

'''simple docstring'''


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowerCAmelCase_   :   List[Any]       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Optional[Any]       =					{'''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_   :   Optional[int]       =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}






class       UpperCamelCase__  (		UpperCamelCase__     ):
	lowerCAmelCase__					:			Dict								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			Optional[Any]								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			List[Any]								=						["""input_ids""", """attention_mask"""]
	lowerCAmelCase__					:			Tuple								=						None







	def __init__(				self			:   Optional[int] , lowerCamelCase			:   int=None , lowerCamelCase			:   Union[str, Any]=None , lowerCamelCase			:   Optional[int]=None , lowerCamelCase			:   List[str]="<unk>" , lowerCamelCase			:   List[str]="<s>" , lowerCamelCase			:   int="</s>" , lowerCamelCase			:   List[Any]="<pad>" , lowerCamelCase			:   Any=False , lowerCamelCase			:   Optional[Any]=False , **lowerCamelCase			:   Dict , ):




				'''simple docstring'''
				super().__init__(
				    lowerCamelCase , lowerCamelCase , tokenizer_file=lowerCamelCase , unk_token=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , pad_token=lowerCamelCase , add_prefix_space=lowerCamelCase , clean_up_tokenization_spaces=lowerCamelCase , **lowerCamelCase , )
				a__     = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space" , lowerCamelCase		) != add_prefix_space:
							a__     = getattr(lowerCamelCase , pre_tok_state.pop("type"		)		)
							a__     = add_prefix_space
							a__     = pre_tok_class(**lowerCamelCase		)

				a__     = add_prefix_space







	def 						__a   (				self			:   Dict , *lowerCamelCase			:   int , **lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				a__     = kwargs.get("is_split_into_words" , lowerCamelCase		)
				if not (self.add_prefix_space or not is_split_into_words):
							raise Exception(
							    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
							    " pretokenized inputs."		)

				return super()._batch_encode_plus(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   Tuple , *lowerCamelCase			:   str , **lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				a__     = kwargs.get("is_split_into_words" , lowerCamelCase		)

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

				return super()._encode_plus(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   int , lowerCamelCase			:   Any = None		):




				'''simple docstring'''
				a__     = self._tokenizer.model.save(lowerCamelCase , name=lowerCamelCase		)
				return tuple(lowerCamelCase		)







	def 						__a   (				self			:   Dict , lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowerCamelCase , add_special_tokens=lowerCamelCase		) + [self.eos_token_id]		)

				if len(lowerCamelCase		) > self.model_max_length:
							a__     = input_ids[-self.model_max_length :]
				return input_ids










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "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(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  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"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


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



