






"""simple docstring"""


import argparse

import torch

from transformers import BertForMaskedLM


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

		if args.model_type == "bert":
				__SCREAMING_SNAKE_CASE   =   BertForMaskedLM.from_pretrained(args.model_name)
				__SCREAMING_SNAKE_CASE   =   "bert"
		else:
				raise ValueError('args.model_type should be \"bert\".')

		__SCREAMING_SNAKE_CASE   =   model.state_dict()
		__SCREAMING_SNAKE_CASE   =   {}

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

		__SCREAMING_SNAKE_CASE   =   0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				for w in ["weight", "bias"]:
						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
						]
						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
						]
						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
						]

						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
						]
						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
						]

						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
						]
						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
						]
						__SCREAMING_SNAKE_CASE   =   state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
						]
				std_idx += 1

		__SCREAMING_SNAKE_CASE   =   state_dict["cls.predictions.decoder.weight"]
		__SCREAMING_SNAKE_CASE   =   state_dict["cls.predictions.bias"]
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						__SCREAMING_SNAKE_CASE   =   state_dict[F"""cls.predictions.transform.dense.{w}"""]
						__SCREAMING_SNAKE_CASE   =   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)





from typing import List

from .keymap import KEYMAP, get_character




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						str										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += [key]
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




def 		lowerCAmelCase_ (						*lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						Dict										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += keys
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




class 		__A   (      UpperCamelCase__      ):

						def __new__(		cls :Dict     ,			__snake_case :Optional[Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=super().__new__(cls     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										if not hasattr(__snake_case     ,			"""key_handler"""			):
														setattr(__snake_case     ,			"""key_handler"""     ,			{}			)
										setattr(__snake_case     ,			"""handle_input"""     ,			KeyHandler.handle_input			)

										for value in attrs.values():
														__magic_name__						:						int										=getattr(__snake_case     ,			"""handle_key"""     ,			[]			)
														for key in handled_keys:
																		__magic_name__						:						List[str]										=value
										return new_cls


						@staticmethod
						def      A__    (		cls :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=get_character()
										if char != KEYMAP["undefined"]:
														__magic_name__						:						Optional[int]										=ord(__snake_case			)
										__magic_name__						:						int										=cls.key_handler.get(__snake_case			)
										if handler:
														__magic_name__						:						Dict										=char
														return handler(cls			)
										else:
														return None




def 		lowerCAmelCase_ (						cls ):
				return KeyHandler(cls.__name__						,  cls.__bases__						,  cls.__dict__.copy() )




