'''simple docstring'''

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

import sentencepiece as spm

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


UpperCamelCase__     :	Tuple				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			'▁'

UpperCamelCase__     :	List[Any]				=			{'vocab_file': 'sentencepiece.bpe.model'}

UpperCamelCase__     :	List[Any]				=			{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

UpperCamelCase__     :	Tuple				=			{
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
UpperCamelCase__     :	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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      int      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      Any      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Union[str, Any]      							=   ['''input_ids''', '''attention_mask''']

      UpperCAmelCase_      :      List[int]      							=   []
      UpperCAmelCase_      :      List[int]      							=   []




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<s>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<mask>"					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_ = None					,lowerCamelCase_=None					,lowerCamelCase_=False					,**lowerCamelCase_					,)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else mask_token

         UpperCAmelCase__							:				List[Any]   				=  {} if sp_model_kwargs is None else sp_model_kwargs
         UpperCAmelCase__							:				Dict   				=  legacy_behaviour

         super().__init__(
             bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,tokenizer_file=lowerCamelCase_					,src_lang=lowerCamelCase_					,tgt_lang=lowerCamelCase_					,additional_special_tokens=lowerCamelCase_					,sp_model_kwargs=self.sp_model_kwargs					,legacy_behaviour=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				List[Any]   				=  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Optional[int]   				=  vocab_file
         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
         # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
         # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

         # Mimic fairseq token-to-id alignment for the first 4 token
         UpperCAmelCase__							:				List[str]   				=  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         UpperCAmelCase__							:				List[str]   				=  1

         UpperCAmelCase__							:				List[str]   				=  len(self.sp_model							)
         UpperCAmelCase__							:				str   				=  {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase_							)
         }
         UpperCAmelCase__							:				Any   				=  {v: k for k, v in self.lang_code_to_id.items()}
         UpperCAmelCase__							:				Dict   				=  len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
         UpperCAmelCase__							:				Optional[Any]   				=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         UpperCAmelCase__							:				Union[str, Any]   				=  list(self.lang_code_to_id.keys()							)

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

         UpperCAmelCase__							:				Tuple   				=  src_lang if src_lang is not None else '''eng_Latn'''
         UpperCAmelCase__							:				Optional[Any]   				=  self.lang_code_to_id[self._src_lang]
         UpperCAmelCase__							:				str   				=  tgt_lang
         self.set_src_lang_special_tokens(self._src_lang							)




      def __getstate__(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				Union[str, Any]   				=  None
         UpperCAmelCase__							:				str   				=  self.sp_model.serialized_model_proto()
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  d

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

         UpperCAmelCase__							:				Optional[Any]   				=  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         return self._src_lang




      @src_lang.setter
      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase_					,token_ids_a=lowerCamelCase_					,already_has_special_tokens=lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  [1] * len(self.prefix_tokens							)
         UpperCAmelCase__							:				List[str]   				=  [1] * len(self.suffix_tokens							)
         if token_ids_a is None:
            return prefix_ones + ([0] * len(lowerCamelCase_							)) + suffix_ones
         return prefix_ones + ([0] * len(lowerCamelCase_							)) + ([0] * len(lowerCamelCase_							)) + suffix_ones




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         if src_lang is None or tgt_lang is None:
            raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
         UpperCAmelCase__							:				int   				=  src_lang
         UpperCAmelCase__							:				Any   				=  self(lowerCamelCase_					,add_special_tokens=lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.convert_tokens_to_ids(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  tgt_lang_id
         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {self.convert_ids_to_tokens(lowerCamelCase_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



         return self.sp_model.encode(lowerCamelCase_					,out_type=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase__							:				List[Any]   				=  self.sp_model.PieceToId(lowerCamelCase_							)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  ''''''.join(lowerCamelCase_							).replace(lowerCamelCase_					,''' '''							).strip()
         return out_string




