
'''simple docstring'''







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[Any]:
	return ConvertCommand(
	    args.model_type			,				args.tf_checkpoint			,				args.pytorch_dump_output			,				args.config			,				args.finetuning_task_name	)


lowercase    :							List[str]      =					'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'





class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		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    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   *lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		logging.get_logger('transformers-cli/converting'  )

								self._logger.info(F'Loading model {model_type}'  )
								_snake_case   						=		model_type
								_snake_case   						=		tf_checkpoint
								_snake_case   						=		pytorch_dump_output
								_snake_case   						=		config
								_snake_case   						=		finetuning_task_name




							def  lowerCamelCase				(       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(_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():
										_snake_case   						=		self._tf_checkpoint
										_snake_case   						=		''
									else:
										_snake_case   						=		self._tf_checkpoint
										_snake_case   						=		''
									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]'  )








import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='bert', choices=['bert'])
       parser.add_argument('--model_name', default='bert-base-uncased', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       snake_case__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

       for w in ["word_embeddings", "position_embeddings"]:
              snake_case__					:							Tuple													=     state_dict[f'{prefix}.embeddings.{w}.weight']
       for w in ["weight", "bias"]:
              snake_case__					:							Optional[Any]													=     state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

       snake_case__					:							int													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case__					:							Union[str, Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
                     ]
                     snake_case__					:							Dict													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
                     ]

                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
                     ]
              std_idx += 1

       snake_case__					:							Optional[int]													=     state_dict['cls.predictions.decoder.weight']
       snake_case__					:							str													=     state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case__					:							int													=     state_dict[f'cls.predictions.transform.dense.{w}']
                     snake_case__					:							Optional[int]													=     state_dict[f'cls.predictions.transform.LayerNorm.{w}']

       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)





