






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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
       import PIL

if is_torch_available():
       import torch


__lowerCamelCase	:		Union[str, Any]										=   logging.get_logger(__name__)







class  _lowercase   (   snake_case__						):
      _a							:						Any										=							["""pixel_values"""]
      def __init__(     self				,			a = True				,			a = None				,			a = PILImageResampling.BILINEAR				,			a = True				,			a = 1 / 2_5_5				,			a = True				,			a = None				,			a = True				,			**a				,			):
       super().__init__(**UpperCAmelCase_   )
       snake_case__  :    int							       =size if size is not None else {"""shortest_edge""": 2_2_4}
       snake_case__  :    Dict							       =get_size_dict(UpperCAmelCase_				,			default_to_square=UpperCAmelCase_   )
       snake_case__  :    Optional[Any]							       =crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
       snake_case__  :    str							       =get_size_dict(UpperCAmelCase_				,			param_name="""crop_size"""   )

       snake_case__  :    str							       =do_resize
       snake_case__  :    Optional[Any]							       =size
       snake_case__  :    Union[str, Any]							       =resample
       snake_case__  :    int							       =do_rescale
       snake_case__  :    str							       =rescale_factor
       snake_case__  :    Union[str, Any]							       =do_center_crop
       snake_case__  :    Union[str, Any]							       =crop_size
       snake_case__  :    Dict							       =do_flip_channel_order
      def 						lowercase__					(     self				,			a				,			a				,			a = PIL.Image.BILINEAR				,			a = None				,			**a				,			):
       snake_case__  :    List[Any]							       =get_size_dict(UpperCAmelCase_				,			default_to_square=UpperCAmelCase_   )
       if "shortest_edge" not in size:
        raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"   )
       snake_case__  :    List[str]							       =get_resize_output_image_size(UpperCAmelCase_				,			size=size["""shortest_edge"""]				,			default_to_square=UpperCAmelCase_   )
       return resize(UpperCAmelCase_				,			size=UpperCAmelCase_				,			resample=UpperCAmelCase_				,			data_format=UpperCAmelCase_				,			**UpperCAmelCase_   )
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a				,			):
       snake_case__  :    Any							       =get_size_dict(UpperCAmelCase_   )
       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()}"   )
       return center_crop(UpperCAmelCase_				,			size=(size["""height"""], size["""width"""])				,			data_format=UpperCAmelCase_				,			**UpperCAmelCase_   )
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a				,			):
       return rescale(UpperCAmelCase_				,			scale=UpperCAmelCase_				,			data_format=UpperCAmelCase_				,			**UpperCAmelCase_   )
      def 						lowercase__					(     self				,			a				,			a = None   ):
       return flip_channel_order(UpperCAmelCase_				,			data_format=UpperCAmelCase_   )
      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Any							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    Tuple							       =resample if resample is not None else self.resample
       snake_case__  :    List[Any]							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case__  :    Union[str, Any]							       =do_center_crop if do_center_crop is not None else self.do_center_crop
       snake_case__  :    Any							       =(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       snake_case__  :    Dict							       =size if size is not None else self.size
       snake_case__  :    Any							       =get_size_dict(UpperCAmelCase_				,			default_to_square=UpperCAmelCase_   )
       snake_case__  :    int							       =crop_size if crop_size is not None else self.crop_size
       snake_case__  :    List[str]							       =get_size_dict(UpperCAmelCase_				,			param_name="""crop_size"""   )

       snake_case__  :    Any							       =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:
        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_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True."""   )

       # All transformations expect numpy arrays.
       snake_case__  :    Tuple							       =[to_numpy_array(UpperCAmelCase_   ) for image in images]

       if do_resize:
        snake_case__  :    Tuple							       =[self.resize(image=UpperCAmelCase_				,			size=UpperCAmelCase_				,			resample=UpperCAmelCase_   ) for image in images]

       if do_center_crop:
        snake_case__  :    Any							       =[self.center_crop(image=UpperCAmelCase_				,			size=UpperCAmelCase_   ) for image in images]

       if do_rescale:
        snake_case__  :    Tuple							       =[self.rescale(image=UpperCAmelCase_				,			scale=UpperCAmelCase_   ) for image in images]

