

'''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:
  _a					:			Any							=    False


_a					:			Any							=    logging.get_logger(__name__)
_a					:			Any							=    'ybelkada/fonts'


def    lowerCamelCase__		(    ):
  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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  requires_backends(SCREAMING_SNAKE_CASE							, ['torch']    )
  _check_torch_version()

  UpperCAmelCase    				=						image_tensor.unsqueeze(0    )
  UpperCAmelCase    				=						torch.nn.functional.unfold(SCREAMING_SNAKE_CASE							, (patch_height, patch_width)							, stride=(patch_height, patch_width)    )
  UpperCAmelCase    				=						patches.reshape(image_tensor.size(0    )							, image_tensor.size(1    )							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, -1    )
  UpperCAmelCase    				=						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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : int = 36							, SCREAMING_SNAKE_CASE   : str = "black"							, SCREAMING_SNAKE_CASE   : str = "white"							, SCREAMING_SNAKE_CASE   : int = 5							, SCREAMING_SNAKE_CASE   : int = 5							, SCREAMING_SNAKE_CASE   : int = 5							, SCREAMING_SNAKE_CASE   : int = 5							, SCREAMING_SNAKE_CASE   : Optional[bytes] = None							, SCREAMING_SNAKE_CASE   : Optional[str] = None							, ):
  requires_backends(SCREAMING_SNAKE_CASE							, 'vision'    )
  # Add new lines so that each line is no more than 80 characters.

  UpperCAmelCase    				=						textwrap.TextWrapper(width=80    )
  UpperCAmelCase    				=						wrapper.wrap(text=SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						'\n'.join(SCREAMING_SNAKE_CASE    )

  if font_bytes is not None and font_path is None:
    UpperCAmelCase    				=						io.BytesIO(SCREAMING_SNAKE_CASE    )
  elif font_path is not None:
    UpperCAmelCase    				=						font_path
  else:
    UpperCAmelCase    				=						hf_hub_download(SCREAMING_SNAKE_CASE							, 'Arial.TTF'    )
  UpperCAmelCase    				=						ImageFont.truetype(SCREAMING_SNAKE_CASE							, encoding='UTF-8'							, size=SCREAMING_SNAKE_CASE    )

  # Use a temporary canvas to determine the width and height in pixels when
  # rendering the text.
  UpperCAmelCase    				=						ImageDraw.Draw(Image.new('RGB'							, (1, 1)							, SCREAMING_SNAKE_CASE    )    )
  UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						temp_draw.textbbox((0, 0)							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  # Create the actual image with a bit of padding around the text.
  UpperCAmelCase    				=						text_width + left_padding + right_padding
  UpperCAmelCase    				=						text_height + top_padding + bottom_padding
  UpperCAmelCase    				=						Image.new('RGB'							, (image_width, image_height)							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						ImageDraw.Draw(SCREAMING_SNAKE_CASE    )
  draw.text(xy=(left_padding, top_padding)							, text=SCREAMING_SNAKE_CASE							, fill=SCREAMING_SNAKE_CASE							, font=SCREAMING_SNAKE_CASE    )
  return image


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : np.ndarray							, SCREAMING_SNAKE_CASE   : str							, **SCREAMING_SNAKE_CASE   : Dict    ):
  requires_backends(SCREAMING_SNAKE_CASE							, 'vision'    )

  # Convert to PIL image if necessary
  UpperCAmelCase    				=						to_pil_image(SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						render_text(SCREAMING_SNAKE_CASE							, **SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						max(header_image.width							, image.width    )

  UpperCAmelCase    				=						int(image.height * (new_width / image.width)    )
  UpperCAmelCase    				=						int(header_image.height * (new_width / header_image.width)    )

  UpperCAmelCase    				=						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
  UpperCAmelCase    				=						to_numpy_array(SCREAMING_SNAKE_CASE    )

  if infer_channel_dimension_format(SCREAMING_SNAKE_CASE    ) == ChannelDimension.LAST:
    UpperCAmelCase    				=						to_channel_dimension_format(SCREAMING_SNAKE_CASE							, ChannelDimension.LAST    )

  return new_image



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[Any]     	= ["flattened_patches"]
  def __init__(  self ,			a_ = True ,			a_ = True ,			a_ = None ,			a_ = 2_0_4_8 ,			a_ = False ,			**a_ ,			)				->					None:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
    UpperCAmelCase    				=						do_normalize
    UpperCAmelCase    				=						do_convert_rgb
    UpperCAmelCase    				=						max_patches
    UpperCAmelCase    				=						is_vqa
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			**a_  )				->					np.ndarray:


