
'''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:
						lowercase__						       =      False


lowercase__						       =      logging.get_logger(__name__)
lowercase__						       =      'ybelkada/fonts'




def    _UpperCamelCase						(       )  ->							List[str]:


		'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


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

		snake_case  :       Tuple     			=						image_tensor.unsqueeze(0			)
		snake_case  :       List[str]     			=						torch.nn.functional.unfold(_UpperCAmelCase ,       (patch_height, patch_width) ,       stride=(patch_height, patch_width)			)
		snake_case  :       Optional[int]     			=						patches.reshape(image_tensor.size(0			) ,       image_tensor.size(1			) ,       _UpperCAmelCase ,       _UpperCAmelCase ,       -1			)
		snake_case  :       str     			=						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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = 36 ,       SCREAMING_SNAKE_CASE__ = "black" ,       SCREAMING_SNAKE_CASE__ = "white" ,       SCREAMING_SNAKE_CASE__ = 5 ,       SCREAMING_SNAKE_CASE__ = 5 ,       SCREAMING_SNAKE_CASE__ = 5 ,       SCREAMING_SNAKE_CASE__ = 5 ,       SCREAMING_SNAKE_CASE__ = None ,       SCREAMING_SNAKE_CASE__ = None ,       )  ->							Image.Image:


		'''simple docstring'''


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

		snake_case  :       int     			=						textwrap.TextWrapper(width=80			)
		snake_case  :       int     			=						wrapper.wrap(text=_UpperCAmelCase			)
		snake_case  :       List[str]     			=						'''\n'''.join(_UpperCAmelCase			)

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

		# Use a temporary canvas to determine the width and height in pixels when
		# rendering the text.
		snake_case  :       List[Any]     			=						ImageDraw.Draw(Image.new('''RGB''' ,       (1, 1) ,       _UpperCAmelCase			)			)
		snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						temp_draw.textbbox((0, 0) ,       _UpperCAmelCase ,       _UpperCAmelCase			)

		# Create the actual image with a bit of padding around the text.
		snake_case  :       List[Any]     			=						text_width + left_padding + right_padding
		snake_case  :       Optional[Any]     			=						text_height + top_padding + bottom_padding
		snake_case  :       Dict     			=						Image.new('''RGB''' ,       (image_width, image_height) ,       _UpperCAmelCase			)
		snake_case  :       List[Any]     			=						ImageDraw.Draw(_UpperCAmelCase			)
		draw.text(xy=(left_padding, top_padding) ,       text=_UpperCAmelCase ,       fill=_UpperCAmelCase ,       font=_UpperCAmelCase			)
		return image




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       **SCREAMING_SNAKE_CASE__			)  ->							int:


		'''simple docstring'''


		requires_backends(_UpperCAmelCase ,       '''vision'''			)

		# Convert to PIL image if necessary
		snake_case  :       Any     			=						to_pil_image(_UpperCAmelCase			)

		snake_case  :       Optional[Any]     			=						render_text(_UpperCAmelCase ,       **_UpperCAmelCase			)
		snake_case  :       Optional[int]     			=						max(header_image.width ,       image.width			)

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

		snake_case  :       List[str]     			=						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  :       Union[str, Any]     			=						to_numpy_array(_UpperCAmelCase			)

		if infer_channel_dimension_format(_UpperCAmelCase			) == ChannelDimension.LAST:
				snake_case  :       Tuple     			=						to_channel_dimension_format(_UpperCAmelCase ,       ChannelDimension.LAST			)

		return new_image






class 	snake_case__       (      _SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						["""flattened_patches"""]



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			Any = True  ,	UpperCamelCase__				:			int = True  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Dict = 2048  ,	UpperCamelCase__				:			Tuple = False  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			str:






				"""simple docstring"""







				super().__init__(**__snake_case      )
				snake_case  :       int     			=						patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
				snake_case  :       str     			=						do_normalize
				snake_case  :       Tuple     			=						do_convert_rgb
				snake_case  :       Optional[int]     			=						max_patches
				snake_case  :       Optional[int]     			=						is_vqa



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Any  ,	**UpperCamelCase__				:			Union[str, Any]      )						->			Any:






				"""simple docstring"""







