

import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

snake_case_        =			{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

snake_case_        =			{'''facebook/blenderbot_small-90M''': 512}

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     lowercase__   :       Optional[int]				=	set()
     lowercase__   :       Tuple				=	word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)      )
          lowercase__   :       str				=	char

     lowercase__   :       str				=	set(UpperCamelCase__      )
     return pairs


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Any														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	str														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	str														=					['''input_ids''', '''attention_mask''']





    def __init__(				self						,       a						,       a						,       a="__start__"						,       a="__end__"						,       a="__unk__"						,       a="__null__"						,       **a						,       ):
         super().__init__(unk_token=_a						,       bos_token=_a						,       eos_token=_a						,       pad_token=_a						,       **_a)

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





    @property
    def 				snake_case_    (				self):
         return len(self.encoder)





    def 				snake_case_    (				self):
         return dict(self.encoder						,       **self.added_tokens_encoder)





    def 				snake_case_    (				self						,       a):
         if token in self.cache:
              return self.cache[token]
         lowercase__   :       str				=	re.sub('([.,!?()])'						,       r' \1'						,       _a)
         lowercase__   :       List[Any]				=	re.sub('(\')'						,       r' \1 '						,       _a)
         lowercase__   :       Optional[int]				=	re.sub(r'\s{2,}'						,       ' '						,       _a)
         if "\n" in token:
              lowercase__   :       Optional[Any]				=	token.replace('\n'						,       ' __newln__')

         lowercase__   :       str				=	token.split(' ')
         lowercase__   :       List[str]				=	[]
         for token in tokens:
              if not len(_a):
                   continue

              lowercase__   :       List[Any]				=	token.lower()
              lowercase__   :       Any				=	tuple(_a)
              lowercase__   :       Tuple				=	tuple(list(word[:-1]) + [word[-1] + '</w>'])
              lowercase__   :       List[Any]				=	get_pairs(_a)

              if not pairs:
                   words.append(_a)
                   continue

              while True:
                   lowercase__   :       Optional[int]				=	min(_a						,       key=lambda a: self.bpe_ranks.get(_a						,       float('inf')))
                   if bigram not in self.bpe_ranks:
                        break
                   lowercase__				,			lowercase__   :       List[Any]				=	bigram
                   lowercase__   :       Tuple				=	[]
                   lowercase__   :       Union[str, Any]				=	0

                   while i < len(_a):
                        try:
                             lowercase__   :       List[str]				=	word.index(_a						,       _a)
                             new_word.extend(word[i:j])
                             lowercase__   :       Tuple				=	j
                        except ValueError:
                             new_word.extend(word[i:])
                             break

                        if word[i] == first and i < len(_a) - 1 and word[i + 1] == second:
                             new_word.append(first + second)
                             i += 2
                        else:
                             new_word.append(word[i])
                             i += 1
                   lowercase__   :       Tuple				=	tuple(_a)
                   lowercase__   :       Any				=	new_word
                   if len(_a) == 1:
                        break
                   else:
                        lowercase__   :       List[Any]				=	get_pairs(_a)
              lowercase__   :       List[Any]				=	'@@ '.join(_a)
              lowercase__   :       Tuple				=	word[:-4]

              lowercase__   :       Tuple				=	word
              words.append(_a)
         return " ".join(_a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	[]

         lowercase__   :       int				=	re.findall(r'\S+\n?'						,       _a)

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





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	token.lower()
         return self.encoder.get(_a						,       self.encoder.get(self.unk_token))





    def 				snake_case_    (				self						,       a):
         return self.decoder.get(_a						,       self.unk_token)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Dict				=	' '.join(_a).replace('@@ '						,       '').strip()
         return out_string





    def 				snake_case_    (				self						,       a						,       a = None):
         if not os.path.isdir(_a):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
              return
         lowercase__   :       Optional[Any]				=	os.path.join(
             _a						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
         lowercase__   :       List[Any]				=	os.path.join(
             _a						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

         lowercase__   :       int				=	0
         with open(_a						,       '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!')
                        lowercase__   :       Dict				=	token_index
                   writer.write(' '.join(_a) + '\n')
                   index += 1

         return vocab_file, merge_file




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

