





import doctest
from collections import deque

import numpy as np

class 		__a				:



       def __init__(  self						: Any      ):
           lowerCAmelCase_       :						Any							= [2, 1, 2, -1]
           lowerCAmelCase_       :						Optional[int]							= [1, 2, 3, 4]



       def 			A				(  self						: Tuple      ):
           lowerCAmelCase_       :						Optional[int]							= len(self.first_signal      )
           lowerCAmelCase_       :						str							= len(self.second_signal      )

           lowerCAmelCase_       :						str							= max(a__       ,      a__      )

           # create a zero matrix of max_length x max_length
           lowerCAmelCase_       :						Optional[Any]							= [[0] * max_length for i in range(a__      )]

           # fills the smaller signal with zeros to make both signals of same length
           if length_first_signal < length_second_signal:
               self.first_signal += [0] * (max_length - length_first_signal)
           elif length_first_signal > length_second_signal:
               self.second_signal += [0] * (max_length - length_second_signal)
           for i in range(a__      ):
               lowerCAmelCase_       :						Optional[Any]							= deque(self.second_signal      )
               rotated_signal.rotate(a__      )
               for j, item in enumerate(a__      ):
                   matrix[i][j] += item

        # multiply the matrix with the first signal
           lowerCAmelCase_       :						List[Any]							= np.matmul(np.transpose(a__      )       ,      np.transpose(self.first_signal      )      )

           # rounding-off to two decimal places
           return [round(a__       ,      2      ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       _lowerCamelCase      							=   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')
       _lowerCamelCase      							=   parser.parse_args()

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

       _lowerCamelCase      							=   model.state_dict()
       _lowerCamelCase      							=   {}

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

    # Transformer Blocks #
       _lowerCamelCase      							=   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"]:
                                   _lowerCamelCase      							=   state_dict[
                                       F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                                   ]
                     _lowerCamelCase      							=   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"]:
                                   _lowerCamelCase      							=   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"]:
                     _lowerCamelCase      							=   state_dict[F'''{layer}''']
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            _lowerCamelCase      							=   state_dict[F'''lm_head.dense.{w}''']
                            _lowerCamelCase      							=   state_dict[F'''lm_head.layer_norm.{w}''']
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.ln_f.{w}''']
              _lowerCamelCase      							=   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)