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

				# convert to torch
				snake_case  :       str     			=						to_channel_dimension_format(__snake_case  ,	ChannelDimension.FIRST      )
				snake_case  :       int     			=						torch.from_numpy(__snake_case      )

				snake_case			,snake_case  :       List[Any]     			=						patch_size['''height'''], patch_size['''width''']
				snake_case			,snake_case  :       Tuple     			=						get_image_size(__snake_case      )

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

				snake_case  :       Dict     			=						torch.nn.functional.interpolate(
				    image.unsqueeze(0      )  ,	size=(resized_height, resized_width)  ,	mode='''bilinear'''  ,	align_corners=__snake_case  ,	antialias=__snake_case  ,	).squeeze(0      )

				# [1, rows, columns, patch_height * patch_width * image_channels]
				snake_case  :       Optional[int]     			=						torch_extract_patches(__snake_case  ,	__snake_case  ,	__snake_case      )

				snake_case  :       Optional[int]     			=						patches.shape
				snake_case  :       Any     			=						patches_shape[1]
				snake_case  :       Dict     			=						patches_shape[2]
				snake_case  :       Optional[Any]     			=						patches_shape[3]

				# [rows * columns, patch_height * patch_width * image_channels]
				snake_case  :       List[Any]     			=						patches.reshape([rows * columns, depth]      )

				# [rows * columns, 1]
				snake_case  :       Optional[int]     			=						torch.arange(__snake_case      ).reshape([rows, 1]      ).repeat(1  ,	__snake_case      ).reshape([rows * columns, 1]      )
				snake_case  :       Optional[Any]     			=						torch.arange(__snake_case      ).reshape([1, columns]      ).repeat(__snake_case  ,	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  :       Optional[int]     			=						row_ids.to(torch.floataa      )
				snake_case  :       Dict     			=						col_ids.to(torch.floataa      )

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

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

				snake_case  :       str     			=						to_numpy_array(__snake_case      )

				return result



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Dict = None  ,	**UpperCamelCase__				:			Tuple      )						->			Optional[Any]:






				"""simple docstring"""







				if image.dtype == np.uinta:
						snake_case  :       Optional[Any]     			=						image.astype(np.floataa      )

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

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



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			str = None  ,	UpperCamelCase__				:			Any = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Union[str, Any] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Any = None  ,	UpperCamelCase__				:			Union[str, Any] = ChannelDimension.FIRST  ,	**UpperCamelCase__				:			List[str]  ,	)						->			Optional[Any]:






				"""simple docstring"""







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

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

				snake_case  :       Union[str, Any]     			=						make_list_of_images(__snake_case      )

				if not valid_images(__snake_case      ):
						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  :       Union[str, Any]     			=						[convert_to_rgb(__snake_case      ) for image in images]

				# All transformations expect numpy arrays.
				snake_case  :       Optional[int]     			=						[to_numpy_array(__snake_case      ) 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  :       Dict     			=						kwargs.pop('''font_bytes'''  ,	__snake_case      )
						snake_case  :       int     			=						kwargs.pop('''font_path'''  ,	__snake_case      )

						if isinstance(__snake_case  ,	__snake_case      ):
								snake_case  :       Optional[int]     			=						[header_text] * len(__snake_case      )

						snake_case  :       str     			=						[
						    render_header(__snake_case  ,	header_text[i]  ,	font_bytes=__snake_case  ,	font_path=__snake_case      )
						    for i, image in enumerate(__snake_case      )
						]

				if do_normalize:
						snake_case  :       List[str]     			=						[self.normalize(image=__snake_case      ) for image in images]

				# convert to torch tensor and permute
				snake_case  :       Optional[Any]     			=						[
				    self.extract_flattened_patches(image=__snake_case  ,	max_patches=__snake_case  ,	patch_size=__snake_case      )
				    for image in images
				]

				# create attention mask in numpy
				snake_case  :       Union[str, Any]     			=						[(image.sum(axis=-1      ) != 0).astype(np.floataa      ) for image in images]

				snake_case  :       Any     			=						BatchFeature(
				    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}  ,	tensor_type=__snake_case      )

				return encoded_outputs









from math import pi, sqrt, tan

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if side_length < 0:
          raise ValueError('''surface_area_cube() only accepts non-negative values'''				)
     return 6 * side_length**2

