



"""simple docstring"""


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

import sentencepiece as spm

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


snake_case_								=			logging.get_logger(__name__)
snake_case_								=			{"""vocab_file""": """spiece.model"""}

snake_case_								=			{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

snake_case_								=			{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

snake_case_								=			"""▁"""







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """token_type_ids"""]






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:int=False  ,    lowercase_					:Optional[Any]=True  ,    lowercase_					:int=True  ,    lowercase_					:List[Any]="<unk>"  ,    lowercase_					:Optional[Any]="[SEP]"  ,    lowercase_					:Optional[Any]="<pad>"  ,    lowercase_					:Union[str, Any]="[CLS]"  ,    lowercase_					:str="[MASK]"  ,    lowercase_					:Optional[Dict[str, Any]] = None  ,    **lowercase_					:Dict  ,    )							->     None:
												# Mask token behave like a normal word, i.e. include the space before it and
												# is included in the raw text, there should be a match in a non-normalized sentence.
												UpperCAmelCase			 =					(
												    AddedToken(lowercase_  ,    lstrip=lowercase_  ,    rstrip=lowercase_  ,    normalized=lowercase_   )
												    if isinstance(lowercase_  ,    lowercase_   )
												    else mask_token
												)

												UpperCAmelCase			 =					{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=lowercase_  ,    remove_space=lowercase_  ,    keep_accents=lowercase_  ,    unk_token=lowercase_  ,    sep_token=lowercase_  ,    pad_token=lowercase_  ,    cls_token=lowercase_  ,    mask_token=lowercase_  ,    sp_model_kwargs=self.sp_model_kwargs  ,    **lowercase_  ,    )

												UpperCAmelCase			 =					do_lower_case
												UpperCAmelCase			 =					remove_space
												UpperCAmelCase			 =					keep_accents
												UpperCAmelCase			 =					vocab_file

												UpperCAmelCase			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(lowercase_   )






						@property
						def       UpperCAmelCase__	(   self					:Any   )							->     str:
												return len(self.sp_model   )






						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												UpperCAmelCase			 =					{self.convert_ids_to_tokens(lowercase_   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab






						def __getstate__(   self					:int   )							->     str:
												UpperCAmelCase			 =					self.__dict__.copy()
												UpperCAmelCase			 =					None
												return state






						def __setstate__(   self					:str  ,    lowercase_					:str   )							->     Any:
												UpperCAmelCase			 =					d

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

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






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Tuple   )							->     Optional[int]:
												if self.remove_space:
																		UpperCAmelCase			 =					' '.join(inputs.strip().split()   )
												else:
																		UpperCAmelCase			 =					inputs
												UpperCAmelCase			 =					outputs.replace('``'  ,    '"'   ).replace('\'\''  ,    '"'   )

												if not self.keep_accents:
																		UpperCAmelCase			 =					unicodedata.normalize('NFKD'  ,    lowercase_   )
																		UpperCAmelCase			 =					''.join([c for c in outputs if not unicodedata.combining(lowercase_   )]   )
												if self.do_lower_case:
																		UpperCAmelCase			 =					outputs.lower()

												return outputs






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:str   )							->     List[str]:
												UpperCAmelCase			 =					self.preprocess_text(lowercase_   )
												UpperCAmelCase			 =					self.sp_model.encode(lowercase_  ,    out_type=lowercase_   )
												UpperCAmelCase			 =					[]
												for piece in pieces:
																		if len(lowercase_   ) > 1 and piece[-1] == str(','   ) and piece[-2].isdigit():
																								UpperCAmelCase			 =					self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_  ,    ''   )   )
																								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																														if len(cur_pieces[0]   ) == 1:
																																				UpperCAmelCase			 =					cur_pieces[1:]
																														else:
																																				UpperCAmelCase			 =					cur_pieces[0][1:]
																								cur_pieces.append(piece[-1]   )
																								new_pieces.extend(lowercase_   )
																		else:
																								new_pieces.append(lowercase_   )

												return new_pieces






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:str   )							->     Union[str, Any]:
												return self.sp_model.PieceToId(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Optional[Any]   )							->     Optional[int]:
												return self.sp_model.IdToPiece(lowercase_   )






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Dict   )							->     Union[str, Any]:
												UpperCAmelCase			 =					[]
												UpperCAmelCase			 =					''
												UpperCAmelCase			 =					False
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								if not prev_is_special:
																														out_string += " "
																								out_string += self.sp_model.decode(lowercase_   ) + token
																								UpperCAmelCase			 =					True
																								UpperCAmelCase			 =					[]
																		else:
																								current_sub_tokens.append(lowercase_   )
																								UpperCAmelCase			 =					False
												out_string += self.sp_model.decode(lowercase_   )
												return out_string.strip()






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:List[int]  ,    lowercase_					:bool = False  ,    lowercase_					:bool = None  ,    lowercase_					:bool = True  ,    **lowercase_					:Any  ,    )							->     str:
												UpperCAmelCase			 =					kwargs.pop('use_source_tokenizer'  ,    lowercase_   )

												UpperCAmelCase			 =					self.convert_ids_to_tokens(lowercase_  ,    skip_special_tokens=lowercase_   )

												# To avoid mixing byte-level and unicode for byte-level BPT
												# we need to build string separately for added tokens and byte-level tokens
												# cf. https://github.com/huggingface/transformers/issues/1133
												UpperCAmelCase			 =					[]
												UpperCAmelCase			 =					[]
												for token in filtered_tokens:
																		if skip_special_tokens and token in self.all_special_ids:
																								continue
																		if token in self.added_tokens_encoder:
																								if current_sub_text:
																														sub_texts.append(self.convert_tokens_to_string(lowercase_   )   )
																														UpperCAmelCase			 =					[]
																								sub_texts.append(lowercase_   )
																		else:
																								current_sub_text.append(lowercase_   )
												if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(lowercase_   )   )

