'''simple docstring'''





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
    raise Exception('requires fairseq >= 1.0.0a')

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

SCREAMING_SNAKE_CASE_:					Tuple  ='Hello world! cécé herlolip'





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			bool      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       FairseqRobertaModel.from_pretrained(snake_case_      )
     roberta.eval()  # disable dropout
     UpperCAmelCase_	   =       roberta.model.encoder.sentence_encoder
     UpperCAmelCase_	   =       XLMRobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,   hidden_size=roberta.cfg.model.encoder_embed_dim ,   num_hidden_layers=roberta.cfg.model.encoder_layers ,   num_attention_heads=roberta.cfg.model.encoder_attention_heads ,   intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,   max_position_embeddings=5_14 ,   type_vocab_size=1 ,   layer_norm_eps=1E-5 ,   )
     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

     print("Our RoBERTa config:" ,   snake_case_      )

     UpperCAmelCase_	   =       XLMRobertaXLForSequenceClassification(snake_case_      ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_      )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase_	   =       roberta_sent_encoder.embed_tokens.weight
     UpperCAmelCase_	   =       roberta_sent_encoder.embed_positions.weight
     UpperCAmelCase_	   =       torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.

     UpperCAmelCase_	   =       roberta_sent_encoder.layer_norm.weight
     UpperCAmelCase_	   =       roberta_sent_encoder.layer_norm.bias

     for i in range(config.num_hidden_layers      ):
          # Encoder: start of layer
          UpperCAmelCase_	   =       model.roberta.encoder.layer[i]
          UpperCAmelCase_	   =       roberta_sent_encoder.layers[i]

          UpperCAmelCase_	   =       layer.attention
          UpperCAmelCase_	   =       roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn_layer_norm.bias

          # self attention
          UpperCAmelCase_	   =       layer.attention.self
          assert (
              roberta_layer.self_attn.k_proj.weight.data.shape
              == roberta_layer.self_attn.q_proj.weight.data.shape
              == roberta_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)      )
          )

          UpperCAmelCase_	   =       roberta_layer.self_attn.q_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.q_proj.bias
          UpperCAmelCase_	   =       roberta_layer.self_attn.k_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.k_proj.bias
          UpperCAmelCase_	   =       roberta_layer.self_attn.v_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_	   =       layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase_	   =       roberta_layer.self_attn.out_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          UpperCAmelCase_	   =       roberta_layer.final_layer_norm.weight
          UpperCAmelCase_	   =       roberta_layer.final_layer_norm.bias

          # intermediate
          UpperCAmelCase_	   =       layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_	   =       roberta_layer.fca.weight
          UpperCAmelCase_	   =       roberta_layer.fca.bias

          # output
          UpperCAmelCase_	   =       layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_	   =       roberta_layer.fca.weight
          UpperCAmelCase_	   =       roberta_layer.fca.bias
          # end of layer

     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].dense.weight
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].dense.bias
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].out_proj.weight
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.dense.weight
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.dense.bias
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.weight
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase_	   =       roberta.encode(snake_case_      ).unsqueeze(0      )  # batch of size 1

     UpperCAmelCase_	   =       model(snake_case_      )[0]
     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_      )      )
     else:
          UpperCAmelCase_	   =       roberta.model(snake_case_      )[0]
     print(our_output.shape ,   their_output.shape      )
     UpperCAmelCase_	   =       torch.max(torch.abs(our_output - their_output      )      ).item()
     print(f"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
     UpperCAmelCase_	   =       torch.allclose(snake_case_ ,   snake_case_ ,   atol=1E-3      )
     print("Do both models output the same tensors?" ,   "🔥" if success else "💩"      )
     if not success:
          raise Exception("Something went wRoNg"      )

     pathlib.Path(snake_case_      ).mkdir(parents=snake_case_ ,   exist_ok=snake_case_      )
     print(f"""Saving model to {pytorch_dump_folder_path}"""      )
     model.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--classification_head', action='store_true', help='Whether to convert a final classification head.'
    )
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )





'''simple docstring'''





import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_:					Tuple  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[str]  ={'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

SCREAMING_SNAKE_CASE_:					Optional[int]  ={
    'abeja/gpt-neox-japanese-2.7b': 20_48,
}





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Tuple      )   ->				Union[str, Any]:


     '''simple docstring'''




     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       json.loads(f.read()      )

