



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 					__snake_case     (      _UpperCAmelCase    ):
    __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(UpperCAmelCase__       ,			UpperCAmelCase__    )





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    emb.weight.shape
    __a							=    nn.Linear(UpperCAmelCase__       ,			UpperCAmelCase__       ,			bias=UpperCAmelCase__    )
    __a							=    emb.weight.data
    return lin_layer





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    torch.load(UpperCAmelCase__       ,			map_location='''cpu'''    )
    __a							=    mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
    __a							=    mam_aaa["""model"""]
    remove_ignore_keys_(UpperCAmelCase__    )
    __a							=    state_dict["""encoder.embed_tokens.weight"""].shape[0]

    __a							=    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'''       ,			)

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

    return model


if __name__ == "__main__":
 __snake_case						:Dict						=					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.''')
 __snake_case						:Optional[int]						=					parser.parse_args()
 __snake_case						:Optional[int]						=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)










import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case						:int						=					logging.get_logger(__name__)

class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    question_encoder
           __a							=    generator
           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if os.path.isfile(__SCREAMING_SNAKE_CASE):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__SCREAMING_SNAKE_CASE					,		exist_ok=__SCREAMING_SNAKE_CASE)
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''question_encoder_tokenizer''')
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.generator.save_pretrained(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a							=    kwargs.pop('''config'''					,		__SCREAMING_SNAKE_CASE)

           if config is None:
               __a							=    RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.question_encoder					,		subfolder='''question_encoder_tokenizer''')
           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.generator					,		subfolder='''generator_tokenizer''')
           return cls(question_encoder=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					Dict					,		*__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.current_tokenizer(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		*__SCREAMING_SNAKE_CASE							:					Dict					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return self.generator.decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.generator





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[str]] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					str = "longest"					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''					,		__SCREAMING_SNAKE_CASE					,		)
           if max_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               __SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               text_target=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    labels['''input_ids''']
           return model_inputs