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if length < 0 or breadth < 0 or height < 0:
          raise ValueError('''surface_area_cuboid() only accepts non-negative values'''				)
     return 2 * ((length * breadth) + (breadth * height) + (length * height))

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius < 0:
          raise ValueError('''surface_area_sphere() only accepts non-negative values'''				)
     return 4 * pi * radius**2

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius < 0:
          raise ValueError('''surface_area_hemisphere() only accepts non-negative values'''				)
     return 3 * pi * radius**2

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius < 0 or height < 0:
          raise ValueError('''surface_area_cone() only accepts non-negative values'''				)
     return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              '''surface_area_conical_frustum() only accepts non-negative values'''				)
     A_          =       (height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius < 0 or height < 0:
          raise ValueError('''surface_area_cylinder() only accepts non-negative values'''				)
     return 2 * pi * radius * (height + radius)

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if torus_radius < 0 or tube_radius < 0:
          raise ValueError('''surface_area_torus() only accepts non-negative values'''				)
     if torus_radius < tube_radius:
          raise ValueError(
              '''surface_area_torus() does not support spindle or self intersecting tori'''				)
     return 4 * pow(_UpperCAmelCase  ,						2				) * torus_radius * tube_radius

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if length < 0 or width < 0:
          raise ValueError('''area_rectangle() only accepts non-negative values'''				)
     return length * width

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if side_length < 0:
          raise ValueError('''area_square() only accepts non-negative values'''				)
     return side_length**2

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if base < 0 or height < 0:
          raise ValueError('''area_triangle() only accepts non-negative values'''				)
     return (base * height) / 2

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if sidea < 0 or sidea < 0 or sidea < 0:
          raise ValueError('''area_triangle_three_sides() only accepts non-negative values'''				)
     elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
          raise ValueError('''Given three sides do not form a triangle'''				)
     A_          =       (sidea + sidea + sidea) / 2
     A_          =       sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)				)
     return area

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if base < 0 or height < 0:
          raise ValueError('''area_parallelogram() only accepts non-negative values'''				)
     return base * height

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if basea < 0 or basea < 0 or height < 0:
          raise ValueError('''area_trapezium() only accepts non-negative values'''				)
     return 1 / 2 * (basea + basea) * height

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius < 0:
          raise ValueError('''area_circle() only accepts non-negative values'''				)
     return pi * radius**2

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if radius_x < 0 or radius_y < 0:
          raise ValueError('''area_ellipse() only accepts non-negative values'''				)
     return pi * radius_x * radius_y

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if diagonal_a < 0 or diagonal_a < 0:
          raise ValueError('''area_rhombus() only accepts non-negative values'''				)
     return 1 / 2 * diagonal_a * diagonal_a

def      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


     if not isinstance(_UpperCAmelCase  ,						_UpperCAmelCase				) or sides < 3:
          raise ValueError(
              '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'''				)
     elif length < 0:
          raise ValueError(
              '''area_reg_polygon() only accepts non-negative values as \
length of a side'''				)
     return (sides * length**2) / (4 * tan(pi / sides				))
     return (sides * length**2) / (4 * tan(pi / sides				))


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

      print('[DEMO] Areas of various geometric shapes: \n')
      print(f'''Rectangle: {area_rectangle(10, 20) = }''')
      print(f'''Square: {area_square(10) = }''')
      print(f'''Triangle: {area_triangle(10, 10) = }''')
      print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
      print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
      print(f'''Rhombus: {area_rhombus(10, 20) = }''')
      print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
      print(f'''Circle: {area_circle(20) = }''')
      print(f'''Ellipse: {area_ellipse(10, 20) = }''')
      print('\nSurface Areas of various geometric shapes: \n')
      print(f'''Cube: {surface_area_cube(20) = }''')
      print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
      print(f'''Sphere: {surface_area_sphere(20) = }''')
      print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
      print(f'''Cone: {surface_area_cone(10, 20) = }''')
      print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
      print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
      print(f'''Torus: {surface_area_torus(20, 10) = }''')
      print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
      print(f'''Square: {area_reg_polygon(4, 10) = }''')
      print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')