     UpperCAmelCase_	   =       collections.OrderedDict()
     UpperCAmelCase_	   =       collections.OrderedDict()
     UpperCAmelCase_	   =       collections.OrderedDict()
     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       f.readlines()
     UpperCAmelCase_	   =       [[t.rstrip("\n"      )] if (t == "," or "," not in t) else t.rstrip("\n"      ).split(","      ) for t in token]
     for idx, b in enumerate(snake_case_      ):
          UpperCAmelCase_	   =       b
          UpperCAmelCase_	   =       idx
          for wd in b:
               UpperCAmelCase_	   =       idx

     return vocab, raw_vocab, ids_to_tokens, emoji




class 	__A				(			UpperCamelCase__     ):
    a__	:						List[str]	       =  VOCAB_FILES_NAMES
    a__	:						Tuple	       =  PRETRAINED_VOCAB_FILES_MAP
    a__	:						Union[str, Any]	       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Optional[Any]	       =  ["""input_ids""", """attention_mask"""]



    def __init__(self   :   Any       ,      __a   :   List[Any]       ,      __a   :   Dict       ,      __a   :   int="<|endoftext|>"       ,      __a   :   Union[str, Any]="<|endoftext|>"       ,      __a   :   int="<|startoftext|>"       ,      __a   :   Tuple="<|endoftext|>"       ,      __a   :   Optional[int]=False       ,      **__a   :   int       ,      ):
         super().__init__(
             unk_token=__a       ,      pad_token=__a       ,      bos_token=__a       ,      eos_token=__a       ,      do_clean_text=__a       ,      **__a       ,      )
         if not os.path.isfile(__a			):
              raise ValueError(
                  f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                  " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"			)
         if not os.path.isfile(__a			):
              raise ValueError(
                  f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                  " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"			)
         UpperCAmelCase_	   =       do_clean_text
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       load_vocab_and_emoji(__a       ,      __a			)
         UpperCAmelCase_	   =       SubWordJapaneseTokenizer(
             vocab=self.vocab       ,      ids_to_tokens=self.ids_to_tokens       ,      emoji=self.emoji			)



    @property
    def  _lowercase						(self   :   Optional[Any]			):
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab			)



    def  _lowercase						(self   :   List[Any]			):
         return dict(self.raw_vocab       ,      **self.added_tokens_encoder			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   int			):
         return self.subword_tokenizer.tokenize(__a       ,      clean=self.do_clean_text			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   List[str]			):
         return self.vocab.get(__a       ,      self.vocab.get(self.unk_token			)			)



    def  _lowercase						(self   :   int       ,      __a   :   List[Any]			):
         return self.subword_tokenizer.convert_id_to_token(__a			)



    def  _lowercase						(self   :   Dict       ,      __a   :   str			):
         UpperCAmelCase_	   =       "".join(__a			).strip()
         return out_string



    def  _lowercase						(self   :   int       ,      __a   :   "Conversation"			):
         UpperCAmelCase_	   =       []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a       ,      add_special_tokens=__a			) + [self.eos_token_id]			)

         if len(__a			) > self.model_max_length:
              UpperCAmelCase_	   =       input_ids[-self.model_max_length :]
         return input_ids






    def  _lowercase						(self   :   int       ,      __a   :   str       ,      __a   :   Optional[str] = None			):
         UpperCAmelCase_	   =       0
         if os.path.isdir(__a			):
              UpperCAmelCase_	   =       os.path.join(
                  __a       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
              UpperCAmelCase_	   =       os.path.join(
                  __a       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]			)
         else:
              UpperCAmelCase_	   =       (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
              )
              UpperCAmelCase_	   =       (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
              )
         with open(__a       ,      "w"       ,      encoding="utf-8"			) as writer:
              for token_index, token in self.ids_to_tokens.items():
                   if index != token_index:
                        logger.warning(
                            f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                            " Please check that the vocabulary is not corrupted!"			)
                        UpperCAmelCase_	   =       token_index
                   writer.write(",".join(__a			) + "\n"			)
                   index += 1
         with open(__a       ,      "w"       ,      encoding="utf-8"			) as writer:
              json.dump(self.emoji       ,      __a			)
         return vocab_file, emoji_file




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[Any]       ,      __a   :   Dict       ,      __a   :   Any       ,      __a   :   int			):
         UpperCAmelCase_	   =       vocab  # same as swe
         UpperCAmelCase_	   =       ids_to_tokens  # same as bpe
         UpperCAmelCase_	   =       emoji
         UpperCAmelCase_	   =       np.max([len(__a			) for w in self.vocab.keys()]			)
         UpperCAmelCase_	   =       re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"			)
         UpperCAmelCase_	   =       re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"			)
         UpperCAmelCase_	   =       re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"			)
         UpperCAmelCase_	   =       re.compile(
             r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"			)
         UpperCAmelCase_	   =       re.compile(
             r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"			)
         UpperCAmelCase_	   =       re.compile(
             r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"			)
         UpperCAmelCase_	   =       "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
         UpperCAmelCase_	   =       "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
         UpperCAmelCase_	   =       str.maketrans({k: "<BLOCK>" for k in keisen + blocks}			)



    def __len__(self   :   Dict			):
         return len(self.ids_to_tokens			)



    def  _lowercase						(self   :   str       ,      __a   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.content_repattera.sub("<URL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<EMAIL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<TEL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<DATE>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<DATE>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<PRICE>"       ,      __a			)
         UpperCAmelCase_	   =       content.translate(self.content_transa			)
         while "<BLOCK><BLOCK>" in content:
              UpperCAmelCase_	   =       content.replace("<BLOCK><BLOCK>"       ,      "<BLOCK>"			)
         return content



