
'''simple docstring'''

import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
    import textwrap

    from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    __snake_case:  Union[str, Any]								=			False


__snake_case:  Optional[Any]								=			logging.get_logger(__name__)
__snake_case:  List[str]								=			"ybelkada/fonts"






def 	_snake_case						(      ):
  """simple docstring"""







  if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
    raise ImportError(
        f'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
        """Pix2StructImageProcessor. Please upgrade torch."""   )






def 	_snake_case						(      A_       :    List[str]     ,		A_       :    Optional[int]     ,		A_       :    Any   ):
  """simple docstring"""







  requires_backends(A_     ,		["""torch"""]   )
  _check_torch_version()

  a_      :	Union[str, Any]      =							image_tensor.unsqueeze(0   )
  a_      :	Tuple      =							torch.nn.functional.unfold(A_     ,		(patch_height, patch_width)     ,		stride=(patch_height, patch_width)   )
  a_      :	Optional[Any]      =							patches.reshape(image_tensor.size(0   )     ,		image_tensor.size(1   )     ,		A_     ,		A_     ,		-1   )
  a_      :	int      =							patches.permute(0     ,		4     ,		2     ,		3     ,		1   ).reshape(
      image_tensor.size(2   ) // patch_height     ,		image_tensor.size(3   ) // patch_width     ,		image_tensor.size(1   ) * patch_height * patch_width     ,		)
  return patches.unsqueeze(0   )






def 	_snake_case						(      A_       :    str     ,		A_       :    int = 36     ,		A_       :    str = "black"     ,		A_       :    str = "white"     ,		A_       :    int = 5     ,		A_       :    int = 5     ,		A_       :    int = 5     ,		A_       :    int = 5     ,		A_       :    Optional[bytes] = None     ,		A_       :    Optional[str] = None     ,		):
  """simple docstring"""







  requires_backends(A_     ,		"""vision"""   )
  # Add new lines so that each line is no more than 80 characters.

  a_      :	List[Any]      =							textwrap.TextWrapper(width=80   )
  a_      :	Dict      =							wrapper.wrap(text=A_   )
  a_      :	Dict      =							"""\n""".join(A_   )

  if font_bytes is not None and font_path is None:
    a_      :	List[str]      =							io.BytesIO(A_   )
  elif font_path is not None:
    a_      :	Optional[int]      =							font_path
  else:
    a_      :	List[Any]      =							hf_hub_download(A_     ,		"""Arial.TTF"""   )
  a_      :	Optional[Any]      =							ImageFont.truetype(A_     ,		encoding="""UTF-8"""     ,		size=A_   )

  # Use a temporary canvas to determine the width and height in pixels when
  # rendering the text.
  a_      :	str      =							ImageDraw.Draw(Image.new("""RGB"""     ,		(1, 1)     ,		A_   )   )
  a_      , a_      , a_      , a_      :	Union[str, Any]      =							temp_draw.textbbox((0, 0)     ,		A_     ,		A_   )

  # Create the actual image with a bit of padding around the text.
  a_      :	int      =							text_width + left_padding + right_padding
  a_      :	Dict      =							text_height + top_padding + bottom_padding
  a_      :	Any      =							Image.new("""RGB"""     ,		(image_width, image_height)     ,		A_   )
  a_      :	Union[str, Any]      =							ImageDraw.Draw(A_   )
  draw.text(xy=(left_padding, top_padding)     ,		text=A_     ,		fill=A_     ,		font=A_   )
  return image






def 	_snake_case						(      A_       :    np.ndarray     ,		A_       :    str     ,		**A_       :    Optional[Any]   ):
  """simple docstring"""







  requires_backends(A_     ,		"""vision"""   )

  # Convert to PIL image if necessary
  a_      :	str      =							to_pil_image(A_   )

  a_      :	Tuple      =							render_text(A_     ,		**A_   )
  a_      :	List[str]      =							max(header_image.width     ,		image.width   )

  a_      :	List[Any]      =							int(image.height * (new_width / image.width)   )
  a_      :	str      =							int(header_image.height * (new_width / header_image.width)   )

  a_      :	Union[str, Any]      =							Image.new("""RGB"""     ,		(new_width, new_height + new_header_height)     ,		"""white"""   )
  new_image.paste(header_image.resize((new_width, new_header_height)   )     ,		(0, 0)   )
  new_image.paste(image.resize((new_width, new_height)   )     ,		(0, new_header_height)   )

