





"""simple docstring"""




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		a		:				Optional[Any]  			=    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''')
		a		:				str  			=    parser.parse_args()

		if args.model_type == "roberta":
				a		:				int  			=    RobertaForMaskedLM.from_pretrained(args.model_name)
				a		:				Dict  			=    '''roberta'''
		elif args.model_type == "gpt2":
				a		:				Tuple  			=    GPTaLMHeadModel.from_pretrained(args.model_name)
				a		:				List[str]  			=    '''transformer'''

		a		:				Dict  			=    model.state_dict()
		a		:				int  			=    {}

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

    # Transformer Blocks #
		a		:				List[str]  			=    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"]:
										a		:				int  			=    state_dict[
										    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						a		:				Union[str, Any]  			=    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"]:
										a		:				int  			=    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"]:
						a		:				Tuple  			=    state_dict[F'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								a		:				Tuple  			=    state_dict[F'''lm_head.dense.{w}''']
								a		:				Tuple  			=    state_dict[F'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						a		:				Any  			=    state_dict[F'''{prefix}.ln_f.{w}''']
				a		:				Union[str, Any]  			=    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"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_nllb import NllbTokenizer
else:
		a		:				Tuple  			=    None


a		:				int  			=    logging.get_logger(__name__)


a		:				int  			=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				Optional[int]  			=    {
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

a		:				int  			=    {
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
a		:				List[Any]  			=    ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      str         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =["""input_ids""", """attention_mask"""]
							lowerCamelCase     :      Union[str, Any]         =NllbTokenizer

							lowerCamelCase     :      List[int]         =[]
							lowerCamelCase     :      List[int]         =[]






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="<mask>"   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=False   ,      **lowerCAmelCase__   ,      )			->				Optional[Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								a   :	Dict			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else mask_token
								a   :	Optional[Any]			=       legacy_behaviour
								super().__init__(
								    vocab_file=lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      src_lang=lowerCAmelCase__   ,      tgt_lang=lowerCAmelCase__   ,      additional_special_tokens=lowerCAmelCase__   ,      legacy_behaviour=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	int			=       vocab_file
								a   :	Any			=       False if not self.vocab_file else True

								a   :	List[str]			=       FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

								self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}   )
								a   :	str			=       {
								    lang_code: self.convert_tokens_to_ids(lowerCAmelCase__   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								a   :	List[Any]			=       src_lang if src_lang is not None else "eng_Latn"
								a   :	str			=       self.convert_tokens_to_ids(self._src_lang   )
								a   :	Any			=       tgt_lang
								self.set_src_lang_special_tokens(self._src_lang   )






							@property
							def 					__a     (     self   )			->				str:
								return self._src_lang






							@src_lang.setter
							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	List[str]			=       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	str			=       [self.sep_token_id]
								a   :	Union[str, Any]			=       [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 + sep + token_ids_a + sep   ) * [0]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								if src_lang is None or tgt_lang is None:
									raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"   )
								a   :	Dict			=       src_lang
								a   :	int			=       self(lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   )
								a   :	Dict			=       self.convert_tokens_to_ids(lowerCAmelCase__   )
								a   :	Any			=       tgt_lang_id
								return inputs






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = "eng_Latn"   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = "fra_Latn"   ,      **lowerCAmelCase__   ,      )			->				BatchEncoding:
								a   :	Optional[int]			=       src_lang
								a   :	int			=       tgt_lang
								return super().prepare_seqaseq_batch(lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   )			->				Tuple:
								return self.set_src_lang_special_tokens(self.src_lang   )






							def 					__a     (     self   )			->				str:
								return self.set_tgt_lang_special_tokens(self.tgt_lang   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	int			=       self.convert_tokens_to_ids(lowerCAmelCase__   )

								if self.legacy_behaviour:
									a   :	Tuple			=       []
									a   :	List[str]			=       [self.eos_token_id, self.cur_lang_code]
								else:
									a   :	int			=       [self.cur_lang_code]
									a   :	int			=       [self.eos_token_id]

								a   :	Union[str, Any]			=       self.convert_ids_to_tokens(self.prefix_tokens   )
								a   :	Any			=       self.convert_ids_to_tokens(self.suffix_tokens   )

								a   :	Any			=       processors.TemplateProcessing(
								    single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens   )   )   ,      )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	str			=       self.convert_tokens_to_ids(lowerCAmelCase__   )
								if self.legacy_behaviour:
									a   :	Optional[Any]			=       []
									a   :	int			=       [self.eos_token_id, self.cur_lang_code]
								else:
									a   :	List[Any]			=       [self.cur_lang_code]
									a   :	List[Any]			=       [self.eos_token_id]

								a   :	int			=       self.convert_ids_to_tokens(self.prefix_tokens   )
								a   :	int			=       self.convert_ids_to_tokens(self.suffix_tokens   )

								a   :	Any			=       processors.TemplateProcessing(
								    single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens   )   )   ,      )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."   )

								if not os.path.isdir(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""   )
									return
								a   :	Any			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)