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
            return
         UpperCAmelCase__							:				Dict   				=  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_							) and os.path.isfile(self.vocab_file							):
            copyfile(self.vocab_file					,lowerCamelCase_							)
         elif not os.path.isfile(self.vocab_file							):
            with open(lowerCamelCase_					,'''wb'''							) as fi:
               UpperCAmelCase__							:				Optional[Any]   				=  self.sp_model.serialized_model_proto()
               fi.write(lowerCamelCase_							)

         return (out_vocab_file,)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = "eng_Latn"					,lowerCamelCase_ = None					,lowerCamelCase_ = "fra_Latn"					,**lowerCamelCase_					,)			->			BatchEncoding:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  src_lang
         UpperCAmelCase__							:				Any   				=  tgt_lang
         return super().prepare_seqaseq_batch(lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return self.set_src_lang_special_tokens(self.src_lang							)




      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return self.set_tgt_lang_special_tokens(self.tgt_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.lang_code_to_id[src_lang]
         if self.legacy_behaviour:
            UpperCAmelCase__							:				int   				=  []
            UpperCAmelCase__							:				List[Any]   				=  [self.eos_token_id, self.cur_lang_code]
         else:
            UpperCAmelCase__							:				int   				=  [self.cur_lang_code]
            UpperCAmelCase__							:				Tuple   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.lang_code_to_id[lang]
         if self.legacy_behaviour:
            UpperCAmelCase__							:				Any   				=  []
            UpperCAmelCase__							:				str   				=  [self.eos_token_id, self.cur_lang_code]
         else:
            UpperCAmelCase__							:				Union[str, Any]   				=  [self.cur_lang_code]
            UpperCAmelCase__							:				Optional[int]   				=  [self.eos_token_id]





'''simple docstring'''

import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

UpperCamelCase__     :	Optional[int]				=			{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[Any]  ,			_A					:				List[Any]  ,			_A					:				Optional[int]  ,			_A					:				Dict=False  ,			_A					:				List[Any]=True    ):
   '''simple docstring'''
   if model_type not in MODEL_CLASSES:
      raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}.'''    )

   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  MODEL_CLASSES[model_type]

   # Initialise TF model
   if config_file in aws_config_map:
      UpperCAmelCase__							:				str   				=  cached_file(_A  ,			_A  ,			force_download=not use_cached_models    )
   UpperCAmelCase__							:				Optional[Any]   				=  config_class.from_json_file(_A    )
   UpperCAmelCase__							:				Optional[Any]   				=  True
   UpperCAmelCase__							:				List[str]   				=  True
   print(F'''Building TensorFlow model from configuration: {config}'''    )
   UpperCAmelCase__							:				Tuple   				=  model_class(_A    )

   # Load weights from tf checkpoint
   if pytorch_checkpoint_path in aws_config_map.keys():
      UpperCAmelCase__							:				Optional[int]   				=  cached_file(
          _A  ,			_A  ,			force_download=not use_cached_models    )
   # Load PyTorch checkpoint in tf2 model:
   UpperCAmelCase__							:				int   				=  load_pytorch_checkpoint_in_tfa_model(_A  ,			_A    )

   if compare_with_pt_model:
      UpperCAmelCase__							:				Optional[int]   				=  tf_model(tf_model.dummy_inputs  ,			training=_A    )  # build the network

      UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(_A  ,			map_location='''cpu'''    )
      UpperCAmelCase__							:				Union[str, Any]   				=  pt_model_class.from_pretrained(
          pretrained_model_name_or_path=_A  ,			config=_A  ,			state_dict=_A    )

      with torch.no_grad():
         UpperCAmelCase__							:				int   				=  pt_model(**pt_model.dummy_inputs    )

      UpperCAmelCase__							:				List[str]   				=  pto[0].numpy()
      UpperCAmelCase__							:				Union[str, Any]   				=  tfo[0].numpy()
      UpperCAmelCase__							:				Optional[int]   				=  np.amax(np.abs(np_pt - np_tf    )    )
      print(F'''Max absolute difference between models outputs {diff}'''    )
      assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