  # Convert back to the original framework if necessary
  a_      :	Optional[Any]      =							to_numpy_array(A_   )

  if infer_channel_dimension_format(A_   ) == ChannelDimension.LAST:
    a_      :	Optional[int]      =							to_channel_dimension_format(A_     ,		ChannelDimension.LAST   )

  return new_image




class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					["flattened_patches"]


     def __init__(      self							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 20_48							,		lowerCAmelCase_ = False							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(**lowerCAmelCase_ )
       a_      :	List[str]      =							patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
       a_      :	List[Any]      =							do_normalize
       a_      :	Optional[Any]      =							do_convert_rgb
       a_      :	List[str]      =							max_patches
       a_      :	Tuple      =							is_vqa


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       requires_backends(self.extract_flattened_patches							,		"""torch""" )
       _check_torch_version()

       # convert to torch
       a_      :	List[Any]      =							to_channel_dimension_format(lowerCAmelCase_							,		ChannelDimension.FIRST )
       a_      :	Tuple      =							torch.from_numpy(lowerCAmelCase_ )

       a_      , a_      :	Any      =							patch_size["""height"""], patch_size["""width"""]
       a_      , a_      :	Optional[Any]      =							get_image_size(lowerCAmelCase_ )

       # maximize scale s.t.
       a_      :	Optional[Any]      =							math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
       a_      :	Optional[Any]      =							max(min(math.floor(scale * image_height / patch_height )							,		lowerCAmelCase_ )							,		1 )
       a_      :	Optional[int]      =							max(min(math.floor(scale * image_width / patch_width )							,		lowerCAmelCase_ )							,		1 )
       a_      :	Dict      =							max(num_feasible_rows * patch_height							,		1 )
       a_      :	Optional[Any]      =							max(num_feasible_cols * patch_width							,		1 )

       a_      :	List[str]      =							torch.nn.functional.interpolate(
           image.unsqueeze(0 )							,		size=(resized_height, resized_width)							,		mode="""bilinear"""							,		align_corners=lowerCAmelCase_							,		antialias=lowerCAmelCase_							,		).squeeze(0 )

       # [1, rows, columns, patch_height * patch_width * image_channels]
       a_      :	Any      =							torch_extract_patches(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

       a_      :	Dict      =							patches.shape
       a_      :	int      =							patches_shape[1]
       a_      :	List[Any]      =							patches_shape[2]
       a_      :	Union[str, Any]      =							patches_shape[3]

       # [rows * columns, patch_height * patch_width * image_channels]
       a_      :	Tuple      =							patches.reshape([rows * columns, depth] )

       # [rows * columns, 1]
       a_      :	Dict      =							torch.arange(lowerCAmelCase_ ).reshape([rows, 1] ).repeat(1							,		lowerCAmelCase_ ).reshape([rows * columns, 1] )
       a_      :	Optional[Any]      =							torch.arange(lowerCAmelCase_ ).reshape([1, columns] ).repeat(lowerCAmelCase_							,		1 ).reshape([rows * columns, 1] )

       # Offset by 1 so the ids do not contain zeros, which represent padding.
       row_ids += 1
       col_ids += 1

       # Prepare additional patch features.
       # [rows * columns, 1]
       a_      :	Any      =							row_ids.to(torch.floataa )
       a_      :	int      =							col_ids.to(torch.floataa )

       # [rows * columns, 2 + patch_height * patch_width * image_channels]
       a_      :	List[Any]      =							torch.cat([row_ids, col_ids, patches]							,		-1 )

       # [max_patches, 2 + patch_height * patch_width * image_channels]
       a_      :	Tuple      =							torch.nn.functional.pad(lowerCAmelCase_							,		[0, 0, 0, max_patches - (rows * columns)] ).float()

       a_      :	List[str]      =							to_numpy_array(lowerCAmelCase_ )

       return result


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       if image.dtype == np.uinta:
         a_      :	Any      =							image.astype(np.floataa )

