
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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowercase__				:				str						=      logging.get_logger(__name__)


if is_vision_available():
				import PIL






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	['pixel_values']



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = 1 / 255  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> None:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      size if size is not None else {'''height''': 2560, '''width''': 1920}
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(tuple, list)      ):
																			# The previous feature extractor size parameter was in (width, height) format
																			__UpperCamelCase  	=      size[::-1]
													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      resample
													__UpperCamelCase  	=      do_thumbnail
													__UpperCamelCase  	=      do_align_long_axis
													__UpperCamelCase  	=      do_pad
													__UpperCamelCase  	=      do_rescale
													__UpperCamelCase  	=      rescale_factor
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__UpperCamelCase  	=      image_std if image_std is not None else IMAGENET_STANDARD_STD



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']

													if (output_width < output_height and input_width > input_height) or (
													    output_width > output_height and input_width < input_height
													):
																			__UpperCamelCase  	=      np.rotaa(SCREAMING_SNAKE_CASE_  ,						3      )

													if data_format is not None:
																			__UpperCamelCase  	=      to_channel_dimension_format(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													return image



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']
													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      output_width - input_width
													__UpperCamelCase  	=      output_height - input_height

													if random_padding:
																			__UpperCamelCase  	=      np.random.randint(low=0  ,						high=delta_height + 1      )
																			__UpperCamelCase  	=      np.random.randint(low=0  ,						high=delta_width + 1      )
													else:
																			__UpperCamelCase  	=      delta_height // 2
																			__UpperCamelCase  	=      delta_width // 2

													__UpperCamelCase  	=      delta_height - pad_top
													__UpperCamelCase  	=      delta_width - pad_left

													__UpperCamelCase  	=      ((pad_top, pad_bottom), (pad_left, pad_right))
													return pad(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''      )
													return self.pad_image(*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']

													# We always resize to the smallest of either the input or output size.
													__UpperCamelCase  	=      min(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      min(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													if height == input_height and width == input_width:
																			return image

													if input_height > input_width:
																			__UpperCamelCase  	=      int(input_width * height / input_height      )
													elif input_width > input_height:
																			__UpperCamelCase  	=      int(input_height * width / input_width      )

													return resize(
													    SCREAMING_SNAKE_CASE_  ,						size=(height, width)  ,						resample=SCREAMING_SNAKE_CASE_  ,						reducing_gap=2.0  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      min(size['''height''']  ,						size['''width''']      )
													__UpperCamelCase  	=      get_resize_output_image_size(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						default_to_square=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      resize(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						resample=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													return resized_image



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> List[str]:





													'''simple docstring'''






													return rescale(SCREAMING_SNAKE_CASE_  ,						scale=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> np.ndarray:





													'''simple docstring'''






													return normalize(SCREAMING_SNAKE_CASE_  ,						mean=SCREAMING_SNAKE_CASE_  ,						std=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST  ,						**SCREAMING_SNAKE_CASE_  ,						)-> PIL.Image.Image:





													'''simple docstring'''






													__UpperCamelCase  	=      do_resize if do_resize is not None else self.do_resize
													__UpperCamelCase  	=      size if size is not None else self.size
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(tuple, list)      ):
																			# Previous feature extractor had size in (width, height) format
																			__UpperCamelCase  	=      size[::-1]
													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      resample if resample is not None else self.resample
													__UpperCamelCase  	=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
													__UpperCamelCase  	=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
													__UpperCamelCase  	=      do_pad if do_pad is not None else self.do_pad
													__UpperCamelCase  	=      do_rescale if do_rescale is not None else self.do_rescale
													__UpperCamelCase  	=      rescale_factor if rescale_factor is not None else self.rescale_factor
													__UpperCamelCase  	=      do_normalize if do_normalize is not None else self.do_normalize
													__UpperCamelCase  	=      image_mean if image_mean is not None else self.image_mean
													__UpperCamelCase  	=      image_std if image_std is not None else self.image_std

													__UpperCamelCase  	=      make_list_of_images(SCREAMING_SNAKE_CASE_      )

													if not valid_images(SCREAMING_SNAKE_CASE_      ):
																			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:
																			raise ValueError('''Size must be specified if do_resize is True.'''      )

													if do_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

													if do_pad and size is None:
																			raise ValueError('''Size must be specified if do_pad 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.
													__UpperCamelCase  	=      [to_numpy_array(SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_align_long_axis:
																			__UpperCamelCase  	=      [self.align_long_axis(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_resize:
																			__UpperCamelCase  	=      [self.resize(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						resample=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_thumbnail:
																			__UpperCamelCase  	=      [self.thumbnail(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_pad:
																			__UpperCamelCase  	=      [self.pad_image(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						random_padding=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_rescale:
																			__UpperCamelCase  	=      [self.rescale(image=SCREAMING_SNAKE_CASE_  ,						scale=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_normalize:
																			__UpperCamelCase  	=      [self.normalize(image=SCREAMING_SNAKE_CASE_  ,						mean=SCREAMING_SNAKE_CASE_  ,						std=SCREAMING_SNAKE_CASE_      ) for image in images]

													__UpperCamelCase  	=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for image in images]

													__UpperCamelCase  	=      {'''pixel_values''': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE_  ,						tensor_type=SCREAMING_SNAKE_CASE_      )






"""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




