



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:
						UpperCamelCase__	:     Any    =			None

UpperCamelCase__	:     List[Any]    =			logging.get_logger(__name__)
UpperCamelCase__	:     List[Any]    =			{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__  ,UpperCamelCase__	:     Any    =			'''[INST]''', '''[/INST]'''
UpperCamelCase__  ,UpperCamelCase__	:     Optional[int]    =			'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
UpperCamelCase__	:     int    =			'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Optional[Any]         =		VOCAB_FILES_NAMES
			_A				:		Optional[Any]         =		LlamaTokenizer
			_A				:		Any         =		"left"
			_A				:		Union[str, Any]         =		["input_ids", "attention_mask"]

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			Optional[Any]=None							,					lowerCAmelCase__					:			Dict=None							,					lowerCAmelCase__					:			Optional[Any]=False							,					lowerCAmelCase__					:			Optional[int]="<unk>"							,					lowerCAmelCase__					:			List[Any]="<s>"							,					lowerCAmelCase__					:			Union[str, Any]="</s>"							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			Optional[int]=False							,					**lowerCAmelCase__					:			Union[str, Any]							,					):





					"""simple docstring"""






					super().__init__(
					    vocab_file=__A							,					tokenizer_file=__A							,					clean_up_tokenization_spaces=__A							,					unk_token=__A							,					bos_token=__A							,					eos_token=__A							,					**__A							,					)
					__SCREAMING_SNAKE_CASE	:		Any   	=		add_bos_token
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		add_eos_token
					self.update_post_processor()

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		vocab_file
					__SCREAMING_SNAKE_CASE	:		Dict   	=		False if not self.vocab_file else True

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.bos_token
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.bos_token_id

					__SCREAMING_SNAKE_CASE	:		str   	=		self.eos_token
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.eos_token_id

					__SCREAMING_SNAKE_CASE	:		Dict   	=		F"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
					__SCREAMING_SNAKE_CASE	:		Any   	=		F"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		[]
					if self.add_bos_token:
							special_tokens.append((bos, bos_token_id)						)
					if self.add_eos_token:
							special_tokens.append((eos, eos_token_id)						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		processors.TemplateProcessing(
					    single=__A							,					pair=__A							,					special_tokens=__A						)

			@property
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					return self._add_eos_token

			@property
			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					return self._add_bos_token

			@add_eos_token.setter
			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		value
					self.update_post_processor()

			@add_bos_token.setter
			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		value
					self.update_post_processor()

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			str = None						):





					"""simple docstring"""






					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(__A						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
							return
					__SCREAMING_SNAKE_CASE	:		int   	=		os.path.join(
					    __A							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

					return (out_vocab_file,)



			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		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...)"""						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[]
					if len(conversation.past_user_inputs						) > 0:
							if not conversation.past_user_inputs[0].startswith(__A						) or E_SYS not in conversation.past_user_inputs[0]:
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
									    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
									)
					elif not dialogue[0][1].startswith(__A						) or E_SYS not in dialogue[0][1]:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(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=__A						)
					        + [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=__A						)
					return dialog_tokens




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass