






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 snake_case__   :						Union[str, Any]													=   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""")
 snake_case__   :						Dict													=   parser.parse_args()

 if args.model_type == "roberta":
  snake_case__   :						Any													=   RobertaForMaskedLM.from_pretrained(args.model_name)
  snake_case__   :						Dict													=   """roberta"""
 elif args.model_type == "gpt2":
  snake_case__   :						Optional[int]													=   GPTaLMHeadModel.from_pretrained(args.model_name)
  snake_case__   :						List[str]													=   """transformer"""

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

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

    # Transformer Blocks #
 snake_case__   :						Optional[Any]													=   0
 for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
  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"]:
     snake_case__   :						Union[str, Any]													=   state_dict[
         f'{prefix}.h.{teacher_idx}.{layer}.{w}'
     ]
   snake_case__   :						Optional[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"]:
     snake_case__   :						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"]:
   snake_case__   :						Optional[int]													=   state_dict[f'{layer}']
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    snake_case__   :						List[str]													=   state_dict[f'lm_head.dense.{w}']
    snake_case__   :						Dict													=   state_dict[f'lm_head.layer_norm.{w}']
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   snake_case__   :						Optional[int]													=   state_dict[f'{prefix}.ln_f.{w}']
  snake_case__   :						str													=   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)













import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'			)
        UpperCamelCase_						=  input_file.read()
        UpperCamelCase_						=  regexp.search(_UpperCAmelCase			)

     return match




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'		,		re.DOTALL			)
        UpperCamelCase_						=  input_file.read()
        # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
        UpperCamelCase_						=  regexp.finditer(_UpperCAmelCase			)

     UpperCamelCase_						=  [match for match in matches if match is not None and match.group(1			) is not None]
     return matches[0] if matches else None




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_encoding_on_file_open(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_print_statements(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""			)





