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

import regex as re

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


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_         =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

SCREAMING_SNAKE_CASE_         =  {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE_         =  {
    '''microsoft/deberta-base''': 512,
    '''microsoft/deberta-large''': 512,
    '''microsoft/deberta-xlarge''': 512,
    '''microsoft/deberta-base-mnli''': 512,
    '''microsoft/deberta-large-mnli''': 512,
    '''microsoft/deberta-xlarge-mnli''': 512,
}

SCREAMING_SNAKE_CASE_         =  {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}




def       lowercase__  (   )	-> Tuple:






    """simple docstring"""




    UpperCAmelCase       =   (
        list(range(ord('!'	)	,			ord('~'	) + 1	)	) + list(range(ord('¡'	)	,			ord('¬'	) + 1	)	) + list(range(ord('®'	)	,			ord('ÿ'	) + 1	)	)
    )
    UpperCAmelCase       =   bs[:]
    UpperCAmelCase       =   0
    for b in range(2**8	):
        if b not in bs:
            bs.append(lowerCAmelCase	)
            cs.append(2**8 + n	)
            n += 1
    UpperCAmelCase       =   [chr(lowerCAmelCase	) for n in cs]
    return dict(zip(lowerCAmelCase	,			lowerCAmelCase	)	)




def       lowercase__  (   lowerCAmelCase			:   List[str]	)	-> Optional[int]:






    """simple docstring"""




    UpperCAmelCase       =   set()
    UpperCAmelCase       =   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        UpperCAmelCase       =   char
    return pairs







class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Dict							 =		VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE				:							Any							 =		PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE				:							int							 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE				:							Dict							 =		["input_ids", "attention_mask", "token_type_ids"]




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_="replace"				,    lowercase_="[CLS]"				,    lowercase_="[SEP]"				,    lowercase_="[SEP]"				,    lowercase_="[CLS]"				,    lowercase_="[UNK]"				,    lowercase_="[PAD]"				,    lowercase_="[MASK]"				,    lowercase_=False				,    lowercase_=False				,    **lowercase_				,    )  ->   Union[str, Any]:
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else bos_token
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else eos_token
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else sep_token
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else cls_token
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else unk_token
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase       =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_							) if isinstance(lowercase_				,    lowercase_							) else mask_token

          super().__init__(
              errors=lowercase_				,    bos_token=lowercase_				,    eos_token=lowercase_				,    unk_token=lowercase_				,    sep_token=lowercase_				,    cls_token=lowercase_				,    pad_token=lowercase_				,    mask_token=lowercase_				,    add_prefix_space=lowercase_				,    add_bos_token=lowercase_				,    **lowercase_				,    )
          UpperCAmelCase       =   add_bos_token

          with open(lowercase_				,    encoding='utf-8'							) as vocab_handle:
              UpperCAmelCase       =   json.load(lowercase_							)
          UpperCAmelCase       =   {v: k for k, v in self.encoder.items()}
          UpperCAmelCase       =   errors  # how to handle errors in decoding
          UpperCAmelCase       =   bytes_to_unicode()
          UpperCAmelCase       =   {v: k for k, v in self.byte_encoder.items()}
          with open(lowercase_				,    encoding='utf-8'							) as merges_handle:
              UpperCAmelCase       =   merges_handle.read().split('\n'							)[1:-1]
          UpperCAmelCase       =   [tuple(merge.split()							) for merge in bpe_merges]
          UpperCAmelCase       =   dict(zip(lowercase_				,    range(len(lowercase_							)							)							)							)
          UpperCAmelCase       =   {}
          UpperCAmelCase       =   add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          UpperCAmelCase       =   re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'							)




