

import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase				:   Optional[int]					   =							logging.get_logger(__name__)

_lowerCamelCase				:   Optional[Any]					   =							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowerCamelCase				:   List[str]					   =							{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_lowerCamelCase				:   List[Any]					   =							{
    "ctrl": 2_5_6,
}

_lowerCamelCase				:   List[Any]					   =							{
    "Pregnancy": 1_6_8_6_2_9,
    "Christianity": 7_6_7_5,
    "Explain": 1_0_6_4_2_3,
    "Fitness": 6_3_4_4_0,
    "Saving": 6_3_1_6_3,
    "Ask": 2_7_1_7_1,
    "Ass": 9_5_9_8_5,
    "Joke": 1_6_3_5_0_9,
    "Questions": 4_5_6_2_2,
    "Thoughts": 4_9_6_0_5,
    "Retail": 5_2_3_4_2,
    "Feminism": 1_6_4_3_3_8,
    "Writing": 1_1_9_9_2,
    "Atheism": 1_9_2_2_6_3,
    "Netflix": 4_8_6_1_6,
    "Computing": 3_9_6_3_9,
    "Opinion": 4_3_2_1_3,
    "Alone": 4_4_9_6_7,
    "Funny": 5_8_9_1_7,
    "Gaming": 4_0_3_5_8,
    "Human": 4_0_8_8,
    "India": 1_3_3_1,
    "Joker": 7_7_1_3_8,
    "Diet": 3_6_2_0_6,
    "Legal": 1_1_8_5_9,
    "Norman": 4_9_3_9,
    "Tip": 7_2_6_8_9,
    "Weight": 5_2_3_4_3,
    "Movies": 4_6_2_7_3,
    "Running": 2_3_4_2_5,
    "Science": 2_0_9_0,
    "Horror": 3_7_7_9_3,
    "Confession": 6_0_5_7_2,
    "Finance": 1_2_2_5_0,
    "Politics": 1_6_3_6_0,
    "Scary": 1_9_1_9_8_5,
    "Support": 1_2_6_5_4,
    "Technologies": 3_2_5_1_6,
    "Teenage": 6_6_1_6_0,
    "Event": 3_2_7_6_9,
    "Learned": 6_7_4_6_0,
    "Notion": 1_8_2_7_7_0,
    "Wikipedia": 3_7_5_8_3,
    "Books": 6_6_6_5,
    "Extract": 7_6_0_5_0,
    "Confessions": 1_0_2_7_0_1,
    "Conspiracy": 7_5_9_3_2,
    "Links": 6_3_6_7_4,
    "Narcissus": 1_5_0_4_2_5,
    "Relationship": 5_4_7_6_6,
    "Relationships": 1_3_4_7_9_6,
    "Reviews": 4_1_6_7_1,
    "News": 4_2_5_6,
    "Translation": 2_6_8_2_0,
    "multilingual": 1_2_8_4_0_6,
}

def 			a__     (		UpperCAmelCase							:			Any			)  ->						Union[str, Any]:
  UpperCAmelCase       :							Dict  = set()
  UpperCAmelCase       :							Optional[int]  = word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)			)
    UpperCAmelCase       :							Dict  = char

  UpperCAmelCase       :							Tuple  = set(__lowerCamelCase			)
  return pairs






class 					__UpperCAmelCase     (       A_						):
   UpperCamelCase        =     VOCAB_FILES_NAMES
   UpperCamelCase        =     PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase        =     CONTROL_CODES



   def __init__(							self							:     int,		__A							:     str,		__A							:     Tuple,		__A							:     List[str]="<unk>",		**__A							:     List[str]						):
     super().__init__(unk_token=_snake_case,		**_snake_case						)

     with open(_snake_case,		encoding='''utf-8'''						) as vocab_handle:
       UpperCAmelCase       :							Dict  = json.load(_snake_case						)
     UpperCAmelCase       :							str  = {v: k for k, v in self.encoder.items()}
     with open(_snake_case,		encoding='''utf-8'''						) as merges_handle:
       UpperCAmelCase       :							Union[str, Any]  = merges_handle.read().split('''\n'''						)[1:-1]
     UpperCAmelCase       :							List[Any]  = [tuple(merge.split()						) for merge in merges]
     UpperCAmelCase       :							List[str]  = dict(zip(_snake_case,		range(len(_snake_case						)						)						)						)
     UpperCAmelCase       :							int  = {}