   # Save pytorch-model
   print(F'''Save TensorFlow model to {tf_dump_path}'''    )
   tf_model.save_weights(_A  ,			save_format='''h5'''    )







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Union[str, Any]  ,			_A					:				Union[str, Any]=None  ,			_A					:				Optional[Any]=None  ,			_A					:				Dict=False  ,			_A					:				str=False  ,			_A					:				Optional[Any]=False  ,			_A					:				List[str]=False  ,			):
   '''simple docstring'''
   if args_model_type is None:
      UpperCAmelCase__							:				Any   				=  list(MODEL_CLASSES.keys()    )
   else:
      UpperCAmelCase__							:				List[Any]   				=  [args_model_type]

   for j, model_type in enumerate(_A  ,			start=1    ):
      print('''=''' * 1_00    )
      print(F''' Converting model type {j}/{len(_A    )}: {model_type}'''    )
      print('''=''' * 1_00    )
      if model_type not in MODEL_CLASSES:
         raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()    )}.'''    )

      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  MODEL_CLASSES[model_type]

      if model_shortcut_names_or_path is None:
         UpperCAmelCase__							:				int   				=  list(aws_model_maps.keys()    )
      if config_shortcut_names_or_path is None:
         UpperCAmelCase__							:				Tuple   				=  model_shortcut_names_or_path

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(_A  ,			_A    )  ,			start=1    ):
         print('''-''' * 1_00    )
         if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
            if not only_convert_finetuned_models:
               print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''    )
               continue
            UpperCAmelCase__							:				Tuple   				=  model_shortcut_name
         elif only_convert_finetuned_models:
            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''    )
            continue
         print(
             F'''    Converting checkpoint {i}/{len(_A    )}: {model_shortcut_name} - model_type {model_type}'''    )
         print('''-''' * 1_00    )

         if config_shortcut_name in aws_config_map:
            UpperCAmelCase__							:				Optional[int]   				=  cached_file(_A  ,			_A  ,			force_download=not use_cached_models    )
         else:
            UpperCAmelCase__							:				Optional[Any]   				=  config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            UpperCAmelCase__							:				Any   				=  cached_file(_A  ,			_A  ,			force_download=not use_cached_models    )
         else:
            UpperCAmelCase__							:				List[str]   				=  model_shortcut_name

         if os.path.isfile(_A    ):
            UpperCAmelCase__							:				Optional[int]   				=  '''converted_model'''

         convert_pt_checkpoint_to_tf(
             model_type=_A  ,			pytorch_checkpoint_path=_A  ,			config_file=_A  ,			tf_dump_path=os.path.join(_A  ,			model_shortcut_name + '''-tf_model.h5'''    )  ,			compare_with_pt_model=_A  ,			)
         if remove_cached_files:
            os.remove(_A    )
            os.remove(_A    )


if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
  )
  parser.add_argument(
      '--model_type',
      default=None,
      type=str,
      help=(
          f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
          'convert all the models from AWS.'
      ),
  )
  parser.add_argument(
      '--pytorch_checkpoint_path',
      default=None,
      type=str,
      help=(
          'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
          'If not given, will download and convert all the checkpoints from AWS.'
      ),
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      help=(
          'The config json file corresponding to the pre-trained model. \n'
          'This specifies the model architecture. If not given and '
          '--pytorch_checkpoint_path is not given or is a shortcut name '
          'use the configuration associated to the shortcut name on the AWS'
      ),
  )
  parser.add_argument(
      '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
  )
  parser.add_argument(
      '--use_cached_models',
      action='store_true',
      help='Use cached models if possible instead of updating to latest checkpoint versions.',
  )
  parser.add_argument(
      '--remove_cached_files',
      action='store_true',
      help='Remove pytorch models after conversion (save memory when converting in batches).',
  )
  parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
  UpperCamelCase__     :	List[str]				=			parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )




