

'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					[
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''_float_tensor''',
     '''decoder.output_projection.weight''',
 ]
 for k in ignore_keys:
  state_dict.pop(A__	,							A__ )


def 						_A	(       A__ ):






 """simple docstring"""



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


def 						_A	(       A__	,							A__="facebook/mbart-large-en-ro"	,							A__=False	,							A__=False ):






 """simple docstring"""



 __lowercase				    =					torch.load(A__	,							map_location='''cpu''' )['''model''']
 remove_ignore_keys_(A__ )
 __lowercase				    =					state_dict['''encoder.embed_tokens.weight'''].shape[0]

 __lowercase				    =					MBartConfig.from_pretrained(A__	,							vocab_size=A__ )
 if mbart_aa and finetuned:
  __lowercase				    =					'''relu'''

 __lowercase				    =					state_dict['''decoder.embed_tokens.weight''']
 __lowercase				    =					MBartForConditionalGeneration(A__ )
 model.model.load_state_dict(A__ )

 if finetuned:
  __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='''bart.large, bart.large.cnn or a 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.''')
 parser.add_argument(
     '''--hf_config''',
     default='''facebook/mbart-large-cc25''',
     type=str,
     help='''Which huggingface architecture to use: mbart-large''',
 )
 parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
 parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
 lowerCAmelCase__   		=      parser.parse_args()
 lowerCAmelCase__   		=      convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)








import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

