

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 :    List[str]      		=     False


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



def 				snake_case_()							->     List[Any]:



  """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_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



  """simple docstring"""






  requires_backends(lowerCAmelCase__			,	['''torch''']					)
  _check_torch_version()

  _snake_case						=							image_tensor.unsqueeze(0					)
  _snake_case						=							torch.nn.functional.unfold(lowerCAmelCase__			,	(patch_height, patch_width)			,	stride=(patch_height, patch_width)					)
  _snake_case						=							patches.reshape(image_tensor.size(0					)			,	image_tensor.size(1					)			,	lowerCAmelCase__			,	lowerCAmelCase__			,	-1					)
  _snake_case						=							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_(_UpperCamelCase			,	_UpperCamelCase = 36			,	_UpperCamelCase = "black"			,	_UpperCamelCase = "white"			,	_UpperCamelCase = 5			,	_UpperCamelCase = 5			,	_UpperCamelCase = 5			,	_UpperCamelCase = 5			,	_UpperCamelCase = None			,	_UpperCamelCase = None			,	)							->     Tuple:



  """simple docstring"""






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

  _snake_case						=							textwrap.TextWrapper(width=80					)
  _snake_case						=							wrapper.wrap(text=lowerCAmelCase__					)
  _snake_case						=							'''\n'''.join(lowerCAmelCase__					)

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

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

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	**_UpperCamelCase					)							->     Any:



  """simple docstring"""






  requires_backends(lowerCAmelCase__			,	'''vision'''					)

  # Convert to PIL image if necessary
  _snake_case						=							to_pil_image(lowerCAmelCase__					)

  _snake_case						=							render_text(lowerCAmelCase__			,	**lowerCAmelCase__					)
  _snake_case						=							max(header_image.width			,	image.width					)

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

  _snake_case						=							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
  _snake_case						=							to_numpy_array(lowerCAmelCase__					)

  if infer_channel_dimension_format(lowerCAmelCase__					) == ChannelDimension.LAST:
    _snake_case						=							to_channel_dimension_format(lowerCAmelCase__			,	ChannelDimension.LAST					)

  return new_image



class   lowercase_   (  SCREAMING_SNAKE_CASE__					):
      UpperCamelCase_      :		Optional[Any]										=    ['''flattened_patches''']



      def __init__(      self				:    str      , A__				:    List[Any] = True      , A__				:    Optional[Any] = True      , A__				:    List[Any] = None      , A__				:    List[str] = 2048      , A__				:    Dict = False      , **A__				:    Optional[int]      , )   ->					str:
        super().__init__(**A__		)
        _snake_case						=							patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
        _snake_case						=							do_normalize
        _snake_case						=							do_convert_rgb
        _snake_case						=							max_patches
        _snake_case						=							is_vqa



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Dict      , A__				:    Tuple      , A__				:    Dict      , **A__				:    str		)   ->					int:
        requires_backends(self.extract_flattened_patches      , '''torch'''		)
        _check_torch_version()

        # convert to torch
        _snake_case						=							to_channel_dimension_format(A__      , ChannelDimension.FIRST		)
        _snake_case						=							torch.from_numpy(A__		)

        _snake_case,					_snake_case						=							patch_size['''height'''], patch_size['''width''']
        _snake_case,					_snake_case						=							get_image_size(A__		)

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

        _snake_case						=							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]
        _snake_case						=							torch_extract_patches(A__      , A__      , A__		)

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

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

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

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

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

        _snake_case						=							to_numpy_array(A__		)

        return result



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Tuple      , A__				:    List[str] = None      , **A__				:    Optional[Any]		)   ->					int:
        if image.dtype == np.uinta:
          _snake_case						=							image.astype(np.floataa		)

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

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







      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[str]      , A__				:    List[Any] = None      , A__				:    Optional[Any] = None      , A__				:    Any = None      , A__				:    Optional[Any] = None      , A__				:    List[Any] = None      , A__				:    Dict = None      , A__				:    Union[str, Any] = ChannelDimension.FIRST      , **A__				:    List[Any]      , )   ->					str:
        _snake_case						=							do_normalize if do_normalize is not None else self.do_normalize
        _snake_case						=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
        _snake_case						=							patch_size if patch_size is not None else self.patch_size
        _snake_case						=							max_patches if max_patches is not None else self.max_patches
        _snake_case						=							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 '''		)

        _snake_case						=							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:
          _snake_case						=							[convert_to_rgb(A__		) for image in images]

        # All transformations expect numpy arrays.
        _snake_case						=							[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.'''		)
          _snake_case						=							kwargs.pop('''font_bytes'''      , A__		)
          _snake_case						=							kwargs.pop('''font_path'''      , A__		)

          if isinstance(A__      , A__		):
            _snake_case						=							[header_text] * len(A__		)

          _snake_case						=							[
              render_header(A__      , header_text[i]      , font_bytes=A__      , font_path=A__		)
              for i, image in enumerate(A__		)
          ]

        if do_normalize:
          _snake_case						=							[self.normalize(image=A__		) for image in images]

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

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

        _snake_case						=							BatchFeature(
            data={'''flattened_patches''': images, '''attention_mask''': attention_masks}      , tensor_type=A__		)

        return encoded_outputs




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

