






'''simple docstring'''







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)












'''simple docstring'''







def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):

				def get_matched_characters(__A							:   str						,		__A							:   str      ) -> str:
								a_		:  Union[str, Any]											=      []
								a_		:  int											=      min(len(_stra      )						,		len(_stra      )      ) // 2
								for i, l in enumerate(_stra      ):
												a_		:  Any											=      int(max(0						,		i - limit      )      )
												a_		:  Union[str, Any]											=      int(min(i + limit + 1						,		len(_stra      )      )      )
												if l in _stra[left:right]:
																matched.append(__A      )
																a_		:  Any											=      f'{_stra[0:_stra.index(__A      )]} {_stra[_stra.index(__A      ) + 1:]}'

								return "".join(__A      )

				# matching characters
				a_		:  Optional[Any]											=      get_matched_characters(__A						,		__A      )
				a_		:  int											=      get_matched_characters(__A						,		__A      )
				a_		:  Any											=      len(__A      )

				# transposition
				a_		:  List[Any]											=      (
				    len([(ca, ca) for ca, ca in zip(__A						,		__A      ) if ca != ca]      ) // 2
				)

				if not match_count:
								a_		:  Dict											=      0.0
				else:
								a_		:  Optional[int]											=      (
								    1
								    / 3
								    * (
								        match_count / len(__A      )
								        + match_count / len(__A      )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				a_		:  List[str]											=      0
				for ca, ca in zip(stra[:4]						,		stra[:4]      ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))




