



'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def 	_A							( _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=[
							    '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(__a	, __a       )



def 	_A							( _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=emb.weight.shape
							__lowercase													=nn.Linear(__a	, __a	, bias=__a       )
							__lowercase													=emb.weight.data
							return lin_layer



def 	_A							( _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=torch.load(__a	, map_location='cpu'       )
							__lowercase													=mam_aaa['args'] or mam_aaa['cfg']['model']
							__lowercase													=mam_aaa['model']
							remove_ignore_keys_(__a       )
							__lowercase													=state_dict['encoder.embed_tokens.weight'].shape[0]

							__lowercase													=MaMaaaConfig(
							    vocab_size=__a	, max_position_embeddings=1_024	, 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'	, )

							__lowercase													=state_dict['decoder.embed_tokens.weight']
							__lowercase													=MaMaaaForConditionalGeneration(__a       )
							model.model.load_state_dict(__a	, strict=__a       )
							__lowercase													=make_linear_from_emb(model.model.shared       )

							return model


if __name__ == "__main__":
		lowerCamelCase									=							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.""")
		lowerCamelCase									=							parser.parse_args()
		lowerCamelCase									=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)








"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


a_							=    logging.get_logger(__name__)

a_							=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a_							=    {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

a_							=    {
    """squeezebert/squeezebert-uncased""": 5_12,
    """squeezebert/squeezebert-mnli""": 5_12,
    """squeezebert/squeezebert-mnli-headless""": 5_12,
}


a_							=    {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			VOCAB_FILES_NAMES
			a_       :			List[str]												=			PRETRAINED_VOCAB_FILES_MAP
			a_       :			Optional[Any]												=			PRETRAINED_INIT_CONFIGURATION
			a_       :			Tuple												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_       :			Optional[int]												=			SqueezeBertTokenizer
			def __init__(	self				,   A=None				,   A=None				,   A=True				,   A="[UNK]"				,   A="[SEP]"				,   A="[PAD]"				,   A="[CLS]"				,   A="[MASK]"				,   A=True				,   A=None				,   **A				,   ):
						super().__init__(
						    A				,   tokenizer_file=A				,   do_lower_case=A				,   unk_token=A				,   sep_token=A				,   pad_token=A				,   cls_token=A				,   mask_token=A				,   tokenize_chinese_chars=A				,   strip_accents=A				,   **A				,   )

						_lowerCamelCase					:					Union[str, Any]	       =    json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
						if (
						    normalizer_state.get('lowercase'				,   A							) != do_lower_case
						    or normalizer_state.get('strip_accents'				,   A							) != strip_accents
						    or normalizer_state.get('handle_chinese_chars'				,   A							) != tokenize_chinese_chars
						):
									_lowerCamelCase					:					List[Any]	       =    getattr(A				,   normalizer_state.pop('type'							)							)
									_lowerCamelCase					:					Optional[Any]	       =    do_lower_case
									_lowerCamelCase					:					str	       =    strip_accents
									_lowerCamelCase					:					Any	       =    tokenize_chinese_chars
									_lowerCamelCase					:					int	       =    normalizer_class(**A							)

						_lowerCamelCase					:					Dict	       =    do_lower_case
			def    _lowerCAmelCase		(	self				,   A				,   A=None							):
						_lowerCamelCase					:					Dict	       =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output
			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					Union[str, Any]	       =    [self.sep_token_id]
						_lowerCamelCase					:					Optional[Any]	       =    [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							) * [0] + len(token_ids_a + sep							) * [1]

			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					str	       =    self._tokenizer.model.save(A				,   name=A							)
						return tuple(A							)





