


'''simple docstring'''





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


UpperCAmelCase__  			=				logging.get_logger(__name__)






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: np.ndarray,_SCREAMING_SNAKE_CASE					: Union[int, Iterable[int]],_SCREAMING_SNAKE_CASE					: bool,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: List[str]=0,_SCREAMING_SNAKE_CASE					: List[str]=None ):
						__A=	round(val / multiple ) * multiple

						if max_val is not None and x > max_val:
									__A=	math.floor(val / multiple ) * multiple

						if x < min_val:
									__A=	math.ceil(val / multiple ) * multiple

						return x

			__A=	(output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ) else output_size

			__A,	__A=	get_image_size(_SCREAMING_SNAKE_CASE )
			__A,	__A=	output_size

			# determine new height and width
			__A=	output_height / input_height
			__A=	output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width ) < abs(1 - scale_height ):
									# fit width
									__A=	scale_width
						else:
									# fit height
									__A=	scale_height

			__A=	constraint_to_multiple_of(scale_height * input_height,multiple=_SCREAMING_SNAKE_CASE )
			__A=	constraint_to_multiple_of(scale_width * input_width,multiple=_SCREAMING_SNAKE_CASE )

			return (new_height, new_width)






class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Optional[Any]					      =			['''pixel_values''']
			def __init__(  self							:			Union[str, Any]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			PILImageResampling = PILImageResampling.BILINEAR			,    lowerCAmelCase_							:			bool = False			,    lowerCAmelCase_							:			int = 1			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = None			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = None			,    **lowerCAmelCase_							:			int			,    )      ->		None:
						super().__init__(**lowerCAmelCase_	)
						__A=	size if size is not None else {'height': 384, 'width': 384}
						__A=	get_size_dict(lowerCAmelCase_	)
						__A=	do_resize
						__A=	size
						__A=	keep_aspect_ratio
						__A=	ensure_multiple_of
						__A=	resample
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__A=	image_std if image_std is not None else IMAGENET_STANDARD_STD
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			np.ndarray			,    lowerCAmelCase_							:			Dict[str, int]			,    lowerCAmelCase_							:			bool = False			,    lowerCAmelCase_							:			int = 1			,    lowerCAmelCase_							:			PILImageResampling = PILImageResampling.BICUBIC			,    lowerCAmelCase_							:			Optional[Union[str, ChannelDimension]] = None			,    **lowerCAmelCase_							:			List[str]			,    )      ->		np.ndarray:
						__A=	get_size_dict(lowerCAmelCase_	)
						if "height" not in size or "width" not in size:
									raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""	)
						__A=	get_resize_output_image_size(
						    lowerCAmelCase_			,    output_size=(size['height'], size['width'])			,    keep_aspect_ratio=lowerCAmelCase_			,    multiple=lowerCAmelCase_			,    )
						return resize(lowerCAmelCase_			,    size=lowerCAmelCase_			,    resample=lowerCAmelCase_			,    data_format=lowerCAmelCase_			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			np.ndarray			,    lowerCAmelCase_							:			Union[int, float]			,    lowerCAmelCase_							:			Optional[Union[str, ChannelDimension]] = None			,    **lowerCAmelCase_							:			Dict			,    )      ->		List[Any]:
						return rescale(lowerCAmelCase_			,    scale=lowerCAmelCase_			,    data_format=lowerCAmelCase_			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			np.ndarray			,    lowerCAmelCase_							:			Union[float, List[float]]			,    lowerCAmelCase_							:			Union[float, List[float]]			,    lowerCAmelCase_							:			Optional[Union[str, ChannelDimension]] = None			,    **lowerCAmelCase_							:			int			,    )      ->		np.ndarray:
						return normalize(lowerCAmelCase_			,    mean=lowerCAmelCase_			,    std=lowerCAmelCase_			,    data_format=lowerCAmelCase_			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			ImageInput			,    lowerCAmelCase_							:			bool = None			,    lowerCAmelCase_							:			int = None			,    lowerCAmelCase_							:			bool = None			,    lowerCAmelCase_							:			int = None			,    lowerCAmelCase_							:			PILImageResampling = None			,    lowerCAmelCase_							:			bool = None			,    lowerCAmelCase_							:			float = None			,    lowerCAmelCase_							:			bool = None			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = None			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = None			,    lowerCAmelCase_							:			Optional[Union[str, TensorType]] = None			,    lowerCAmelCase_							:			ChannelDimension = ChannelDimension.FIRST			,    **lowerCAmelCase_							:			List[Any]			,    )      ->		PIL.Image.Image:
						__A=	do_resize if do_resize is not None else self.do_resize
						__A=	size if size is not None else self.size
						__A=	get_size_dict(lowerCAmelCase_	)
						__A=	keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__A=	ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__A=	resample if resample is not None else self.resample
						__A=	do_rescale if do_rescale is not None else self.do_rescale
						__A=	rescale_factor if rescale_factor is not None else self.rescale_factor
						__A=	do_normalize if do_normalize is not None else self.do_normalize
						__A=	image_mean if image_mean is not None else self.image_mean
						__A=	image_std if image_std is not None else self.image_std

						__A=	make_list_of_images(lowerCAmelCase_	)

						if not valid_images(lowerCAmelCase_	):
									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_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.
						__A=	[to_numpy_array(lowerCAmelCase_	) for image in images]