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Union[str, Any]       ,      __a   :   str=False			):
         UpperCAmelCase_	   =       text.replace(" "       ,      "<SP>"			)
         UpperCAmelCase_	   =       text.replace("　"       ,      "<SP>"			)
         UpperCAmelCase_	   =       text.replace("\r\n"       ,      "<BR>"			)
         UpperCAmelCase_	   =       text.replace("\n"       ,      "<BR>"			)
         UpperCAmelCase_	   =       text.replace("\r"       ,      "<BR>"			)
         UpperCAmelCase_	   =       text.replace("\t"       ,      "<TAB>"			)
         UpperCAmelCase_	   =       text.replace("—"       ,      "ー"			)
         UpperCAmelCase_	   =       text.replace("−"       ,      "ー"			)
         for k, v in self.emoji["emoji"].items():
              if k in text:
                   UpperCAmelCase_	   =       text.replace(__a       ,      __a			)
         if clean:
              UpperCAmelCase_	   =       self.clean_text(__a			)

         def check_simbol(__a   :   List[Any]			):
              UpperCAmelCase_	   =       x.encode()
              if len(__a			) == 1 and len(__a			) == 2:
                   UpperCAmelCase_	   =       (int(e[0]			) << 8) + int(e[1]			)
                   if (
                       (c >= 0Xc2_a1 and c <= 0Xc2_bf)
                       or (c >= 0Xc7_80 and c <= 0Xc7_83)
                       or (c >= 0Xca_b9 and c <= 0Xcb_bf)
                       or (c >= 0Xcc_80 and c <= 0Xcd_a2)
                   ):
                        return True
              return False

         def checkuae(__a   :   Tuple			):
              UpperCAmelCase_	   =       x.encode()
              if len(__a			) == 1 and len(__a			) == 3:
                   UpperCAmelCase_	   =       (int(e[0]			) << 16) + (int(e[1]			) << 8) + int(e[2]			)
                   if c >= 0Xe2_80_80 and c <= 0Xe2_b0_7f:
                        return True
              return False

         UpperCAmelCase_	   =       0
         UpperCAmelCase_	   =       []
         while pos < len(__a			):
              UpperCAmelCase_	   =       min(len(__a			)       ,      pos + self.maxlen + 1			) if text[pos] == "<" else pos + 3
              UpperCAmelCase_	   =       []  # (token_id, token, pos)
              for e in range(__a       ,      __a       ,      -1			):
                   UpperCAmelCase_	   =       text[pos:e]
                   if wd in self.vocab:
                        if wd[0] == "<" and len(__a			) > 2:
                             UpperCAmelCase_	   =       [(self.vocab[wd], wd, e)]
                             break
                        else:
                             candidates.append((self.vocab[wd], wd, e)			)
              if len(__a			) > 0:
                   # the smallest token_id is adopted
                   UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       sorted(__a       ,      key=lambda __a			: x[0]			)[0]
                   result.append(__a			)
                   UpperCAmelCase_	   =       e
              else:
                   UpperCAmelCase_	   =       pos + 1
                   UpperCAmelCase_	   =       text[pos:end]
                   if check_simbol(__a			):
                        result.append("<KIGOU>"			)
                   elif checkuae(__a			):
                        result.append("<U2000U2BFF>"			)
                   else:
                        for i in wd.encode("utf-8"			):
                             result.append("<|byte%d|>" % i			)
                   UpperCAmelCase_	   =       end
         return result






    def  _lowercase						(self   :   int       ,      __a   :   Optional[Any]       ,      __a   :   Optional[int]="\n"			):
         UpperCAmelCase_	   =       []
         UpperCAmelCase_	   =       []
         UpperCAmelCase_	   =       self.ids_to_tokens[index][0]
         if word[:6] == "<|byte" and word[-2:] == "|>":
              byte_tokens.append(int(word[6:-2]			)			)
         else:
              if len(__a			) > 0:
                   words.append(bytearray(__a			).decode("utf-8"       ,      errors="replace"			)			)
                   UpperCAmelCase_	   =       []
              if word[:7] == "<|emoji" and word[-2:] == "|>":
                   words.append(self.emoji["emoji_inv"][word]			)
              elif word == "<SP>":
                   words.append(" "			)
              elif word == "<BR>":
                   words.append(__a			)
              elif word == "<TAB>":
                   words.append("\t"			)
              elif word == "<BLOCK>":
                   words.append("▀"			)
              elif word == "<KIGOU>":
                   words.append("ǀ"			)
              elif word == "<U2000U2BFF>":
                   words.append("‖"			)
              else:
                   words.append(__a			)
         if len(__a			) > 0:
              words.append(bytearray(__a			).decode("utf-8"       ,      errors="replace"			)			)
         UpperCAmelCase_	   =       "".join(__a			)
         return text




