





"""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:
						UpperCamelCase_	 =None

UpperCamelCase_	 =logging.get_logger(__name__)
UpperCamelCase_	 ={"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_ ,      UpperCamelCase_	 ="""[INST]""", """[/INST]"""
UpperCamelCase_ ,      UpperCamelCase_	 ="""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCamelCase_	 ="""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 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       LlamaTokenizer
				UpperCamelCase								=       '''left'''
				UpperCamelCase								=       ['''input_ids''', '''attention_mask''']






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




										'''simple docstring'''



										super().__init__(
										    vocab_file=lowerCAmelCase__,					tokenizer_file=lowerCAmelCase__,					clean_up_tokenization_spaces=lowerCAmelCase__,					unk_token=lowerCAmelCase__,					bos_token=lowerCAmelCase__,					eos_token=lowerCAmelCase__,					**lowerCAmelCase__,					)
										_UpperCamelCase     :   Dict      =       add_bos_token
										_UpperCamelCase     :   Any      =       add_eos_token
										self.update_post_processor()

										_UpperCamelCase     :   List[Any]      =       vocab_file
										_UpperCamelCase     :   List[Any]      =       False if not self.vocab_file else True






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.bos_token
										_UpperCamelCase     :   List[Any]      =       self.bos_token_id

										_UpperCamelCase     :   str      =       self.eos_token
										_UpperCamelCase     :   Tuple      =       self.eos_token_id

										_UpperCamelCase     :   Optional[Any]      =       f"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
										_UpperCamelCase     :   int      =       f"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

										_UpperCamelCase     :   Union[str, 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)				)
										_UpperCamelCase     :   Any      =       processors.TemplateProcessing(
										    single=lowerCAmelCase__,					pair=lowerCAmelCase__,					special_tokens=lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       int				)      -> Dict:




										'''simple docstring'''



										return self._add_eos_token






				@property
				def 							snake_case      (					self						:       Tuple				)      -> str:




										'''simple docstring'''



										return self._add_bos_token






				@add_eos_token.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       value
										self.update_post_processor()






				@add_bos_token.setter
				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       List[str]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       value
										self.update_post_processor()






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

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

										return (out_vocab_file,)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       "Conversation"				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       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...)'''				)

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










import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Tuple			   =							CONFIG_NAME






						@register_to_config
						def __init__(   self   :   Any		, _A   :   bool = True		, _A   :   int = 8		, _A   :   str = "lanczos"		, _A   :   bool = True		, _A   :   bool = False		, )				->     int:







							"""simple docstring"""


							super().__init__()






						@staticmethod
						def UpperCAmelCase_					(   _A   :   np.ndarray						)				->     PIL.Image.Image:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Optional[int]           =						images[None, ...]
							snake_case_		:      Any           =						(images * 255).round().astype('uint8'						)
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_		:      List[str]           =						[Image.fromarray(image.squeeze()		, mode='L'						) for image in images]
							else:
								snake_case_		:      Any           =						[Image.fromarray(_A						) for image in images]

							return pil_images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Union[List[PIL.Image.Image], PIL.Image.Image]						)				->     np.ndarray:







							"""simple docstring"""


							if not isinstance(_A		, _A						):
								snake_case_		:      Tuple           =						[images]
							snake_case_		:      int           =						[np.array(_A						).astype(np.floataa						) / 2_5_5.0 for image in images]
							snake_case_		:      Dict           =						np.stack(_A		, axis=0						)

							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   np.ndarray						)				->     torch.FloatTensor:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Union[str, Any]           =						images[..., None]

							snake_case_		:      Any           =						torch.from_numpy(images.transpose(0		, 3		, 1		, 2						)						)
							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   torch.FloatTensor						)				->     np.ndarray:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						images.cpu().permute(0		, 2		, 3		, 1						).float().numpy()
							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[int]						)				->     Optional[Any]:







							"""simple docstring"""


							return 2.0 * images - 1.0






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[int]						)				->     Tuple:







							"""simple docstring"""


							return (images / 2 + 0.5).clamp(0		, 1						)






						@staticmethod
						def UpperCAmelCase_					(   _A   :   PIL.Image.Image						)				->     PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						image.convert('RGB'						)
							return image






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   PIL.Image.Image		, _A   :   Optional[int] = None		, _A   :   Optional[int] = None		, )				->     PIL.Image.Image:







							"""simple docstring"""


							if height is None:
								snake_case_		:      Union[str, Any]           =						image.height
							if width is None:
								snake_case_		:      int           =						image.width

							snake_case_    ,snake_case_		:      str           =						(
							    x - x % self.config.vae_scale_factor for x in (width, height)
							)  # resize to integer multiple of vae_scale_factor
							snake_case_		:      Optional[Any]           =						image.resize((width, height)		, resample=PIL_INTERPOLATION[self.config.resample]						)
							return image






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]		, _A   :   Optional[int] = None		, _A   :   Optional[int] = None		, )				->     torch.Tensor:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						(PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(_A		, _A						):
								snake_case_		:      Optional[int]           =						[image]
							elif not (isinstance(_A		, _A						) and all(isinstance(_A		, _A						) for i in image						)):
								raise ValueError(
								    F"""Input is in incorrect format: {[type(_A						) for i in image]}. Currently, we only support {', '.join(_A						)}"""						)

							if isinstance(image[0]		, PIL.Image.Image						):
								if self.config.do_convert_rgb:
									snake_case_		:      str           =						[self.convert_to_rgb(_A						) for i in image]
								if self.config.do_resize:
									snake_case_		:      Union[str, Any]           =						[self.resize(_A		, _A		, _A						) for i in image]
								snake_case_		:      int           =						self.pil_to_numpy(_A						)  # to np
								snake_case_		:      str           =						self.numpy_to_pt(_A						)  # to pt

							elif isinstance(image[0]		, np.ndarray						):
								snake_case_		:      List[Any]           =						np.concatenate(_A		, axis=0						) if image[0].ndim == 4 else np.stack(_A		, axis=0						)
								snake_case_		:      Any           =						self.numpy_to_pt(_A						)
								snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Tuple           =						image.shape
								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
									    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""						)

							elif isinstance(image[0]		, torch.Tensor						):
								snake_case_		:      List[Any]           =						torch.cat(_A		, axis=0						) if image[0].ndim == 4 else torch.stack(_A		, axis=0						)
								snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Any           =						image.shape

