

'''simple docstring'''





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 							_lowerCAmelCase (      __magic_name__	:    Namespace	)							->     Union[str, Any]:
						return ConvertCommand(
						    args.model_type      ,   args.tf_checkpoint      ,   args.pytorch_dump_output      ,   args.config      ,   args.finetuning_task_name	)


UpperCamelCase_							=					"""
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 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@staticmethod
				def    lowerCamelCase_  (					UpperCAmelCase__    :		ArgumentParser      ):
										'''simple docstring'''
										lowercase   :       Dict								=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=UpperCAmelCase__		,				required=UpperCAmelCase__		,				help='''Model\'s type.'''      )
										train_parser.add_argument(
										    '''--tf_checkpoint'''		,				type=UpperCAmelCase__		,				required=UpperCAmelCase__		,				help='''TensorFlow checkpoint path or folder.'''      )
										train_parser.add_argument(
										    '''--pytorch_dump_output'''		,				type=UpperCAmelCase__		,				required=UpperCAmelCase__		,				help='''Path to the PyTorch saved model output.'''      )
										train_parser.add_argument('''--config'''		,				type=UpperCAmelCase__		,				default=''''''		,				help='''Configuration file path or folder.'''      )
										train_parser.add_argument(
										    '''--finetuning_task_name'''		,				type=UpperCAmelCase__		,				default=UpperCAmelCase__		,				help='''Optional fine-tuning task name if the TF model was a finetuned model.'''		,				)
										train_parser.set_defaults(func=UpperCAmelCase__      )







				def __init__(					self    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str		,				*UpperCAmelCase__    :		Optional[int]		,				):
										'''simple docstring'''
										lowercase   :       Dict								=logging.get_logger('''transformers-cli/converting'''      )

										self._logger.info(F'''Loading model {model_type}'''      )
										lowercase   :       List[Any]								=model_type
										lowercase   :       List[Any]								=tf_checkpoint
										lowercase   :       Tuple								=pytorch_dump_output
										lowercase   :       Tuple								=config
										lowercase   :       Optional[Any]								=finetuning_task_name







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''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(UpperCAmelCase__      )

																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(UpperCAmelCase__      )

																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(UpperCAmelCase__      )

																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(UpperCAmelCase__      )

																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(UpperCAmelCase__      )

																if "ckpt" in self._tf_checkpoint.lower():
																						lowercase   :       List[Any]								=self._tf_checkpoint
																						lowercase   :       List[str]								=''''''
																else:
																						lowercase   :       List[str]								=self._tf_checkpoint
																						lowercase   :       Optional[Any]								=''''''
																convert_transfo_xl_checkpoint_to_pytorch(
																    UpperCAmelCase__		,				self._config		,				self._pytorch_dump_output		,				UpperCAmelCase__      )
										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(UpperCAmelCase__      )

																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(UpperCAmelCase__      )

																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]'''      )



'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							print(f'''N layers selected for distillation: {std_idx}''')
							print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

							print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
							torch.save(compressed_sd, args.dump_checkpoint)





