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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


_lowerCamelCase					:   Tuple							=		logging.get_logger(__name__)

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

_lowerCamelCase					:   Optional[Any]							=		{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_lowerCamelCase					:   int							=		{
    '''gpt-neox-20b''': 20_48,
}


class 						lowercase    (     a     ):
							lowercase__						:       str								=							VOCAB_FILES_NAMES
							lowercase__						:       List[str]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       Dict								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__						:       Any								=							["""input_ids""", """attention_mask"""]




							def __init__(						self   :					List[str]	,							_UpperCamelCase   :					Dict=None	,							_UpperCamelCase   :					List[str]=None	,							_UpperCamelCase   :					Union[str, Any]=None	,							_UpperCamelCase   :					List[str]="<|endoftext|>"	,							_UpperCamelCase   :					Tuple="<|endoftext|>"	,							_UpperCamelCase   :					Union[str, Any]="<|endoftext|>"	,							_UpperCamelCase   :					Union[str, Any]=False	,							**_UpperCamelCase   :					Any	,							)					->				List[Any]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get("add_prefix_space"	,							_UpperCamelCase      ) != add_prefix_space:
																	SCREAMING_SNAKE_CASE    			=       getattr(_UpperCamelCase	,							pre_tok_state.pop("type"      )      )
																	SCREAMING_SNAKE_CASE    			=       add_prefix_space
																	SCREAMING_SNAKE_CASE    			=       pre_tok_class(**_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       add_prefix_space




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self._tokenizer.model.save(_UpperCamelCase	,							name=_UpperCamelCase      )
												return tuple(_UpperCamelCase      )





							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					"Conversation"      )					->				List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       []
												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:
																	SCREAMING_SNAKE_CASE    			=       input_ids[-self.model_max_length :]
												return input_ids





def 	__lowerCamelCase							(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"  )

					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       numbers[0]

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

										# update the maximum product found till now
										SCREAMING_SNAKE_CASE    			=       max(UpperCAmelCase__      , UpperCAmelCase__  )

					return max_prod