       # take mean across the whole `image`
       a_      :	Optional[Any]      =							np.mean(lowerCAmelCase_ )
       a_      :	Dict      =							np.std(lowerCAmelCase_ )
       a_      :	List[Any]      =							max(lowerCAmelCase_							,		1.0 / math.sqrt(np.prod(image.shape ) ) )

       return normalize(lowerCAmelCase_							,		mean=lowerCAmelCase_							,		std=lowerCAmelCase_							,		**lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = ChannelDimension.FIRST							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							do_normalize if do_normalize is not None else self.do_normalize
       a_      :	Optional[Any]      =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
       a_      :	Optional[Any]      =							patch_size if patch_size is not None else self.patch_size
       a_      :	Dict      =							max_patches if max_patches is not None else self.max_patches
       a_      :	List[str]      =							self.is_vqa

       if kwargs.get("""data_format"""							,		lowerCAmelCase_ ) is not None:
         raise ValueError("""data_format is not an accepted input as the outputs are """ )

       a_      :	int      =							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.""" )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         a_      :	Optional[Any]      =							[convert_to_rgb(lowerCAmelCase_ ) for image in images]

       # All transformations expect numpy arrays.
       a_      :	List[Any]      =							[to_numpy_array(lowerCAmelCase_ ) for image in images]

       if is_vqa:
         if header_text is None:
           raise ValueError("""A header text must be provided for VQA models.""" )
         a_      :	Dict      =							kwargs.pop("""font_bytes"""							,		lowerCAmelCase_ )
         a_      :	Union[str, Any]      =							kwargs.pop("""font_path"""							,		lowerCAmelCase_ )

         if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
           a_      :	Any      =							[header_text] * len(lowerCAmelCase_ )

         a_      :	List[Any]      =							[
             render_header(lowerCAmelCase_							,		header_text[i]							,		font_bytes=lowerCAmelCase_							,		font_path=lowerCAmelCase_ )
             for i, image in enumerate(lowerCAmelCase_ )
         ]

       if do_normalize:
         a_      :	int      =							[self.normalize(image=lowerCAmelCase_ ) for image in images]

       # convert to torch tensor and permute
       a_      :	Tuple      =							[
           self.extract_flattened_patches(image=lowerCAmelCase_							,		max_patches=lowerCAmelCase_							,		patch_size=lowerCAmelCase_ )
           for image in images
       ]

       # create attention mask in numpy
       a_      :	Optional[int]      =							[(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]

       a_      :	str      =							BatchFeature(
           data={"""flattened_patches""": images, """attention_mask""": attention_masks}							,		tensor_type=lowerCAmelCase_ )

       return encoded_outputs




'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case:  Optional[Any]								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{"vocab_file": "sentencepiece.model"}

__snake_case:  int								=			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

__snake_case:  Union[str, Any]								=			{
    "google/rembert": 2_56,
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					VOCAB_FILES_NAMES
     a_    	=					PRETRAINED_VOCAB_FILES_MAP
     a_    	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[UNK]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[PAD]"							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[MASK]"							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(
           do_lower_case=lowerCAmelCase_							,		remove_space=lowerCAmelCase_							,		keep_accents=lowerCAmelCase_							,		bos_token=lowerCAmelCase_							,		eos_token=lowerCAmelCase_							,		unk_token=lowerCAmelCase_							,		sep_token=lowerCAmelCase_							,		pad_token=lowerCAmelCase_							,		cls_token=lowerCAmelCase_							,		mask_token=lowerCAmelCase_							,		**lowerCAmelCase_							,		)

       a_      :	int      =							do_lower_case
       a_      :	int      =							remove_space
       a_      :	Dict      =							keep_accents
       a_      :	Union[str, Any]      =							vocab_file

       a_      :	List[str]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(lowerCAmelCase_ )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return len(self.sp_model )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							{self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab


     def __getstate__(      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.__dict__.copy()
       a_      :	Optional[Any]      =							None
       return state


     def __setstate__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							d
       a_      :	Optional[int]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.EncodeAsPieces(lowerCAmelCase_ )
       return pieces


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.PieceToId(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.IdToPiece(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.decode_pieces(lowerCAmelCase_ )
       return out_string


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[Any]      =							[self.sep_token_id]
       a_      :	Tuple      =							[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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = False ):

       '''simple docstring'''

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               """You should not supply a second sequence if the provided sequence of """
               """ids is already formatted with special tokens for the model.""" )
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	Optional[int]      =							[self.sep_token_id]
       a_      :	Optional[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]






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if not os.path.isdir(lowerCAmelCase_ ):
         logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCAmelCase_ ) )
         return
       a_      :	List[str]      =							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,)