      @property
      # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
      def       a_ (							self							)  ->   int:
          return len(self.encoder							)




      def       a_ (							self							)  ->   Any:
          return dict(self.encoder				,    **self.added_tokens_encoder							)




      def       a_ (							self				,    lowercase_							)  ->   Any:
          if token in self.cache:
              return self.cache[token]
          UpperCAmelCase       =   tuple(lowercase_							)
          UpperCAmelCase       =   get_pairs(lowercase_							)

          if not pairs:
              return token

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

                  if word[i] == first and i < len(lowercase_							) - 1 and word[i + 1] == second:
                      new_word.append(first + second							)
                      i += 2
                  else:
                      new_word.append(word[i]							)
                      i += 1
              UpperCAmelCase       =   tuple(lowercase_							)
              UpperCAmelCase       =   new_word
              if len(lowercase_							) == 1:
                  break
              else:
                  UpperCAmelCase       =   get_pairs(lowercase_							)
          UpperCAmelCase       =   ' '.join(lowercase_							)
          UpperCAmelCase       =   word
          return word




      def       a_ (							self				,    lowercase_				,    lowercase_ = None							)  ->   List[int]:
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase       =   [self.cls_token_id]
          UpperCAmelCase       =   [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep




      def       a_ (							self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = False							)  ->   List[int]:
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=lowercase_				,    token_ids_a=lowercase_				,    already_has_special_tokens=lowercase_							)

          if token_ids_a is None:
              return [1] + ([0] * len(lowercase_							)) + [1]
          return [1] + ([0] * len(lowercase_							)) + [1] + ([0] * len(lowercase_							)) + [1]




      def       a_ (							self				,    lowercase_				,    lowercase_ = None							)  ->   List[int]:
          UpperCAmelCase       =   [self.sep_token_id]
          UpperCAmelCase       =   [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							) * [0] + len(token_ids_a + sep							) * [1]




      def       a_ (							self				,    lowercase_							)  ->   str:
          UpperCAmelCase       =   []
          for token in re.findall(self.pat				,    lowercase_							):
              UpperCAmelCase       =   ''.join(
                  self.byte_encoder[b] for b in token.encode('utf-8'							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_							).split(' '							)							)
          return bpe_tokens




      def       a_ (							self				,    lowercase_							)  ->   Tuple:
          return self.encoder.get(lowercase_				,    self.encoder.get(self.unk_token							)							)




      def       a_ (							self				,    lowercase_							)  ->   Optional[Any]:
          return self.decoder.get(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   ''.join(lowercase_							)
          UpperCAmelCase       =   bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'				,    errors=self.errors							)
          return text




      def       a_ (							self				,    lowercase_				,    lowercase_ = None							)  ->   Tuple[str]:
          if not os.path.isdir(lowercase_							):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
              return
          UpperCAmelCase       =   os.path.join(
              lowercase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
          UpperCAmelCase       =   os.path.join(
              lowercase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

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

          UpperCAmelCase       =   0
          with open(lowercase_				,    '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 lowercase_							: 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       =   token_index
                  writer.write(' '.join(lowercase_							) + '\n'							)
                  index += 1

          return vocab_file, merge_file




      def       a_ (							self				,    lowercase_				,    lowercase_=False				,    **lowercase_							)  ->   int:
          UpperCAmelCase       =   kwargs.pop('add_prefix_space'				,    self.add_prefix_space							)
          if (is_split_into_words or add_prefix_space) and (len(lowercase_							) > 0 and not text[0].isspace()):
              UpperCAmelCase       =   ' ' + text
          return (text, kwargs)




      def       a_ (							self				,    lowercase_							)  ->   List[int]:
          UpperCAmelCase       =   []
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase_				,    add_special_tokens=lowercase_							) + [self.eos_token_id]							)
          if len(lowercase_							) > self.model_max_length:
              UpperCAmelCase       =   input_ids[-self.model_max_length :]
          return input_ids





"""simple docstring"""

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

    # Embeddings
    # Position IDs
    UpperCAmelCase       =   fnet_pretraining_model.state_dict()

    UpperCAmelCase       =   state_dict['fnet.embeddings.position_ids']
    UpperCAmelCase       =   {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']	)

    # Pooler Layers
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

    # Masked LM Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']	)
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_bias']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(lowerCAmelCase	)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--fnet_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained FNet model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



