



"""simple docstring"""

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

     lowercase__    :  Dict							    =	model.state_dict()
     lowercase__    :  List[Any]							    =	{}

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

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: Dict      =       {"vocab_file": "spiece.model"}

_UpperCAmelCase					: Dict      =       {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

_UpperCAmelCase					: int      =       {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       int	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       List[Any]	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       List[Any]	     =				["input_ids", "attention_mask"]
		__lowercase       :       List[int]	     =				[]



		def __init__( self				,      A_				,      A_="<unk>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_="[SEP]"				,      A_="[MASK]"				,      A_="[CLS]"				,      A_ = None				,      **A_				,      )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else bos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else eos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else unk_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else pad_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else cls_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else sep_token

				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else mask_token

				UpperCamelCase		=     {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=A_				,      eos_token=A_				,      unk_token=A_				,      pad_token=A_				,      sep_token=A_				,      mask_token=A_				,      cls_token=A_				,      sp_model_kwargs=self.sp_model_kwargs				,      **A_				,      )

				UpperCamelCase		=     vocab_file

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(A_       )



		@property
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return self.sp_model.get_piece_size()



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
				vocab.update(self.added_tokens_encoder       )
				return vocab



		def __getstate__( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.__dict__.copy()
				UpperCamelCase		=     None
				return state



		def __setstate__( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     d

				# for backward compatibility
				if not hasattr(self				,      'sp_model_kwargs'       ):
						UpperCamelCase		=     {}

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(self.vocab_file       )



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				return self.sp_model.encode(A_				,      out_type=A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return self.sp_model.piece_to_id(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.sp_model.IdToPiece(A_       )
				return token



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				UpperCamelCase		=     ''
				UpperCamelCase		=     False
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								if not prev_is_special:
										out_string += " "
								out_string += self.sp_model.decode(A_       ) + token
								UpperCamelCase		=     True
								UpperCamelCase		=     []
						else:
								current_sub_tokens.append(A_       )
								UpperCamelCase		=     False
				out_string += self.sp_model.decode(A_       )
				return out_string.strip()



		def __UpperCamelCase ( self				,      A_				,      A_ = False				,      A_ = None				,      A_ = True				,      **A_				,      )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.pop('use_source_tokenizer'				,      A_       )

				UpperCamelCase		=     self.convert_ids_to_tokens(A_				,      skip_special_tokens=A_       )

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				UpperCamelCase		=     []
				UpperCamelCase		=     []
				for token in filtered_tokens:
						if skip_special_tokens and token in self.all_special_ids:
								continue
						if token in self.added_tokens_encoder:
								if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(A_       )       )
										UpperCamelCase		=     []
								sub_texts.append(A_       )
						else:
								current_sub_text.append(A_       )
				if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(A_       )       )

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
						UpperCamelCase		=     re.sub(r' (\[(MASK|SEP)\])'				,      r'\1'				,      ' '.join(A_       )       )
				else:
						UpperCamelCase		=     ''.join(A_       )

				UpperCamelCase		=     (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
						UpperCamelCase		=     self.clean_up_tokenization(A_       )
						return clean_text
				else:
						return text



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     os.path.join(
				    A_				,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

				if os.path.abspath(self.vocab_file       ) != os.path.abspath(A_       ) and os.path.isfile(self.vocab_file       ):
						copyfile(self.vocab_file				,      A_       )
				elif not os.path.isfile(self.vocab_file       ):
						with open(A_				,      'wb'       ) as fi:
								UpperCamelCase		=     self.sp_model.serialized_model_proto()
								fi.write(A_       )

				return (out_vocab_file,)



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase		=     [self.cls_token_id]
				UpperCamelCase		=     [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					List[int]:
				"""simple docstring"""
				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=A_				,      token_ids_a=A_				,      already_has_special_tokens=A_       )

				if token_ids_a is None:
						return [1] + ([0] * len(A_       )) + [1]
				return [1] + ([0] * len(A_       )) + [1] + ([0] * len(A_       )) + [1]



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]
				UpperCamelCase		=     [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]
