


'''simple docstring'''

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 lowerCamelCase				:  Tuple    		=      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				:  int    		=      parser.parse_args()

 if args.model_type == "roberta":
  lowerCamelCase				:  Union[str, Any]    		=      RobertaForMaskedLM.from_pretrained(args.model_name)
  lowerCamelCase				:  Optional[Any]    		=      'roberta'
 elif args.model_type == "gpt2":
  lowerCamelCase				:  int    		=      GPTaLMHeadModel.from_pretrained(args.model_name)
  lowerCamelCase				:  Union[str, Any]    		=      'transformer'

 lowerCamelCase				:  Optional[int]    		=      model.state_dict()
 lowerCamelCase				:  Dict    		=      {}

 # Embeddings #
 if args.model_type == "gpt2":
  for param_name in ["wte.weight", "wpe.weight"]:
   lowerCamelCase				:  Union[str, Any]    		=      state_dict[f"""{prefix}.{param_name}"""]
 else:
  for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
   lowerCamelCase				:  Dict    		=      f"""{prefix}.embeddings.{w}.weight"""
   lowerCamelCase				:  Tuple    		=      state_dict[param_name]
  for w in ["weight", "bias"]:
   lowerCamelCase				:  Optional[Any]    		=      f"""{prefix}.embeddings.LayerNorm.{w}"""
   lowerCamelCase				:  Union[str, Any]    		=      state_dict[param_name]

    # Transformer Blocks #
 lowerCamelCase				:  Union[str, Any]    		=      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				:  Union[str, Any]    		=      state_dict[
         f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
     ]
   lowerCamelCase				:  Any    		=      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				:  Optional[int]    		=      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				:  int    		=      state_dict[f"""{layer}"""]
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    lowerCamelCase				:  List[str]    		=      state_dict[f"""lm_head.dense.{w}"""]
    lowerCamelCase				:  Optional[Any]    		=      state_dict[f"""lm_head.layer_norm.{w}"""]
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   lowerCamelCase				:  Optional[Any]    		=      state_dict[f"""{prefix}.ln_f.{w}"""]
  lowerCamelCase				:  Optional[int]    		=      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)








'''simple docstring'''

from __future__ import annotations

from collections import deque
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       str  ,		UpperCamelCase       :       list[str]					):

             '''simple docstring'''







             lowercase__      =					[]
             self.adlist.append(
                 {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}					)

             for keyword in keywords:
                    self.add_keyword(UpperCamelCase					)
             self.set_fail_transitions()



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             for state in self.adlist[current_state]["next_states"]:
                    if char == self.adlist[state]["value"]:
                           return state
             return None



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					0
             for character in keyword:
                    lowercase__      =					self.find_next_state(UpperCamelCase  ,		UpperCamelCase					)
                    if next_state is None:
                           self.adlist.append(
                               {
                                   '''value''': character,
                                   '''next_states''': [],
                                   '''fail_state''': 0,
                                   '''output''': [],
                               }					)
                           self.adlist[current_state]["next_states"].append(len(self.adlist					) - 1					)
                           lowercase__      =					len(self.adlist					) - 1
                    else:
                           lowercase__      =					next_state
             self.adlist[current_state]["output"].append(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					deque()
             for node in self.adlist[0]["next_states"]:
                    q.append(UpperCamelCase					)
                    lowercase__      =					0
             while q:
                    lowercase__      =					q.popleft()
                    for child in self.adlist[r]["next_states"]:
                           q.append(UpperCamelCase					)
                           lowercase__      =					self.adlist[r]['''fail_state''']
                           while (
                               self.find_next_state(UpperCamelCase  ,		self.adlist[child]['''value''']					) is None
                               and state != 0
                           ):
                                  lowercase__      =					self.adlist[state]['''fail_state''']
                           lowercase__      =					self.find_next_state(
                               UpperCamelCase  ,		self.adlist[child]['''value''']					)
                           if self.adlist[child]["fail_state"] is None:
                                  lowercase__      =					0
                           lowercase__      =					(
                               self.adlist[child]['''output''']
                               + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
                           )






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					{}  # returns a dict with keywords and list of its occurrences
             lowercase__      =					0
             for i in range(len(UpperCamelCase					)					):
                    while (
                        self.find_next_state(UpperCamelCase  ,		string[i]					) is None
                        and current_state != 0
                    ):
                           lowercase__      =					self.adlist[current_state]['''fail_state''']
                    lowercase__      =					self.find_next_state(UpperCamelCase  ,		string[i]					)
                    if next_state is None:
                           lowercase__      =					0
                    else:
                           lowercase__      =					next_state
                           for key in self.adlist[current_state]["output"]:
                                  if key not in result:
                                         lowercase__      =					[]
                                  result[key].append(i - len(UpperCamelCase					) + 1					)
             return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()




