


"""simple docstring"""






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__snake_case		=       {
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]=None       ):






     """simple docstring"""

     _a    	=				XLNetConfig.from_json_file(a__       )

     _a    	=				finetuning_task.lower() if finetuning_task is not None else ''''''
     if finetuning_task in GLUE_TASKS_NUM_LABELS:
          print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}'       )
          _a    	=				finetuning_task
          _a    	=				GLUE_TASKS_NUM_LABELS[finetuning_task]
          _a    	=				XLNetForSequenceClassification(a__       )
     elif "squad" in finetuning_task:
          _a    	=				finetuning_task
          _a    	=				XLNetForQuestionAnswering(a__       )
     else:
          _a    	=				XLNetLMHeadModel(a__       )

     # Load weights from tf checkpoint
     load_tf_weights_in_xlnet(a__,     a__,     a__       )

     # Save pytorch-model
     _a    	=				os.path.join(a__,     a__       )
     _a    	=				os.path.join(a__,     a__       )
     print(f'Save PyTorch model to {os.path.abspath(a__       )}'       )
     torch.save(model.state_dict(),     a__       )
     print(f'Save configuration file to {os.path.abspath(a__       )}'       )
     with open(a__,     '''w''',     encoding='''utf-8'''       ) as f:
          f.write(config.to_json_string()       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--xlnet_config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained XLNet model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the folder to store the PyTorch model or dataset/vocab.",
   )
   parser.add_argument(
       "--finetuning_task",
       default=None,
       type=str,
       help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
   )
   __snake_case		=       parser.parse_args()
   print(args)

   convert_xlnet_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
   )



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

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

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       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''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       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)