
"""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:
					__lowerCAmelCase							:  List[str] 				=		None

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

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

# fmt: off
__lowerCAmelCase							:  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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[str]         =		VOCAB_FILES_NAMES
	snake_case__							:		Optional[Any]         =		LlamaTokenizer
	snake_case__							:		Union[str, Any]         =		'left'
	snake_case__							:		Any         =		['input_ids', 'attention_mask']


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

			'''simple docstring'''






			super().__init__(
			    vocab_file=__magic_name__       ,    tokenizer_file=__magic_name__       ,    clean_up_tokenization_spaces=__magic_name__       ,    unk_token=__magic_name__       ,    bos_token=__magic_name__       ,    eos_token=__magic_name__       ,    **__magic_name__       ,    )
			a__   		=     add_bos_token
			a__   		=     add_eos_token
			self.update_post_processor()

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


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

			'''simple docstring'''






			a__   		=     self.bos_token
			a__   		=     self.bos_token_id

			a__   		=     self.eos_token
			a__   		=     self.eos_token_id

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

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


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

			'''simple docstring'''






			return self._add_eos_token


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

			'''simple docstring'''






			return self._add_bos_token


	@add_eos_token.setter
	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :int			)			->   int:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	@add_bos_token.setter
	def    _UpperCamelCase							(  self :Any       ,    __magic_name__ :str			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


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

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

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

			return (out_vocab_file,)






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

			'''simple docstring'''






			a__   		=     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...)'''			)

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




