
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						A_						       =				None

A_						       =				logging.get_logger(__name__)
A_						       =				{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

A_						       =				'[INST]', '[/INST]'
A_						       =				'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
A_						       =				'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'




class    __lowerCamelCase     (   lowercase_ ):
				a__:			int									=						VOCAB_FILES_NAMES
				a__:			List[str]									=						LlamaTokenizer
				a__:			Tuple									=						"""left"""
				a__:			int									=						["""input_ids""", """attention_mask"""]




				def __init__(							self       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	UpperCAmelCase=False       ,	UpperCAmelCase="<unk>"       ,	UpperCAmelCase="<s>"       ,	UpperCAmelCase="</s>"       ,	UpperCAmelCase=True       ,	UpperCAmelCase=False       ,	**UpperCAmelCase       ,	):
							super().__init__(
							    vocab_file=UpperCAmelCase       ,	tokenizer_file=UpperCAmelCase       ,	clean_up_tokenization_spaces=UpperCAmelCase       ,	unk_token=UpperCAmelCase       ,	bos_token=UpperCAmelCase       ,	eos_token=UpperCAmelCase       ,	**UpperCAmelCase       ,	)
							lowerCamelCase_ 		=     add_bos_token
							lowerCamelCase_ 		=     add_eos_token
							self.update_post_processor()

							lowerCamelCase_ 		=     vocab_file
							lowerCamelCase_ 		=     False if not self.vocab_file else True




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.bos_token
							lowerCamelCase_ 		=     self.bos_token_id

							lowerCamelCase_ 		=     self.eos_token
							lowerCamelCase_ 		=     self.eos_token_id

							lowerCamelCase_ 		=     f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
							lowerCamelCase_ 		=     f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

							lowerCamelCase_ 		=     []
							if self.add_bos_token:
										special_tokens.append((bos, bos_token_id)      )
							if self.add_eos_token:
										special_tokens.append((eos, eos_token_id)      )
							lowerCamelCase_ 		=     processors.TemplateProcessing(
							    single=UpperCAmelCase       ,	pair=UpperCAmelCase       ,	special_tokens=UpperCAmelCase      )




				@property
				def 					UpperCAmelCase__	(							self      ):
							return self._add_eos_token




				@property
				def 					UpperCAmelCase__	(							self      ):
							return self._add_bos_token




				@add_eos_token.setter
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     value
							self.update_post_processor()




				@add_bos_token.setter
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     value
							self.update_post_processor()




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase = None      ):







							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
										    '''tokenizer.'''      )

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

							if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase      ):
										copyfile(self.vocab_file       ,	UpperCAmelCase      )

							return (out_vocab_file,)





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     list(conversation.iter_texts()      )
							if not all([is_user for is_user, msg in dialogue[::2]]      ) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]      ):
										raise ValueError(
										    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''      )

							lowerCamelCase_ 		=     []
							if len(conversation.past_user_inputs      ) > 0:
										if not conversation.past_user_inputs[0].startswith(UpperCAmelCase      ) or E_SYS not in conversation.past_user_inputs[0]:
													lowerCamelCase_ 		=     (
													    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
													)
							elif not dialogue[0][1].startswith(UpperCAmelCase      ) or E_SYS not in dialogue[0][1]:
										lowerCamelCase_ 		=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "       ,	add_special_tokens=UpperCAmelCase      )
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2]       ,	dialogue[1::2]      )
							    ]       ,	[]       ,	)
							if not (dialogue[-1][0]):
										raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"      )
							dialog_tokens += [self.bos_token_id] + self.encode(
							    f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"       ,	add_special_tokens=UpperCAmelCase      )
							return dialog_tokens





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Dict									=						'yoso'




				def __init__(							self       ,	UpperCAmelCase=5_0265       ,	UpperCAmelCase=768       ,	UpperCAmelCase=12       ,	UpperCAmelCase=12       ,	UpperCAmelCase=3072       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=4096       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0_2       ,	UpperCAmelCase=1e-1_2       ,	UpperCAmelCase="absolute"       ,	UpperCAmelCase=True       ,	UpperCAmelCase=9       ,	UpperCAmelCase=64       ,	UpperCAmelCase=None       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0       ,	UpperCAmelCase=2       ,	**UpperCAmelCase       ,	):
							super().__init__(pad_token_id=UpperCAmelCase       ,	bos_token_id=UpperCAmelCase       ,	eos_token_id=UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     vocab_size
							lowerCamelCase_ 		=     max_position_embeddings
							lowerCamelCase_ 		=     hidden_size
							lowerCamelCase_ 		=     num_hidden_layers
							lowerCamelCase_ 		=     num_attention_heads
							lowerCamelCase_ 		=     intermediate_size
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     hidden_dropout_prob
							lowerCamelCase_ 		=     attention_probs_dropout_prob
							lowerCamelCase_ 		=     initializer_range
							lowerCamelCase_ 		=     type_vocab_size
							lowerCamelCase_ 		=     layer_norm_eps
							lowerCamelCase_ 		=     position_embedding_type
							lowerCamelCase_ 		=     use_expectation
							lowerCamelCase_ 		=     hash_code_len
							lowerCamelCase_ 		=     num_hash
							lowerCamelCase_ 		=     conv_window
							lowerCamelCase_ 		=     use_fast_hash
							lowerCamelCase_ 		=     lsh_backward



