

from __future__ import annotations

import os
from typing import Any

import requests

lowerCAmelCase							:  Tuple   	=						"""https://api.github.com"""

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCAmelCase							:  Union[str, Any]   	=						BASE_URL + """/user"""

# https://github.com/settings/tokens
lowerCAmelCase							:  Dict   	=						os.environ.get("""USER_TOKEN""", """""")







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       {
						    "Authorization": f"token {auth_token}",
						    "Accept": "application/vnd.github.v3+json",
						}
						return requests.get(_UpperCAmelCase				,	headers=_UpperCAmelCase    ).json()


if __name__ == "__main__":  # pragma: no cover
						if USER_TOKEN:
												for key, value in fetch_github_info(USER_TOKEN).items():
																		print(f'''{key}: {value}''')
						else:
												raise ValueError("""'USER_TOKEN' field cannot be empty.""")










'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 lowerCamelCase							:   List[str]  						=	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')
 lowerCamelCase							:   str  						=	parser.parse_args()

 if args.model_type == "bert":
  lowerCamelCase							:   List[Any]  						=	BertForMaskedLM.from_pretrained(args.model_name)
  lowerCamelCase							:   Any  						=	'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 lowerCamelCase							:   int  						=	model.state_dict()
 lowerCamelCase							:   int  						=	{}

 for w in ["word_embeddings", "position_embeddings"]:
  lowerCamelCase							:   Any  						=	state_dict[f"""{prefix}.embeddings.{w}.weight"""]
 for w in ["weight", "bias"]:
  lowerCamelCase							:   Any  						=	state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

 lowerCamelCase							:   Tuple  						=	0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
   ]
   lowerCamelCase							:   List[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
   ]

   lowerCamelCase							:   Tuple  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
   ]
   lowerCamelCase							:   Optional[int]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
   ]

   lowerCamelCase							:   Optional[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
   ]
   lowerCamelCase							:   Any  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
   ]
  std_idx += 1

 lowerCamelCase							:   Optional[int]  						=	state_dict['cls.predictions.decoder.weight']
 lowerCamelCase							:   str  						=	state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   str  						=	state_dict[f"""cls.predictions.transform.dense.{w}"""]
   lowerCamelCase							:   Any  						=	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)




