



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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


a_								=       logging.get_logger(__name__)

a_								=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_								=       {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

a_								=       {
    """gpt2""": 1_024,
    """gpt2-medium""": 1_024,
    """gpt2-large""": 1_024,
    """gpt2-xl""": 1_024,
    """distilgpt2""": 1_024,
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	VOCAB_FILES_NAMES
 lowerCAmelCase__							       =	PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__							       =	["""input_ids""", """attention_mask"""]
 lowerCAmelCase__							       =	GPTaTokenizer




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



    '''simple docstring'''
    super().__init__(
        __UpperCAmelCase						,					__UpperCAmelCase						,					tokenizer_file=__UpperCAmelCase						,					unk_token=__UpperCAmelCase						,					bos_token=__UpperCAmelCase						,					eos_token=__UpperCAmelCase						,					add_prefix_space=__UpperCAmelCase						,					**__UpperCAmelCase						,					)

    __lowerCamelCase    				=     kwargs.pop('''add_bos_token'''						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
    if pre_tok_state.get('''add_prefix_space'''						,					__UpperCAmelCase					) != add_prefix_space:
       __lowerCamelCase    				=     getattr(__UpperCAmelCase						,					pre_tok_state.pop('''type'''					)					)
       __lowerCamelCase    				=     add_prefix_space
       __lowerCamelCase    				=     pre_tok_class(**__UpperCAmelCase					)

    __lowerCamelCase    				=     add_prefix_space




 def 							lowerCamelCase   (    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     kwargs.get('''is_split_into_words'''						,					__UpperCAmelCase					)
    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(*__UpperCAmelCase						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     kwargs.get('''is_split_into_words'''						,					__UpperCAmelCase					)

    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(*__UpperCAmelCase						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    __lowerCamelCase    				=     self._tokenizer.model.save(__UpperCAmelCase						,					name=__UpperCAmelCase					)
    return tuple(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     []
    for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(__UpperCAmelCase						,					add_special_tokens=__UpperCAmelCase					) + [self.eos_token_id]					)

    if len(__UpperCAmelCase					) > self.model_max_length:
       __lowerCamelCase    				=     input_ids[-self.model_max_length :]
    return input_ids











def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






