import numpy as np

from transformers import Pipeline

def 			lowercase_				(			_lowerCamelCase							:       Dict):
    lowercase__ :   Union[str, Any]    					=				np.max(_lowerCamelCase		,	axis=-1		,	keepdims=_lowerCamelCase)
    lowercase__ :   List[str]    					=				np.exp(outputs - maxes)
    return shifted_exp / shifted_exp.sum(axis=-1		,	keepdims=_lowerCamelCase)



class   snake_case_       (		__A       ):






   def       __UpperCamelCase     (      self				:     Dict	,						**lowercase_				:     Any  )					->						Tuple:
       lowercase__ :   Any    					=				{}
       if "second_text" in kwargs:
           lowercase__ :   List[str]    					=				kwargs["second_text"]
       return preprocess_kwargs, {}, {}






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     str	,						lowercase_				:     Optional[Any]=None  )					->						Optional[int]:
       return self.tokenizer(lowercase_	,						text_pair=lowercase_	,						return_tensors=self.framework  )






   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.model(**lowercase_  )







   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     List[str]  )					->						List[Any]:
       lowercase__ :   List[str]    					=				model_outputs.logits[0].numpy()
       lowercase__ :   List[str]    					=				softmax(lowercase_  )

       lowercase__ :   List[str]    					=				np.argmax(lowercase_  )
       lowercase__ :   Optional[int]    					=				self.model.config.idalabel[best_class]
       lowercase__ :   Union[str, Any]    					=				probabilities[best_class].item()
       lowercase__ :   int    					=				logits.tolist()
       return {"label": label, "score": score, "logits": logits}










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)





