'''simple docstring'''




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:
						_lowercase		:					List[str]													=		None

_lowercase		:					Optional[int]													=		logging.get_logger(__name__)
_lowercase		:					int													=		{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowercase		:					Any													=		'''[INST]''', '''[/INST]'''
_lowercase		:					Tuple													=		'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
_lowercase		:					List[str]													=		'''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        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			VOCAB_FILES_NAMES
			UpperCamelCase__							=			LlamaTokenizer
			UpperCamelCase__							=			'left'
			UpperCamelCase__							=			['input_ids', 'attention_mask']







			def __init__(      self							:    Optional[Any]							,     lowercase_							:    Optional[Any]=None							,     lowercase_							:    Tuple=None							,     lowercase_							:    List[Any]=False							,     lowercase_							:    List[str]="<unk>"							,     lowercase_							:    List[str]="<s>"							,     lowercase_							:    List[str]="</s>"							,     lowercase_							:    Any=True							,     lowercase_							:    Union[str, Any]=False							,     **lowercase_							:    Optional[int]							,     ):
										super().__init__(
										    vocab_file=lowercase_							,     tokenizer_file=lowercase_							,     clean_up_tokenization_spaces=lowercase_							,     unk_token=lowercase_							,     bos_token=lowercase_							,     eos_token=lowercase_							,     **lowercase_							,     )
										lowercase_   :     Union[str, Any]									=			add_bos_token
										lowercase_   :     Optional[int]									=			add_eos_token
										self.update_post_processor()

										lowercase_   :     Union[str, Any]									=			vocab_file
										lowercase_   :     Tuple									=			False if not self.vocab_file else True







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										lowercase_   :     List[Any]									=			self.bos_token
										lowercase_   :     str									=			self.bos_token_id

										lowercase_   :     Any									=			self.eos_token
										lowercase_   :     Tuple									=			self.eos_token_id

										lowercase_   :     List[Any]									=			f'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
										lowercase_   :     Dict									=			f'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

										lowercase_   :     Optional[Any]									=			[]
										if self.add_bos_token:
																	special_tokens.append((bos, bos_token_id)       )
										if self.add_eos_token:
																	special_tokens.append((eos, eos_token_id)       )
										lowercase_   :     int									=			processors.TemplateProcessing(
										    single=lowercase_							,     pair=lowercase_							,     special_tokens=lowercase_       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										return self._add_eos_token







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										return self._add_bos_token







			@add_eos_token.setter
			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    int       ):
										lowercase_   :     str									=			value
										self.update_post_processor()







			@add_bos_token.setter
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    str       ):
										lowercase_   :     str									=			value
										self.update_post_processor()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     lowercase_							:    str							,     lowercase_							:    Optional[str] = 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(lowercase_       ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
																	return
										lowercase_   :     Any									=			os.path.join(
										    lowercase_							,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

										return (out_vocab_file,)







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    "Conversation"       ):
										lowercase_   :     Optional[int]									=			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...)"""       )

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







"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def __init__(  self :Tuple       ,    __magic_name__ :str       ,    __magic_name__ :Optional[Any]=7       ,    __magic_name__ :int=3       ,    __magic_name__ :Union[str, Any]=18       ,    __magic_name__ :Union[str, Any]=30       ,    __magic_name__ :str=400       ,    __magic_name__ :str=True       ,    __magic_name__ :Any=None       ,    __magic_name__ :Tuple=True       ,    __magic_name__ :List[str]=None       ,    )			->   Optional[int]:

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   str:

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


	def    _UpperCamelCase							(  self :List[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 20}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 18, '''width''': 18}			)

			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42       ,    crop_size=84			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 42}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 84, '''width''': 84}			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    Image.Image			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )


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

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    numpify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )






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

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    torchify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    torch.Tensor			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )




