

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
						lowerCAmelCase							:  str            =						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							:  Optional[int]            =						parser.parse_args()

						if args.model_type == "roberta":
												lowerCAmelCase							:  Any            =						RobertaForMaskedLM.from_pretrained(args.model_name)
												lowerCAmelCase							:  Optional[int]            =						"""roberta"""
						elif args.model_type == "gpt2":
												lowerCAmelCase							:  int            =						GPTaLMHeadModel.from_pretrained(args.model_name)
												lowerCAmelCase							:  Tuple            =						"""transformer"""

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

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

    # Transformer Blocks #
						lowerCAmelCase							:  Optional[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							:  Any            =						state_dict[
																														    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																														]
																		lowerCAmelCase							:  Tuple            =						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							:  Dict            =						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							:  Dict            =						state_dict[f'''{layer}''']
												if args.vocab_transform:
																		for w in ["weight", "bias"]:
																								lowerCAmelCase							:  Tuple            =						state_dict[f'''lm_head.dense.{w}''']
																								lowerCAmelCase							:  List[Any]            =						state_dict[f'''lm_head.layer_norm.{w}''']
						elif args.model_type == "gpt2":
												for w in ["weight", "bias"]:
																		lowerCAmelCase							:  Optional[int]            =						state_dict[f'''{prefix}.ln_f.{w}''']
												lowerCAmelCase							:  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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase							:  Any            =						logging.get_logger(__name__)

lowerCAmelCase							:  Dict            =						{"""vocab_file""": """vocab.txt"""}

lowerCAmelCase							:  List[str]            =						{
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

lowerCAmelCase							:  List[Any]            =						{
    """YituTech/conv-bert-base""": 512,
    """YituTech/conv-bert-medium-small""": 512,
    """YituTech/conv-bert-small""": 512,
}


lowerCAmelCase							:  Tuple            =						{
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							List[Any]      							=					PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase       :							Optional[Any]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							Dict      							=					ConvBertTokenizer



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[int]=None       ,						lowerCAmelCase__  :					List[str]=None       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					Any="[UNK]"       ,						lowerCAmelCase__  :					Optional[Any]="[SEP]"       ,						lowerCAmelCase__  :					Any="[PAD]"       ,						lowerCAmelCase__  :					Dict="[CLS]"       ,						lowerCAmelCase__  :					Dict="[MASK]"       ,						lowerCAmelCase__  :					Tuple=True       ,						lowerCAmelCase__  :					str=None       ,						**lowerCAmelCase__  :					Dict       ,						):
									super().__init__(
									    lowerCAmelCase__       ,						tokenizer_file=lowerCAmelCase__       ,						do_lower_case=lowerCAmelCase__       ,						unk_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						tokenize_chinese_chars=lowerCAmelCase__       ,						strip_accents=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get("lowercase"       ,						lowerCAmelCase__) != do_lower_case
									    or normalizer_state.get("strip_accents"       ,						lowerCAmelCase__) != strip_accents
									    or normalizer_state.get("handle_chinese_chars"       ,						lowerCAmelCase__) != tokenize_chinese_chars
									):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	getattr(lowerCAmelCase__       ,						normalizer_state.pop("type"))
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_lower_case
															SCREAMING_SNAKE_CASE_:			List[str]  		=	strip_accents
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenize_chinese_chars
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	normalizer_class(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_lower_case



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Union[str, Any]=None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

									return output



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[self.cls_token_id]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep) * [0]
									return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									SCREAMING_SNAKE_CASE_:			Any  		=	self._tokenizer.model.save(lowerCAmelCase__       ,						name=lowerCAmelCase__)
									return tuple(lowerCAmelCase__)






