





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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_UpperCAmelCase				:       List[str]	   =				logging.get_logger(__name__)


_UpperCAmelCase				:       Tuple	   =				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase				:       str	   =				{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase				:       Union[str, Any]	   =				{
    """allenai/led-base-16384""": 1_63_84,
}

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		LEDTokenizer
 UpperCAmelCase__													=		["""input_ids""", """attention_mask"""]
 def __init__(      self  :      int			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      Dict=None			,     UpperCAmelCase  :      int="replace"			,     UpperCAmelCase  :      int="<s>"			,     UpperCAmelCase  :      Optional[int]="</s>"			,     UpperCAmelCase  :      str="</s>"			,     UpperCAmelCase  :      List[Any]="<s>"			,     UpperCAmelCase  :      Any="<unk>"			,     UpperCAmelCase  :      str="<pad>"			,     UpperCAmelCase  :      Union[str, Any]="<mask>"			,     UpperCAmelCase  :      str=False			,     UpperCAmelCase  :      Dict=True			,     **UpperCAmelCase  :      Optional[int]			,     )			->	List[Any]:
     super().__init__(
         UpperCAmelCase			,     UpperCAmelCase			,     tokenizer_file=UpperCAmelCase			,     errors=UpperCAmelCase			,     bos_token=UpperCAmelCase			,     eos_token=UpperCAmelCase			,     sep_token=UpperCAmelCase			,     cls_token=UpperCAmelCase			,     unk_token=UpperCAmelCase			,     pad_token=UpperCAmelCase			,     mask_token=UpperCAmelCase			,     add_prefix_space=UpperCAmelCase			,     trim_offsets=UpperCAmelCase			,     **UpperCAmelCase			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
     if pre_tok_state.get('add_prefix_space'			,     UpperCAmelCase	) != add_prefix_space:
         lowerCamelCase__    :      int					 =		getattr(UpperCAmelCase			,     pre_tok_state.pop('type'	)	)
         lowerCamelCase__    :      Optional[int]					 =		add_prefix_space
         lowerCamelCase__    :      List[str]					 =		pre_tok_class(**UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     lowerCamelCase__    :      str					 =		'post_processor'
     lowerCamelCase__    :      Tuple					 =		getattr(self.backend_tokenizer			,     UpperCAmelCase			,     UpperCAmelCase	)
     if tokenizer_component_instance:
         lowerCamelCase__    :      Any					 =		json.loads(tokenizer_component_instance.__getstate__()	)

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
             lowerCamelCase__    :      int					 =		tuple(state['sep']	)
         if "cls" in state:
             lowerCamelCase__    :      Optional[int]					 =		tuple(state['cls']	)

         lowerCamelCase__    :      Optional[int]					 =		False

         if state.get('add_prefix_space'			,     UpperCAmelCase	) != add_prefix_space:
             lowerCamelCase__    :      Dict					 =		add_prefix_space
             lowerCamelCase__    :      Optional[Any]					 =		True

         if state.get('trim_offsets'			,     UpperCAmelCase	) != trim_offsets:
             lowerCamelCase__    :      Any					 =		trim_offsets
             lowerCamelCase__    :      Tuple					 =		True

         if changes_to_apply:
             lowerCamelCase__    :      Tuple					 =		getattr(UpperCAmelCase			,     state.pop('type'	)	)
             lowerCamelCase__    :      Any					 =		component_class(**UpperCAmelCase	)
             setattr(self.backend_tokenizer			,     UpperCAmelCase			,     UpperCAmelCase	)
 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def      A_	(      self  :      str	)			->	str:
     if self._mask_token is None:
         if self.verbose:
             logger.error('Using mask_token, but it is not set yet.'	)
         return None
     return str(self._mask_token	)
 @mask_token.setter
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Tuple	)			->	str:
     lowerCamelCase__    :      Any					 =		AddedToken(UpperCAmelCase			,     lstrip=UpperCAmelCase			,     rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase			,     UpperCAmelCase	) else value
     lowerCamelCase__    :      Union[str, Any]					 =		value
 def      A_	(      self  :      Any			,     *UpperCAmelCase  :      Tuple			,     **UpperCAmelCase  :      List[str]	)			->	BatchEncoding:
     lowerCamelCase__    :      Union[str, Any]					 =		kwargs.get('is_split_into_words'			,     UpperCAmelCase	)

     if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             'to use it with pretokenized inputs.'	)

     return super()._batch_encode_plus(*UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      List[Any]			,     *UpperCAmelCase  :      str			,     **UpperCAmelCase  :      List[Any]	)			->	BatchEncoding:
     lowerCamelCase__    :      Optional[Any]					 =		kwargs.get('is_split_into_words'			,     UpperCAmelCase	)

     if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             'to use it with pretokenized inputs.'	)

     return super()._encode_plus(*UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     lowerCamelCase__    :      Union[str, Any]					 =		self._tokenizer.model.save(UpperCAmelCase			,     name=UpperCAmelCase	)
     return tuple(UpperCAmelCase	)
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      str=None	)			->	str:
     lowerCamelCase__    :      str					 =		[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      A_	(      self  :      Dict			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      Dict					 =		[self.sep_token_id]
     lowerCamelCase__    :      List[str]					 =		[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]







 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Union[Dict[str, EncodedInput], BatchEncoding]			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Optional[bool] = None			,     )			->	dict:
     lowerCamelCase__    :      List[str]					 =		super()._pad(
         encoded_inputs=UpperCAmelCase			,     max_length=UpperCAmelCase			,     padding_strategy=UpperCAmelCase			,     pad_to_multiple_of=UpperCAmelCase			,     return_attention_mask=UpperCAmelCase			,     )

     # Load from model defaults
     if return_attention_mask is None:
         lowerCamelCase__    :      Tuple					 =		'attention_mask' in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
         lowerCamelCase__    :      List[Any]					 =		encoded_inputs[self.model_input_names[0]]
         # `global_attention_mask` need to have the same length as other (sequential) inputs.
         lowerCamelCase__    :      Dict					 =		len(encoded_inputs['global_attention_mask']	) != len(UpperCAmelCase	)

         if needs_to_be_padded:
             lowerCamelCase__    :      Any					 =		len(UpperCAmelCase	) - len(encoded_inputs['global_attention_mask']	)

             if self.padding_side == "right":
                 # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                 lowerCamelCase__    :      List[str]					 =		(
                     encoded_inputs['global_attention_mask'] + [-1] * difference
                 )
             elif self.padding_side == "left":
                 lowerCamelCase__    :      Dict					 =		[-1] * difference + encoded_inputs[
                     'global_attention_mask'
                 ]
             else:
                 raise ValueError('Invalid padding strategy:' + str(self.padding_side	)	)

     return encoded_inputs












import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	encoder_layers=args.encoder_layers						,	decoder_layers=args.decoder_layers						,	encoder_attention_heads=args.encoder_attention_heads						,	decoder_attention_heads=args.decoder_attention_heads						,	encoder_ffn_dim=args.encoder_ffn_embed_dim						,	decoder_ffn_dim=args.decoder_ffn_embed_dim						,	d_model=args.encoder_embed_dim						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
    parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