												# Mimic the behavior of the Rust tokenizer:
												# No space after <unk>
												if spaces_between_special_tokens:
																		UpperCAmelCase			 =					re.sub(R'(<unk>) '  ,    R'\1'  ,    ' '.join(lowercase_   )   )
												else:
																		UpperCAmelCase			 =					''.join(lowercase_   )

												UpperCAmelCase			 =					(
												    clean_up_tokenization_spaces
												    if clean_up_tokenization_spaces is not None
												    else self.clean_up_tokenization_spaces
												)
												if clean_up_tokenization_spaces:
																		UpperCAmelCase			 =					self.clean_up_tokenization(lowercase_   )
																		return clean_text
												else:
																		return text






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.sep_token_id]
												UpperCAmelCase			 =					[self.cls_token_id]
												if token_ids_a is None:
																		return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None  ,    lowercase_					:bool = False   )							->     List[int]:

												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=lowercase_  ,    token_ids_a=lowercase_  ,    already_has_special_tokens=lowercase_   )

												if token_ids_a is not None:
																		return [1] + ([0] * len(lowercase_   )) + [1] + ([0] * len(lowercase_   )) + [1]
												return [1] + ([0] * len(lowercase_   )) + [1]






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.sep_token_id]
												UpperCAmelCase			 =					[self.cls_token_id]

												if token_ids_a is None:
																		return len(cls + token_ids_a + sep   ) * [0]
												return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												if not os.path.isdir(lowercase_   ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
																		return
												UpperCAmelCase			 =					os.path.join(
												    lowercase_  ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

												if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase_   ) and os.path.isfile(self.vocab_file   ):
																		copyfile(self.vocab_file  ,    lowercase_   )
												elif not os.path.isfile(self.vocab_file   ):
																		with open(lowercase_  ,    'wb'   ) as fi:
																								UpperCAmelCase			 =					self.sp_model.serialized_model_proto()
																								fi.write(lowercase_   )

												return (out_vocab_file,)










"""simple docstring"""


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  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    )							->     Union[str, Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     PIL.Image.Image:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[List[PIL.Image.Image], PIL.Image.Image]   )							->     np.ndarray:
												if not isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[images]
												UpperCAmelCase			 =					[np.array(lowercase_   ).astype(np.floataa   ) / 255.0 for image in images]
												UpperCAmelCase			 =					np.stack(lowercase_  ,    axis=0   )

												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     torch.FloatTensor:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[..., None]

												UpperCAmelCase			 =					torch.from_numpy(images.transpose(0  ,    3  ,    1  ,    2   )   )
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:torch.FloatTensor   )							->     np.ndarray:
												UpperCAmelCase			 =					images.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:List[Any]   )							->     Optional[int]:
												return 2.0 * images - 1.0






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str   )							->     str:
												return (images / 2 + 0.5).clamp(0  ,    1   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:PIL.Image.Image   )							->     PIL.Image.Image:
												UpperCAmelCase			 =					image.convert('RGB'   )
												return image






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:PIL.Image.Image  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     PIL.Image.Image:
												if height is None:
																		UpperCAmelCase			 =					image.height
												if width is None:
																		UpperCAmelCase			 =					image.width

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






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     torch.Tensor:
												UpperCAmelCase			 =					(PIL.Image.Image, np.ndarray, torch.Tensor)
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[image]
												elif not (isinstance(lowercase_  ,    lowercase_   ) and all(isinstance(lowercase_  ,    lowercase_   ) for i in image   )):
																		raise ValueError(
																		    f"""Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {', '.join(lowercase_   )}"""   )

												if isinstance(image[0]  ,    PIL.Image.Image   ):
																		if self.config.do_convert_rgb:
																								UpperCAmelCase			 =					[self.convert_to_rgb(lowercase_   ) for i in image]
																		if self.config.do_resize:
																								UpperCAmelCase			 =					[self.resize(lowercase_  ,    lowercase_  ,    lowercase_   ) for i in image]
																		UpperCAmelCase			 =					self.pil_to_numpy(lowercase_   )  # to np
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )  # to pt

												elif isinstance(image[0]  ,    np.ndarray   ):
																		UpperCAmelCase			 =					np.concatenate(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					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   ):
																		UpperCAmelCase			 =					torch.cat(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					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]
												UpperCAmelCase			 =					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()}]"""  ,    lowercase_  ,    )
																		UpperCAmelCase			 =					False

												if do_normalize:
																		UpperCAmelCase			 =					self.normalize(lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     List[str]:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    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'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if output_type == "latent":
																		return image

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

												UpperCAmelCase			 =					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

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		return image

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Union[str, Any]  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Optional[int]   )							->     Optional[Any]:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(image[:, :, :3]   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Any   )							->     Union[str, Any]:
												return image[:, :, 1] * 2**8 + image[:, :, 2]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int   )							->     Tuple:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					images[:, :, :, 3:]
												if images.shape[-1] == 6:
																		UpperCAmelCase			 =					(images_depth * 2_55).round().astype('uint8'   )
																		UpperCAmelCase			 =					[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )  ,    mode='I;16'   ) for image_depth in images_depth
																		]
												elif images.shape[-1] == 4:
																		UpperCAmelCase			 =					(images_depth * 6_5535.0).astype(np.uintaa   )
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_  ,    mode='I;16'   ) for image_depth in images_depth]
												else:
																		raise Exception('Not supported'   )

												return pil_images






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     Dict:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    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'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

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

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

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

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

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





