code: 
  
  '''simple docstring'''
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
   import PIL
  
  
  lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  List[str]						      =							["""pixel_values"""]
  
  
  
        def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[int, float] = 1 / 255     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					Optional[int]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(**UpperCamelCase				)
               lowercase__       =				size if size is not None else {'''height''': 346, '''width''': 346}
               lowercase__       =				get_size_dict(UpperCamelCase				)
               lowercase__       =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
               lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)
  
               lowercase__       =				do_resize
               lowercase__       =				size
               lowercase__       =				resample
               lowercase__       =				do_center_crop
               lowercase__       =				crop_size
               lowercase__       =				do_rescale
               lowercase__       =				rescale_factor
               lowercase__       =				rescale_offset
               lowercase__       =				do_normalize
               lowercase__       =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
               lowercase__       =				image_std if image_std is not None else IMAGENET_STANDARD_STD
               lowercase__       =				include_top
  
  
  
        def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Dict     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				get_size_dict(UpperCamelCase				)
               if "height" not in size or "width" not in size:
                      raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
               return resize(
                   UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		resample=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Union[str, Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				get_size_dict(UpperCamelCase				)
               if "height" not in size or "width" not in size:
                      raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
               return center_crop(UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[int, float]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if offset:
                      lowercase__       =				(image - 127.5) * scale
                      if data_format is not None:
                             lowercase__       =				to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				)
                      lowercase__       =				rescaled_image.astype(np.floataa				)
               else:
                      lowercase__       =				rescale(UpperCamelCase     ,		scale=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
               return rescaled_image
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return normalize(UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					ImageInput     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[str, Any]=None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					float = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[str, TensorType]] = None     ,		UpperCamelCase					:					ChannelDimension = ChannelDimension.FIRST     ,		**UpperCamelCase					:					List[Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				do_resize if do_resize is not None else self.do_resize
               lowercase__       =				resample if resample is not None else self.resample
               lowercase__       =				do_center_crop if do_center_crop is not None else self.do_center_crop
               lowercase__       =				do_rescale if do_rescale is not None else self.do_rescale
               lowercase__       =				rescale_factor if rescale_factor is not None else self.rescale_factor
               lowercase__       =				rescale_offset if rescale_offset is not None else self.rescale_offset
               lowercase__       =				do_normalize if do_normalize is not None else self.do_normalize
               lowercase__       =				image_mean if image_mean is not None else self.image_mean
               lowercase__       =				image_std if image_std is not None else self.image_std
               lowercase__       =				include_top if include_top is not None else self.include_top
  
               lowercase__       =				size if size is not None else self.size
               lowercase__       =				get_size_dict(UpperCamelCase				)
               lowercase__       =				crop_size if crop_size is not None else self.crop_size
               lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)
  
               lowercase__       =				make_list_of_images(UpperCamelCase				)
  
               if not valid_images(UpperCamelCase				):
                      raise ValueError(
                          '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                          '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)
  
               if do_resize and size is None or resample is None:
                      raise ValueError('''Size and resample must be specified if do_resize is True.'''				)
  
               if do_center_crop and crop_size is None:
                      raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)
  
               if do_rescale and rescale_factor is None:
                      raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)
  
               if do_normalize and (image_mean is None or image_std is None):
                      raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)
  
               # All transformations expect numpy arrays.
               lowercase__       =				[to_numpy_array(UpperCamelCase				) for image in images]
  
               if do_resize:
                      lowercase__       =				[self.resize(image=UpperCamelCase     ,		size=UpperCamelCase     ,		resample=UpperCamelCase				) for image in images]
  
               if do_center_crop:
                      lowercase__       =				[self.center_crop(image=UpperCamelCase     ,		size=UpperCamelCase				) for image in images]
  
               if do_rescale:
                      lowercase__       =				[self.rescale(image=UpperCamelCase     ,		scale=UpperCamelCase     ,		offset=UpperCamelCase				) for image in images]
  
               if do_normalize:
                      lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase				) for image in images]
  
               if include_top:
                      lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=[0, 0, 0]     ,		std=UpperCamelCase				) for image in images]
  
               lowercase__       =				[to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				) for image in images]
  
               lowercase__       =				{'''pixel_values''': images}
               return BatchFeature(data=UpperCamelCase     ,		tensor_type=UpperCamelCase				)
  
  
  
  
  
style_context: 
  
  
  '''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							:     Optional[int]								=			None
  
  lowerCamelCase							:     List[Any]								=			logging.get_logger(__name__)
  lowerCamelCase							:     Dict								=			{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase		,						lowerCamelCase							:     str								=			'[INST]', '[/INST]'
  lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			'<<SYS>>\n', '\n<</SYS>>\n\n'
  
  # fmt: off
  lowerCamelCase							:     Optional[int]								=			'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_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  List[str]						      =							LlamaTokenizer
        lowerCAmelCase__      :  Tuple						      =							"""left"""
        lowerCAmelCase__      :  int						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Dict="<unk>"     ,		UpperCamelCase					:					int="<s>"     ,		UpperCamelCase					:					Dict="</s>"     ,		UpperCamelCase					:					Any=True     ,		UpperCamelCase					:					Tuple=False     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(
                   vocab_file=UpperCamelCase     ,		tokenizer_file=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               lowercase__       =				add_bos_token
               lowercase__       =				add_eos_token
               self.update_post_processor()
  
               lowercase__       =				vocab_file
               lowercase__       =				False if not self.vocab_file else True
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.bos_token
               lowercase__       =				self.bos_token_id
  
               lowercase__       =				self.eos_token
               lowercase__       =				self.eos_token_id
  
               lowercase__       =				f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
               lowercase__       =				f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"
  
               lowercase__       =				[]
               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__       =				processors.TemplateProcessing(
                   single=UpperCamelCase     ,		pair=UpperCamelCase     ,		special_tokens=UpperCamelCase				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_eos_token
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_bos_token
  
  
  
        @add_eos_token.setter
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        @add_bos_token.setter
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[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(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				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					:					str     ,		UpperCamelCase					:					"Conversation"				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				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__       =				[]
               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]:
                             lowercase__       =				(
                                 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]:
                      lowercase__       =				(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
  
  
  
  
  