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
        snake_case__  :    Optional[int]							       =[self.flip_channel_order(image=UpperCAmelCase_   ) for image in images]

       snake_case__  :    Optional[int]							       =[to_channel_dimension_format(UpperCAmelCase_				,			UpperCAmelCase_   ) for image in images]

       snake_case__  :    int							       ={"""pixel_values""": images}
       return BatchFeature(data=UpperCAmelCase_				,			tensor_type=UpperCAmelCase_   )



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    Union[str, Any]							       =outputs.logits

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

        if is_torch_tensor(UpperCAmelCase_   ):
         snake_case__  :    Optional[int]							       =target_sizes.numpy()

        snake_case__  :    Any							       =[]

        for idx in range(len(UpperCAmelCase_   )   ):
         snake_case__  :    List[Any]							       =torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0   )				,			size=target_sizes[idx]				,			mode="""bilinear"""				,			align_corners=UpperCAmelCase_   )
         snake_case__  :    Optional[int]							       =resized_logits[0].argmax(dim=0   )
         semantic_segmentation.append(UpperCAmelCase_   )
       else:
        snake_case__  :    Optional[Any]							       =logits.argmax(dim=1   )
        snake_case__  :    int							       =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

       return semantic_segmentation













import os
import re
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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		Optional[Any]										=   {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__lowerCamelCase	:		List[str]										=   {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}







class  _lowercase   (   _A						):
      _a							:						int										=							VOCAB_FILES_NAMES
      _a							:						List[str]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Any										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      _a							:						List[int]										=							[]
      def __init__(     self				,			a				,			a="<unk>"				,			a="<s>"				,			a="</s>"				,			a="<pad>"				,			a="[SEP]"				,			a="[MASK]"				,			a="[CLS]"				,			a = None				,			**a				,			):
       snake_case__  :    str							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else bos_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    Any							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else cls_token
       snake_case__  :    Tuple							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case__  :    Union[str, Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

       snake_case__  :    Union[str, Any]							       ={} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=a				,			eos_token=a				,			unk_token=a				,			pad_token=a				,			sep_token=a				,			mask_token=a				,			cls_token=a				,			sp_model_kwargs=self.sp_model_kwargs				,			**a				,			)

       snake_case__  :    Optional[int]							       =vocab_file

       snake_case__  :    List[str]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(a   )
      @property
      def 						lowercase__					(     self   ):
       return self.sp_model.get_piece_size()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ={self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab
      def __getstate__(     self   ):
       snake_case__  :    List[Any]							       =self.__dict__.copy()
       snake_case__  :    Optional[int]							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[Any]							       ={}

       snake_case__  :    Tuple							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.piece_to_id(a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Dict							       =self.sp_model.IdToPiece(a   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =[]
       snake_case__  :    Dict							       =""""""
       snake_case__  :    Dict							       =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(a   ) + token
         snake_case__  :    Optional[Any]							       =True
         snake_case__  :    Dict							       =[]
        else:
         current_sub_tokens.append(a   )
         snake_case__  :    List[Any]							       =False
       out_string += self.sp_model.decode(a   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a				,			a = False				,			a = None				,			a = True				,			**a				,			):
       snake_case__  :    List[str]							       =kwargs.pop("""use_source_tokenizer"""				,			a   )

       snake_case__  :    int							       =self.convert_ids_to_tokens(a				,			skip_special_tokens=a   )

       # 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
       snake_case__  :    List[Any]							       =[]
       snake_case__  :    int							       =[]
       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(a   )   )
          snake_case__  :    List[Any]							       =[]
         sub_texts.append(a   )
        else:
         current_sub_text.append(a   )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(a   )   )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
        snake_case__  :    Dict							       =re.sub(R""" (\[(MASK|SEP)\])"""				,			R"""\1"""				,			""" """.join(a   )   )
       else:
        snake_case__  :    Dict							       ="""""".join(a   )

       snake_case__  :    str							       =(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        snake_case__  :    Any							       =self.clean_up_tokenization(a   )
        return clean_text
       else:
        return text
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Any							       =os.path.join(
           a				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ) and os.path.isfile(self.vocab_file   ):
        copyfile(self.vocab_file				,			a   )
       elif not os.path.isfile(self.vocab_file   ):
        with open(a				,			"""wb"""   ) as fi:
         snake_case__  :    Any							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       return (out_vocab_file,)
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    Union[str, Any]							       =[self.cls_token_id]
       snake_case__  :    Dict							       =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=a				,			token_ids_a=a				,			already_has_special_tokens=a   )

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



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    List[Any]							       =[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]





