






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

import regex as re

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


__a:     Any   					=					logging.get_logger(__name__)


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

__a:     int   					=					{
    '''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'''
        )
    },
}

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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    set()
       _UpperCAmelCase							=    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCAmelCase							=    char

       _UpperCAmelCase							=    set(__snake_case )
       return pairs





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  ['''input_ids''', '''attention_mask''']


  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[str]="__start__"       ,				lowerCamelCase		:			List[Any]="__end__"       ,				lowerCamelCase		:			Any="__unk__"       ,				lowerCamelCase		:			Optional[Any]="__null__"       ,				**lowerCamelCase		:			Optional[Any]       ,				)						->	Tuple:


         """simple docstring"""
         super().__init__(unk_token=lowerCamelCase       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				pad_token=lowerCamelCase       ,				**lowerCamelCase							)

         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as vocab_handle:
                _UpperCAmelCase							=    json.load(lowerCamelCase							)
         _UpperCAmelCase							=    {v: k for k, v in self.encoder.items()}
         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as merges_handle:
                _UpperCAmelCase							=    merges_handle.read().split("""\n"""							)[1:-1]
         _UpperCAmelCase							=    [tuple(merge.split()							) for merge in merges]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    {}


  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         return len(self.encoder							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Dict:


         """simple docstring"""
         return dict(self.encoder       ,				**self.added_tokens_encoder							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""
         if token in self.cache:
                return self.cache[token]
         _UpperCAmelCase							=    re.sub("""([.,!?()])"""       ,				r""" \1"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub("""(')"""       ,				r""" \1 """       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub(r"""\s{2,}"""       ,				""" """       ,				lowerCamelCase							)
         if "\n" in token:
                _UpperCAmelCase							=    token.replace("""\n"""       ,				""" __newln__"""							)

         _UpperCAmelCase							=    token.split(""" """							)
         _UpperCAmelCase							=    []
         for token in tokens:
                if not len(lowerCamelCase							):
                       continue

                _UpperCAmelCase							=    token.lower()
                _UpperCAmelCase							=    tuple(lowerCamelCase							)
                _UpperCAmelCase							=    tuple(list(word[:-1]							) + [word[-1] + """</w>"""]							)
                _UpperCAmelCase							=    get_pairs(lowerCamelCase							)

                if not pairs:
                       words.append(lowerCamelCase							)
                       continue

                while True:
                       _UpperCAmelCase							=    min(lowerCamelCase       ,				key=lambda lowerCamelCase							: self.bpe_ranks.get(lowerCamelCase       ,				float("""inf"""							)							)							)
                       if bigram not in self.bpe_ranks:
                              break
                       _UpperCAmelCase	,			_UpperCAmelCase							=    bigram
                       _UpperCAmelCase							=    []
                       _UpperCAmelCase							=    0

                       while i < len(lowerCamelCase							):
                              try:
                                     _UpperCAmelCase							=    word.index(lowerCamelCase       ,				lowerCamelCase							)
                                     new_word.extend(word[i:j]							)
                                     _UpperCAmelCase							=    j
                              except ValueError:
                                     new_word.extend(word[i:]							)
                                     break

                              if word[i] == first and i < len(lowerCamelCase							) - 1 and word[i + 1] == second:
                                     new_word.append(first + second							)
                                     i += 2
                              else:
                                     new_word.append(word[i]							)
                                     i += 1
                       _UpperCAmelCase							=    tuple(lowerCamelCase							)
                       _UpperCAmelCase							=    new_word
                       if len(lowerCamelCase							) == 1:
                              break
                       else:
                              _UpperCAmelCase							=    get_pairs(lowerCamelCase							)
                _UpperCAmelCase							=    """@@ """.join(lowerCamelCase							)
                _UpperCAmelCase							=    word[:-4]

                _UpperCAmelCase							=    word
                words.append(lowerCamelCase							)
         return " ".join(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			str							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    []

         _UpperCAmelCase							=    re.findall(r"""\S+\n?"""       ,				lowerCamelCase							)

         for token in words:
                split_tokens.extend(list(self.bpe(lowerCamelCase							).split(""" """							)							)							)
         return split_tokens


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    token.lower()
         return self.encoder.get(lowerCamelCase       ,				self.encoder.get(self.unk_token							)							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int							)						->	str:


         """simple docstring"""
         return self.decoder.get(lowerCamelCase       ,				self.unk_token							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    """ """.join(lowerCamelCase							).replace("""@@ """       ,				""""""							).strip()
         return out_string





  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[str] = None							)						->	Tuple[str]:


         """simple docstring"""
         if not os.path.isdir(lowerCamelCase							):
                logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                return
         _UpperCAmelCase							=    os.path.join(
             lowerCamelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    os.path.join(
             lowerCamelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

         _UpperCAmelCase							=    0
         with open(lowerCamelCase       ,				"""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 lowerCamelCase							: 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(lowerCamelCase							) + """\n"""							)
                       index += 1

         return vocab_file, merge_file



'''simple docstring'''







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


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

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  Optional[Any]											=    {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase     :  List[str]											=    {
    """Salesforce/codegen-350M-mono""": 20_48,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     CodeGenTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            if kwargs.pop('add_bos_token'   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							kwargs.pop('name_or_path'   ,						'' )
                   raise ValueError(
                       'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                       'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                       F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
                       F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
                       'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                       ' so that the fast tokenizer works correctly.' )

            _lowerCAmelCase     :				List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				List[Any]       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				Dict       =							add_prefix_space
                   _lowerCAmelCase     :				List[Any]       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Tuple       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super().decode(
                token_ids=snake_case__   ,						skip_special_tokens=snake_case__   ,						clean_up_tokenization_spaces=snake_case__   ,						**snake_case__   ,						)

            if truncate_before_pattern is not None and len(snake_case__ ) > 0:
                   _lowerCAmelCase     :				Optional[int]       =							self.truncate(snake_case__   ,						snake_case__ )

            return decoded_text







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            def find_re(snake_case__   ,						snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				List[Any]       =							pattern.search(snake_case__   ,						snake_case__ )
                   return m.start() if m else -1

            _lowerCAmelCase     :				Any       =							[re.compile(snake_case__   ,						re.MULTILINE ) for pattern in truncate_before_pattern]

            _lowerCAmelCase     :				Dict       =							list(re.finditer('^print'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[int]       =							completion[: prints[1].start()]

            _lowerCAmelCase     :				Optional[int]       =							list(re.finditer('^def'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[Any]       =							completion[: defs[1].start()]

            _lowerCAmelCase     :				int       =							0

            _lowerCAmelCase     :				Tuple       =							[
                pos for pos in [find_re(snake_case__   ,						snake_case__   ,						snake_case__ ) for terminal in terminals] if pos != -1
            ]

            if len(snake_case__ ) > 0:
                   return completion[: min(snake_case__ )]
            else:
                   return completion