   @property
   def        __magic_name__    (							self							:     Optional[Any]						):
     return len(self.encoder						)



   def        __magic_name__    (							self							:     Optional[int]						):
     return dict(self.encoder,		**self.added_tokens_encoder						)



   def        __magic_name__    (							self							:     Any,		__A							:     Union[str, Any]						):



     if token in self.cache:
       return self.cache[token]
     UpperCAmelCase       :							str  = tuple(_snake_case						)
     UpperCAmelCase       :							Dict  = tuple(list(word[:-1]						) + [word[-1] + '''</w>''']						)
     UpperCAmelCase       :							Any  = get_pairs(_snake_case						)

     if not pairs:
       return token

     while True:
       UpperCAmelCase       :							Dict  = min(_snake_case,		key=lambda __A						: self.bpe_ranks.get(_snake_case,		float('''inf'''						)						)						)
       if bigram not in self.bpe_ranks:
         break
       UpperCAmelCase       :							Any  = bigram
       UpperCAmelCase       :							Tuple  = []
       UpperCAmelCase       :							Any  = 0
       while i < len(_snake_case						):
         try:
           UpperCAmelCase       :							Optional[Any]  = word.index(_snake_case,		_snake_case						)
         except ValueError:
           new_word.extend(word[i:]						)
           break
         else:
           new_word.extend(word[i:j]						)
           UpperCAmelCase       :							int  = j

         if word[i] == first and i < len(_snake_case						) - 1 and word[i + 1] == second:
           new_word.append(first + second						)
           i += 2
         else:
           new_word.append(word[i]						)
           i += 1
       UpperCAmelCase       :							Tuple  = tuple(_snake_case						)
       UpperCAmelCase       :							int  = new_word
       if len(_snake_case						) == 1:
         break
       else:
         UpperCAmelCase       :							Optional[Any]  = get_pairs(_snake_case						)
     UpperCAmelCase       :							Union[str, Any]  = '''@@ '''.join(_snake_case						)
     UpperCAmelCase       :							List[Any]  = word[:-4]
     UpperCAmelCase       :							int  = word
     return word



   def        __magic_name__    (							self							:     Optional[Any],		__A							:     Optional[int]						):
     UpperCAmelCase       :							List[str]  = []

     UpperCAmelCase       :							int  = re.findall(R'''\S+\n?''',		_snake_case						)

     for token in words:
       split_tokens.extend(list(self.bpe(_snake_case						).split(''' '''						)						)						)
     return split_tokens



   def        __magic_name__    (							self							:     List[Any],		__A							:     List[Any]						):
     return self.encoder.get(_snake_case,		self.encoder.get(self.unk_token						)						)



   def        __magic_name__    (							self							:     Optional[int],		__A							:     str						):
     return self.decoder.get(_snake_case,		self.unk_token						)



   def        __magic_name__    (							self							:     List[str],		__A							:     str						):
     UpperCAmelCase       :							List[str]  = ''' '''.join(_snake_case						).replace('''@@ ''',		''''''						).strip()
     return out_string





   def        __magic_name__    (							self							:     Dict,		__A							:     str,		__A							:     Optional[str] = None						):



     if not os.path.isdir(_snake_case						):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
       return
     UpperCAmelCase       :							int  = os.path.join(
         _snake_case,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
     UpperCAmelCase       :							Dict  = os.path.join(
         _snake_case,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']						)

     with open(_snake_case,		'''w''',		encoding='''utf-8'''						) as f:
       f.write(json.dumps(self.encoder,		indent=2,		sort_keys=_snake_case,		ensure_ascii=_snake_case						) + '''\n'''						)

     UpperCAmelCase       :							Optional[int]  = 0
     with open(_snake_case,		'''w''',		encoding='''utf-8'''						) as writer:
       writer.write('''#version: 0.2\n'''						)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),		key=lambda __A						: kv[1]						):
         if index != token_index:
           logger.warning(
               F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
               ''' Please check that the tokenizer is not corrupted!'''						)
           UpperCAmelCase       :							List[Any]  = token_index
         writer.write(''' '''.join(_snake_case						) + '''\n'''						)
         index += 1

     return vocab_file, merge_file

   # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
   #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
   #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
   #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
   #     return ''.join(tokens_generated_so_far)










"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__   :    Tuple				=			val

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

		print(model.load_state_dict(__lowerCamelCase	)	)
		model.eval()
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