								# don't need any preprocess if the image is latents
								if channel == 4:
									return image

								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
									    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""						)

        # expected range [0,1], normalize to [-1,1]
							snake_case_		:      Tuple           =						self.config.do_normalize
							if image.min() < 0:
								warnings.warn(
								    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
								    F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""		, _A		, )
								snake_case_		:      Optional[Any]           =						False

							if do_normalize:
								snake_case_		:      Dict           =						self.normalize(_A						)

							return image






						def UpperCAmelCase_					(   self   :   Any		, _A   :   torch.FloatTensor		, _A   :   str = "pil"		, _A   :   Optional[List[bool]] = None		, )				->     int:







							"""simple docstring"""


							if not isinstance(_A		, torch.Tensor						):
								raise ValueError(
								    F"""Input for postprocessing is in incorrect format: {type(_A						)}. We only support pytorch tensor"""						)
							if output_type not in ["latent", "pt", "np", "pil"]:
								snake_case_		:      List[Any]           =						(
								    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
								    '`pil`, `np`, `pt`, `latent`'
								)
								deprecate('Unsupported output_type'		, '1.0.0'		, _A		, standard_warn=_A						)
								snake_case_		:      Optional[int]           =						'np'

							if output_type == "latent":
								return image

							if do_denormalize is None:
								snake_case_		:      Optional[Any]           =						[self.config.do_normalize] * image.shape[0]

							snake_case_		:      Dict           =						torch.stack(
							    [self.denormalize(image[i]						) if do_denormalize[i] else image[i] for i in range(image.shape[0]						)]						)

							if output_type == "pt":
								return image

							snake_case_		:      Dict           =						self.pt_to_numpy(_A						)

							if output_type == "np":
								return image

							if output_type == "pil":
								return self.numpy_to_pil(_A						)
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Union[str, Any]			   =							CONFIG_NAME






						@register_to_config
						def __init__(   self   :   List[str]		, _A   :   bool = True		, _A   :   int = 8		, _A   :   str = "lanczos"		, _A   :   bool = True		, )				->     Optional[Any]:







							"""simple docstring"""


							super().__init__()






						@staticmethod
						def UpperCAmelCase_					(   _A   :   int						)				->     Dict:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Dict           =						images[None, ...]
							snake_case_		:      Optional[int]           =						(images * 255).round().astype('uint8'						)
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_		:      Union[str, Any]           =						[Image.fromarray(image.squeeze()		, mode='L'						) for image in images]
							else:
								snake_case_		:      Any           =						[Image.fromarray(image[:, :, :3]						) for image in images]

							return pil_images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[Any]						)				->     Optional[int]:







							"""simple docstring"""


							return image[:, :, 1] * 2**8 + image[:, :, 2]






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   Optional[int]						)				->     Any:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      int           =						images[None, ...]
							snake_case_		:      Any           =						images[:, :, :, 3:]
							if images.shape[-1] == 6:
								snake_case_		:      Optional[int]           =						(images_depth * 255).round().astype('uint8'						)
								snake_case_		:      List[str]           =						[
								    Image.fromarray(self.rgblike_to_depthmap(_A						)		, mode='I;16'						) for image_depth in images_depth
								]
							elif images.shape[-1] == 4:
								snake_case_		:      Dict           =						(images_depth * 6_5_5_3_5.0).astype(np.uintaa						)
								snake_case_		:      Union[str, Any]           =						[Image.fromarray(_A		, mode='I;16'						) for image_depth in images_depth]
							else:
								raise Exception('Not supported'						)

							return pil_images






						def UpperCAmelCase_					(   self   :   Any		, _A   :   torch.FloatTensor		, _A   :   str = "pil"		, _A   :   Optional[List[bool]] = None		, )				->     Dict:







							"""simple docstring"""


							if not isinstance(_A		, torch.Tensor						):
								raise ValueError(
								    F"""Input for postprocessing is in incorrect format: {type(_A						)}. We only support pytorch tensor"""						)
							if output_type not in ["latent", "pt", "np", "pil"]:
								snake_case_		:      int           =						(
								    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
								    '`pil`, `np`, `pt`, `latent`'
								)
								deprecate('Unsupported output_type'		, '1.0.0'		, _A		, standard_warn=_A						)
								snake_case_		:      str           =						'np'

							if do_denormalize is None:
								snake_case_		:      Union[str, Any]           =						[self.config.do_normalize] * image.shape[0]

							snake_case_		:      List[Any]           =						torch.stack(
							    [self.denormalize(image[i]						) if do_denormalize[i] else image[i] for i in range(image.shape[0]						)]						)

							snake_case_		:      int           =						self.pt_to_numpy(_A						)

							if output_type == "np":
								if image.shape[-1] == 6:
									snake_case_		:      Tuple           =						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]						) for im in image]		, axis=0						)
								else:
									snake_case_		:      Any           =						image[:, :, :, 3:]
								return image[:, :, :, :3], image_depth

							if output_type == "pil":
								return self.numpy_to_pil(_A						), self.numpy_to_depth(_A						)
							else:
								raise Exception(F"""This type {output_type} is not supported"""						)

