





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 		lowerCAmelCase  (	UpperCamelCase__   :   Namespace					)      ->    List[str]:

						"""simple docstring"""





						return ConvertCommand(
						    args.model_type       ,			args.tf_checkpoint       ,			args.pytorch_dump_output       ,			args.config       ,			args.finetuning_task_name					)


lowerCAmelCase							:       str					 =	"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""





class 				a			(      __lowercase			):






				@staticmethod
				def 			snake_case_    (  _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   parser.add_parser(
										    '''convert'''			, help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''			, )
										train_parser.add_argument('''--model_type'''			, type=_lowerCAmelCase			, required=_lowerCAmelCase			, help='''Model\'s type.'''					)
										train_parser.add_argument(
										    '''--tf_checkpoint'''			, type=_lowerCAmelCase			, required=_lowerCAmelCase			, help='''TensorFlow checkpoint path or folder.'''					)
										train_parser.add_argument(
										    '''--pytorch_dump_output'''			, type=_lowerCAmelCase			, required=_lowerCAmelCase			, help='''Path to the PyTorch saved model output.'''					)
										train_parser.add_argument('''--config'''			, type=_lowerCAmelCase			, default=''''''			, help='''Configuration file path or folder.'''					)
										train_parser.add_argument(
										    '''--finetuning_task_name'''			, type=_lowerCAmelCase			, default=_lowerCAmelCase			, help='''Optional fine-tuning task name if the TF model was a finetuned model.'''			, )
										train_parser.set_defaults(func=_lowerCAmelCase					)






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

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   logging.get_logger('''transformers-cli/converting'''					)

										self._logger.info(f"""Loading model {model_type}"""					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   model_type
										__SCREAMING_SNAKE_CASE:				Any           =   tf_checkpoint
										__SCREAMING_SNAKE_CASE:				Dict           =   pytorch_dump_output
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   config
										__SCREAMING_SNAKE_CASE:				Tuple           =   finetuning_task_name






				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self._model_type == "albert":
																try:
																						from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										elif self._model_type == "bert":
																try:
																						from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										elif self._model_type == "funnel":
																try:
																						from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										elif self._model_type == "t5":
																try:
																						from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										elif self._model_type == "gpt":
																from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
																    convert_openai_checkpoint_to_pytorch,
																)

																convert_openai_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										elif self._model_type == "transfo_xl":
																try:
																						from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																						    convert_transfo_xl_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																if "ckpt" in self._tf_checkpoint.lower():
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self._tf_checkpoint
																						__SCREAMING_SNAKE_CASE:				Dict           =   ''''''
																else:
																						__SCREAMING_SNAKE_CASE:				Tuple           =   self._tf_checkpoint
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   ''''''
																convert_transfo_xl_checkpoint_to_pytorch(
																    _lowerCAmelCase			, self._config			, self._pytorch_dump_output			, _lowerCAmelCase					)
										elif self._model_type == "gpt2":
																try:
																						from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																						    convert_gpta_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										elif self._model_type == "xlnet":
																try:
																						from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																						    convert_xlnet_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(_lowerCAmelCase					)

																convert_xlnet_checkpoint_to_pytorch(
																    self._tf_checkpoint			, self._config			, self._pytorch_dump_output			, self._finetuning_task_name					)
										elif self._model_type == "xlm":
																from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
																    convert_xlm_checkpoint_to_pytorch,
																)

																convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint			, self._pytorch_dump_output					)
										elif self._model_type == "lxmert":
																from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
																    convert_lxmert_checkpoint_to_pytorch,
																)

																convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint			, self._pytorch_dump_output					)
										elif self._model_type == "rembert":
																from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
																    convert_rembert_tf_checkpoint_to_pytorch,
																)

																convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint			, self._config			, self._pytorch_dump_output					)
										else:
																raise ValueError(
																    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''					)










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	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							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



