






'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    Tuple           =     [
			    "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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    Union[str, Any]           =     list(s_dict.keys()							)
			for key in keys:
						if "transformer_layers" in key:
									lowercase_	:    Optional[Any]           =     s_dict.pop(_UpperCamelCase							)
						elif "subsample" in key:
									lowercase_	:    List[str]           =     s_dict.pop(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    Any           =     emb.weight.shape
			lowercase_	:    List[str]           =     nn.Linear(_UpperCamelCase						,		_UpperCamelCase						,		bias=_UpperCamelCase							)
			lowercase_	:    Union[str, Any]           =     emb.weight.data
			return lin_layer




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    List[str]           =     torch.load(_UpperCamelCase						,		map_location="cpu"							)
			lowercase_	:    List[Any]           =     mam_aaa["args"]
			lowercase_	:    Any           =     mam_aaa["model"]
			lowercase_	:    Union[str, Any]           =     state_dict["decoder.output_projection.weight"]

			remove_ignore_keys_(_UpperCamelCase							)
			rename_keys(_UpperCamelCase							)

			lowercase_	:    Union[str, Any]           =     state_dict["decoder.embed_tokens.weight"].shape[0]

			lowercase_	:    Tuple           =     args.share_decoder_input_output_embed

			lowercase_	:    Any           =     [int(_UpperCamelCase							) for i in args.conv_kernel_sizes.split(","							)]
			lowercase_	:    Dict           =     SpeechaTextConfig(
			    vocab_size=_UpperCamelCase						,		max_source_positions=args.max_source_positions						,		max_target_positions=args.max_target_positions						,		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						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function="relu"						,		num_conv_layers=len(_UpperCamelCase							)						,		conv_channels=args.conv_channels						,		conv_kernel_sizes=_UpperCamelCase						,		input_feat_per_channel=args.input_feat_per_channel						,		input_channels=args.input_channels						,		tie_word_embeddings=_UpperCamelCase						,		num_beams=5						,		max_length=200						,		use_cache=_UpperCamelCase						,		decoder_start_token_id=2						,		early_stopping=_UpperCamelCase						,		)

			lowercase_	:    int           =     SpeechaTextForConditionalGeneration(_UpperCamelCase							)
			lowercase_	:    Union[str, Any]           =     model.model.load_state_dict(_UpperCamelCase						,		strict=_UpperCamelCase							)
			if len(_UpperCamelCase							) > 0 and not set(_UpperCamelCase							) <= {
			    "encoder.embed_positions.weights",
			    "decoder.embed_positions.weights",
			}:
						raise ValueError(
						    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
						    F""" but all the following weights are missing {missing}"""							)

			if tie_embeds:
						lowercase_	:    Tuple           =     make_linear_from_emb(model.model.decoder.embed_tokens							)
			else:
						lowercase_	:    Any           =     lm_head_weights

			model.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
	UpperCamelCase__ =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	UpperCamelCase__ =			parser.parse_args()
	convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)












'''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


UpperCamelCase__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Tuple           =     XLNetConfig.from_json_file(_UpperCamelCase							)

   lowercase_	:    Union[str, Any]           =     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}"""							)
      lowercase_	:    Dict           =     finetuning_task
      lowercase_	:    Any           =     GLUE_TASKS_NUM_LABELS[finetuning_task]
      lowercase_	:    Any           =     XLNetForSequenceClassification(_UpperCamelCase							)
   elif "squad" in finetuning_task:
      lowercase_	:    Optional[int]           =     finetuning_task
      lowercase_	:    Optional[int]           =     XLNetForQuestionAnswering(_UpperCamelCase							)
   else:
      lowercase_	:    Union[str, Any]           =     XLNetLMHeadModel(_UpperCamelCase							)

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

   # Save pytorch-model
   lowercase_	:    Optional[Any]           =     os.path.join(_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Dict           =     os.path.join(_UpperCamelCase						,		_UpperCamelCase							)
   print(F"""Save PyTorch model to {os.path.abspath(_UpperCamelCase							)}"""							)
   torch.save(model.state_dict()						,		_UpperCamelCase							)
   print(F"""Save configuration file to {os.path.abspath(_UpperCamelCase							)}"""							)
   with open(_UpperCamelCase						,		"w"						,		encoding="utf-8"							) as f:
      f.write(config.to_json_string()							)


if __name__ == "__main__":
 UpperCamelCase__ =			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',
 )
 UpperCamelCase__ =			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
 )




