						"""simple docstring"""





						A_		:				Any     =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
								return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None					):



						"""simple docstring"""





						A_		:				Tuple     =      [self.sep_token_id]
						A_		:				Optional[int]     =      [self.cls_token_id]

						if token_ids_a is None:
								return len(cls + token_ids_a + sep					) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]












import argparse

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


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

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

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


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






