import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__lowercase							 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[float]						=      field(
        default=0.0						,	metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""} )
    a__	:						bool						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """Whether to SortishSamler or not."""} )
    a__	:						bool						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} )
    a__	:						bool						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """whether to use adafactor"""} )
    a__	:						Optional[float]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""} )
    a__	:						Optional[float]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""} )
    a__	:						Optional[float]						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """Dropout probability. Goes into model.config."""} )
    a__	:						Optional[float]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Attention dropout probability. Goes into model.config."""} )
    a__	:						Optional[str]						=      field(
        default="""linear"""						,	metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''}						,	)












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)