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

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

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

						__A=	[to_channel_dimension_format(lowerCAmelCase_			,    lowerCAmelCase_	) for image in images]

						__A=	{'pixel_values': images}
						return BatchFeature(data=lowerCAmelCase_			,    tensor_type=lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			List[Tuple] = None	)      ->		List[str]:
						__A=	outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(lowerCAmelCase_	) != len(lowerCAmelCase_	):
												raise ValueError(
												    'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

									if is_torch_tensor(lowerCAmelCase_	):
												__A=	target_sizes.numpy()

									__A=	[]

									for idx in range(len(lowerCAmelCase_	)	):
												__A=	torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0	)			,    size=target_sizes[idx]			,    mode='bilinear'			,    align_corners=lowerCAmelCase_	)
												__A=	resized_logits[0].argmax(dim=0	)
												semantic_segmentation.append(lowerCAmelCase_	)
						else:
									__A=	logits.argmax(dim=1	)
									__A=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

						return semantic_segmentation







'''simple docstring'''





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


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

UpperCAmelCase__  			=				{
    '''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''',
    }
}

UpperCAmelCase__  			=				{
    '''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__					(					a_   ):





			'''simple docstring'''

			A						:				int					      =			VOCAB_FILES_NAMES
			A						:				Optional[int]					      =			PRETRAINED_VOCAB_FILES_MAP
			A						:				Tuple					      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				int					      =			['''input_ids''', '''attention_mask''']
			def __init__(  self							:			int			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Union[str, Any]=False			,    lowerCAmelCase_							:			List[str]=False			,    lowerCAmelCase_							:			List[Any]=False			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			List[Any]=None			,    lowerCAmelCase_							:			Optional[Dict[str, Any]] = None			,    **lowerCAmelCase_							:			Optional[Any]			,    )      ->		None:
						__A=	{} if sp_model_kwargs is None else sp_model_kwargs

						__A=	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'	)
									__A=	'None'

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

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

						__A=	do_lower_case
						__A=	remove_space
						__A=	keep_accents
						__A=	vocab_file

						__A=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(lowerCAmelCase_	)

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

						# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
						__A=	re.compile(
						    F"""[{"".join(map(lowerCAmelCase_			,    list(range(0			,    9	)	) + list(range(11			,    32	)	) + list(range(127			,    160	)	) + [160, 173, 8_203]	)	)}]"""	)
			def __getstate__(  self							:			Optional[int]	)      ->		Tuple:
						__A=	self.__dict__.copy()
						__A=	None
						return state
			def __setstate__(  self							:			int			,    lowerCAmelCase_							:			int	)      ->		Tuple:
						__A=	d

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

						__A=	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 					lowerCAmelCase     (  self							:			Tuple	)      ->		int:
						return len(self.sp_model	)
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			str	)      ->		str:
						__A=	self.non_printing_characters_re.sub(''			,    lowerCAmelCase_	)

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

						# NFC Unicode normalization
						__A=	unicodedata.normalize('NFC'			,    lowerCAmelCase_	)
						return text
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			str			,    **lowerCAmelCase_							:			Optional[Any]	)      ->		List[str]:
						__A=	self.preprocess_text(lowerCAmelCase_	)
						return self.sp_model.encode(lowerCAmelCase_			,    out_type=lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			str	)      ->		int:
						return self.sp_model.PieceToId(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			int	)      ->		str:
						return self.sp_model.IdToPiece(lowerCAmelCase_	)
			@staticmethod
			def 					lowerCAmelCase     (  lowerCAmelCase_							:			str	)      ->		str:
						return out_string
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			List[str]	)      ->		str:
						__A=	[]
						__A=	''
						__A=	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(lowerCAmelCase_	) + token
												__A=	True
												__A=	[]
									else:
												current_sub_tokens.append(lowerCAmelCase_	)
												__A=	False
						out_string += self.sp_model.decode(lowerCAmelCase_	)

						return out_string
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Dict[str, int]:
						__A=	{self.convert_ids_to_tokens(lowerCAmelCase_	): i for i in range(self.vocab_size	)}
						vocab.update(self.added_tokens_encoder	)
						return vocab
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[str] = None	)      ->		Tuple[str]:
						if not os.path.isdir(lowerCAmelCase_	):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
									return
						__A=	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_	) and os.path.isfile(self.vocab_file	):
									copyfile(self.vocab_file			,    lowerCAmelCase_	)
						elif not os.path.isfile(self.vocab_file	):
									with open(lowerCAmelCase_			,    'wb'	) as fi:
												__A=	self.sp_model.serialized_model_proto()
												fi.write(lowerCAmelCase_	)

						return (out_vocab_file,)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, List[str]]			,    lowerCAmelCase_							:			Union[str, bool] = False	)      ->		Union[List[int], List[List[int]], "torch.Tensor"]:

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

						if return_tensors is True or return_tensors == "pt":
									__A=	torch.tensor(lowerCAmelCase_	)

						return token_ids
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Union[int, List[int]]	)      ->		str:
						return self.sp_model.decode(lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			"Conversation"	)      ->		List[int]:
						__A=	[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
						__A=	(
						    F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowerCAmelCase_	) + F"""{self.bos_token}Bot:"""
						)
						return self.encode(text=lowerCAmelCase_	)



