
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
		import torch


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowercase        =  logging.get_logger(__name__)
lowercase        =  {'''vocab_file''': '''spiece.model'''}

lowercase        =  {
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

lowercase        =  {
    '''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Tuple=False   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     int=None   ,					__UpperCamelCase					:     Tuple=None   ,					__UpperCamelCase					:     Optional[Dict[str, Any]] = None   ,					**__UpperCamelCase					:     Dict   ,					):
												lowerCamelCase_							  =					{} if sp_model_kwargs is None else sp_model_kwargs

												lowerCamelCase_							  =					kwargs.get("""name_or_path"""   )
												if name_or_path is None:
																			logger.warning(
																			    """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
																			    """ you are testing the model, this can safely be ignored"""   )
																			lowerCamelCase_							  =					"""None"""

												# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
												lowerCamelCase_							  =					"""<|endoftext|>""" if eos_token is None else eos_token
												lowerCamelCase_							  =					"""<unk>""" if unk_token is None else unk_token
												if "gpt-sw3-7b" in name_or_path:
																			lowerCamelCase_							  =					unk_token if pad_token is None else pad_token
																			lowerCamelCase_							  =					eos_token if bos_token is None else bos_token
												else:
																			lowerCamelCase_							  =					"""<pad>""" if pad_token is None else pad_token
																			lowerCamelCase_							  =					"""<s>""" if bos_token is None else bos_token

												super().__init__(
												    do_lower_case=__UpperCamelCase   ,					remove_space=__UpperCamelCase   ,					keep_accents=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					do_lower_case
												lowerCamelCase_							  =					remove_space
												lowerCamelCase_							  =					keep_accents
												lowerCamelCase_							  =					vocab_file

												lowerCamelCase_							  =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(__UpperCamelCase   )

												# Used for whitespace normalization in input texts
												# fmt : off
												lowerCamelCase_							  =					{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
												# fmt : on

												# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
												lowerCamelCase_							  =					re.compile(
												    F'''[{''.join(map(__UpperCamelCase   ,					list(range(0   ,					9   )   ) + list(range(1_1   ,					3_2   )   ) + list(range(1_2_7   ,					1_6_0   )   ) + [1_6_0, 1_7_3, 8_2_0_3]   )   )}]'''   )

					def __getstate__(	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.__dict__.copy()
												lowerCamelCase_							  =					None
												return state

					def __setstate__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					d

												# for backward compatibility
												if not hasattr(self   ,					"""sp_model_kwargs"""   ):
																			lowerCamelCase_							  =					{}

												lowerCamelCase_							  =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(self.vocab_file   )

					@property
					# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return len(self.sp_model   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.non_printing_characters_re.sub(""""""   ,					__UpperCamelCase   )

												# Normalize whitespaces
												lowerCamelCase_							  =					"""""".join([char if char not in self.whitespaces else """ """ for char in text]   )

												# NFC Unicode normalization
												lowerCamelCase_							  =					unicodedata.normalize("""NFC"""   ,					__UpperCamelCase   )
												return text

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.preprocess_text(__UpperCamelCase   )
												return self.sp_model.encode(__UpperCamelCase   ,					out_type=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ):
												return self.sp_model.PieceToId(__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     int   ):
												return self.sp_model.IdToPiece(__UpperCamelCase   )

					@staticmethod
					def    lowercase__      (	__UpperCamelCase					:     str   ):
												return out_string

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					""""""
												lowerCamelCase_							  =					False
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																										if not prev_is_special:
																																	out_string += " "

																										out_string += self.sp_model.decode(__UpperCamelCase   ) + token
																										lowerCamelCase_							  =					True
																										lowerCamelCase_							  =					[]
																			else:
																										current_sub_tokens.append(__UpperCamelCase   )
																										lowerCamelCase_							  =					False
												out_string += self.sp_model.decode(__UpperCamelCase   )

												return out_string

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					{self.convert_ids_to_tokens(__UpperCamelCase   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if not os.path.isdir(__UpperCamelCase   ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
																			return
												lowerCamelCase_							  =					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   ) and os.path.isfile(self.vocab_file   ):
																			copyfile(self.vocab_file   ,					__UpperCamelCase   )
												elif not os.path.isfile(self.vocab_file   ):
																			with open(__UpperCamelCase   ,					"""wb"""   ) as fi:
																										lowerCamelCase_							  =					self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase   )

												return (out_vocab_file,)

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, List[str]]   ,					__UpperCamelCase					:     Union[str, bool] = False   ):

												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					self.preprocess_text(__UpperCamelCase   )
																			lowerCamelCase_							  =					self.sp_model.encode(__UpperCamelCase   )
												else:
																			lowerCamelCase_							  =					[self.preprocess_text(__UpperCamelCase   ) for t in text]
																			lowerCamelCase_							  =					self.sp_model.encode(__UpperCamelCase   )

												if return_tensors is True or return_tensors == "pt":
																			lowerCamelCase_							  =					torch.tensor(__UpperCamelCase   )

												return token_ids

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[int, List[int]]   ):
												return self.sp_model.decode(__UpperCamelCase   )



					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     "Conversation"   ):
												lowerCamelCase_							  =					[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
												lowerCamelCase_							  =					(
												    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(__UpperCamelCase   ) + F'''{self.bos_token}Bot:'''
												)
												return self.encode(text=__UpperCamelCase   )







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,
    get_resize_output_image_size,
    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


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					do_resize
												lowerCamelCase_							  =					size
												# Default value set here for backwards compatibility where the value in config is None
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												lowerCamelCase_							  =					resample
												lowerCamelCase_							  =					do_rescale
												lowerCamelCase_							  =					rescale_factor
												lowerCamelCase_							  =					do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCamelCase_							  =					image_std if image_std is not None else IMAGENET_STANDARD_STD

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												if "shortest_edge" not in size:
																			raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
												lowerCamelCase_							  =					size["""shortest_edge"""]

												if shortest_edge < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[int, float]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     int   ,					):
												return rescale(__UpperCamelCase   ,					scale=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     List[str]   ,					):
												return normalize(__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     ChannelDimension = ChannelDimension.FIRST   ,					**__UpperCamelCase					:     List[str]   ,					):
												lowerCamelCase_							  =					do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else self.crop_pct
												lowerCamelCase_							  =					resample if resample is not None else self.resample
												lowerCamelCase_							  =					do_rescale if do_rescale is not None else self.do_rescale
												lowerCamelCase_							  =					rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCamelCase_							  =					do_normalize if do_normalize is not None else self.do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else self.image_mean
												lowerCamelCase_							  =					image_std if image_std is not None else self.image_std

												lowerCamelCase_							  =					size if size is not None else self.size
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""   )

												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.
												lowerCamelCase_							  =					[to_numpy_array(__UpperCamelCase   ) for image in images]

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

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

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

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

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