    """simple docstring"""
    requires_backends(self.extract_flattened_patches ,			'torch'  )
    _check_torch_version()

    # convert to torch
    UpperCAmelCase    				=						to_channel_dimension_format(a_ ,			ChannelDimension.FIRST  )
    UpperCAmelCase    				=						torch.from_numpy(a_  )

    UpperCAmelCase				,   UpperCAmelCase    				=						patch_size['height'], patch_size['width']
    UpperCAmelCase				,   UpperCAmelCase    				=						get_image_size(a_  )

    # maximize scale s.t.
    UpperCAmelCase    				=						math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)  )
    UpperCAmelCase    				=						max(min(math.floor(scale * image_height / patch_height  ) ,			a_  ) ,			1  )
    UpperCAmelCase    				=						max(min(math.floor(scale * image_width / patch_width  ) ,			a_  ) ,			1  )
    UpperCAmelCase    				=						max(num_feasible_rows * patch_height ,			1  )
    UpperCAmelCase    				=						max(num_feasible_cols * patch_width ,			1  )

    UpperCAmelCase    				=						torch.nn.functional.interpolate(
        image.unsqueeze(0  ) ,			size=(resized_height, resized_width) ,			mode='bilinear' ,			align_corners=a_ ,			antialias=a_ ,			).squeeze(0  )

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

    UpperCAmelCase    				=						patches.shape
    UpperCAmelCase    				=						patches_shape[1]
    UpperCAmelCase    				=						patches_shape[2]
    UpperCAmelCase    				=						patches_shape[3]

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

    # [rows * columns, 1]
    UpperCAmelCase    				=						torch.arange(a_  ).reshape([rows, 1]  ).repeat(1 ,			a_  ).reshape([rows * columns, 1]  )
    UpperCAmelCase    				=						torch.arange(a_  ).reshape([1, columns]  ).repeat(a_ ,			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]
    UpperCAmelCase    				=						row_ids.to(torch.floataa  )
    UpperCAmelCase    				=						col_ids.to(torch.floataa  )

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

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

    UpperCAmelCase    				=						to_numpy_array(a_  )

    return result
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			**a_  )				->					np.ndarray:


    """simple docstring"""
    if image.dtype == np.uinta:
      UpperCAmelCase    				=						image.astype(np.floataa  )

    # take mean across the whole `image`
    UpperCAmelCase    				=						np.mean(a_  )
    UpperCAmelCase    				=						np.std(a_  )
    UpperCAmelCase    				=						max(a_ ,			1.0 / math.sqrt(np.prod(image.shape  )  )  )

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





  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = ChannelDimension.FIRST ,			**a_ ,			)				->					ImageInput:


    """simple docstring"""
    UpperCAmelCase    				=						do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase    				=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
    UpperCAmelCase    				=						patch_size if patch_size is not None else self.patch_size
    UpperCAmelCase    				=						max_patches if max_patches is not None else self.max_patches
    UpperCAmelCase    				=						self.is_vqa

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

    UpperCAmelCase    				=						make_list_of_images(a_  )

    if not valid_images(a_  ):
      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:
      UpperCAmelCase    				=						[convert_to_rgb(a_  ) for image in images]

    # All transformations expect numpy arrays.
    UpperCAmelCase    				=						[to_numpy_array(a_  ) for image in images]

    if is_vqa:
      if header_text is None:
        raise ValueError('A header text must be provided for VQA models.'  )
      UpperCAmelCase    				=						kwargs.pop('font_bytes' ,			a_  )
      UpperCAmelCase    				=						kwargs.pop('font_path' ,			a_  )

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

      UpperCAmelCase    				=						[
          render_header(a_ ,			header_text[i] ,			font_bytes=a_ ,			font_path=a_  )
          for i, image in enumerate(a_  )
      ]

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

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

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

    UpperCAmelCase    				=						BatchFeature(
        data={'flattened_patches': images, 'attention_mask': attention_masks} ,			tensor_type=a_  )

    return encoded_outputs





'''simple docstring'''




from ..utils import DummyObject, requires_backends



class 							lowercase_						(		metaclass=a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= ["keras_nlp"]
  def __init__(  self ,			*a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    requires_backends(self ,			['keras_nlp']  )


