



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:
	UpperCAmelCase   :							Any							 =False


UpperCAmelCase   :							Tuple							 =logging.get_logger(__name__)
UpperCAmelCase   :							List[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    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					requires_backends(_lowerCAmelCase   ,	["torch"])
					_check_torch_version()

					UpperCamelCase_		  =   image_tensor.unsqueeze(0)
					UpperCamelCase_		  =   torch.nn.functional.unfold(_lowerCAmelCase   ,	(patch_height, patch_width)   ,	stride=(patch_height, patch_width))
					UpperCamelCase_		  =   patches.reshape(image_tensor.size(0)   ,	image_tensor.size(1)   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	-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    (_lowerCAmelCase   ,	_lowerCAmelCase = 36   ,	_lowerCAmelCase = "black"   ,	_lowerCAmelCase = "white"   ,	_lowerCAmelCase = 5   ,	_lowerCAmelCase = 5   ,	_lowerCAmelCase = 5   ,	_lowerCAmelCase = 5   ,	_lowerCAmelCase = None   ,	_lowerCAmelCase = None   ,	):
					requires_backends(_lowerCAmelCase   ,	"vision")
					# Add new lines so that each line is no more than 80 characters.

					UpperCamelCase_		  =   textwrap.TextWrapper(width=80)
					UpperCamelCase_		  =   wrapper.wrap(text=_lowerCAmelCase)
					UpperCamelCase_		  =   "\n".join(_lowerCAmelCase)

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

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

					# 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)   ,	_lowerCAmelCase)
					UpperCamelCase_		  =   ImageDraw.Draw(_lowerCAmelCase)
					draw.text(xy=(left_padding, top_padding)   ,	text=_lowerCAmelCase   ,	fill=_lowerCAmelCase   ,	font=_lowerCAmelCase)
					return image



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	**_lowerCAmelCase):
					requires_backends(_lowerCAmelCase   ,	"vision")

					# Convert to PIL image if necessary
					UpperCamelCase_		  =   to_pil_image(_lowerCAmelCase)

					UpperCamelCase_		  =   render_text(_lowerCAmelCase   ,	**_lowerCAmelCase)
					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(_lowerCAmelCase)

					if infer_channel_dimension_format(_lowerCAmelCase) == ChannelDimension.LAST:
										UpperCamelCase_		  =   to_channel_dimension_format(_lowerCAmelCase   ,	ChannelDimension.LAST)

					return new_image






class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						["""flattened_patches"""]

				def __init__( self     , snake_case__ = True     , snake_case__ = True     , snake_case__ = None     , snake_case__ = 2048     , snake_case__ = False     , **snake_case__     , ):

									'''simple docstring'''


									super().__init__(**snake_case__    )
									UpperCamelCase_		  =   patch_size if patch_size is not None else {"height": 16, "width": 16}
									UpperCamelCase_		  =   do_normalize
									UpperCamelCase_		  =   do_convert_rgb
									UpperCamelCase_		  =   max_patches
									UpperCamelCase_		  =   is_vqa

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__     , **snake_case__    ):

									'''simple docstring'''


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

									# convert to torch
									UpperCamelCase_		  =   to_channel_dimension_format(snake_case__     , ChannelDimension.FIRST    )
									UpperCamelCase_		  =   torch.from_numpy(snake_case__    )

									UpperCamelCase_  ,    UpperCamelCase_		  =   patch_size["height"], patch_size["width"]
									UpperCamelCase_  ,    UpperCamelCase_		  =   get_image_size(snake_case__    )

									# 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    )     , snake_case__    )     , 1    )
									UpperCamelCase_		  =   max(min(math.floor(scale * image_width / patch_width    )     , snake_case__    )     , 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=snake_case__     , antialias=snake_case__     , ).squeeze(0    )

									# [1, rows, columns, patch_height * patch_width * image_channels]
									UpperCamelCase_		  =   torch_extract_patches(snake_case__     , snake_case__     , snake_case__    )

									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(snake_case__    ).reshape([rows, 1]    ).repeat(1     , snake_case__    ).reshape([rows * columns, 1]    )
									UpperCamelCase_		  =   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]
									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(snake_case__     , [0, 0, 0, max_patches - (rows * columns)]    ).float()

									UpperCamelCase_		  =   to_numpy_array(snake_case__    )

									return result

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__ = None     , **snake_case__    ):

									'''simple docstring'''


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

									# take mean across the whole `image`
									UpperCamelCase_		  =   np.mean(snake_case__    )
									UpperCamelCase_		  =   np.std(snake_case__    )
									UpperCamelCase_		  =   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     , snake_case__     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = ChannelDimension.FIRST     , **snake_case__     , ):

									'''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"     , snake_case__    ) is not None:
														raise ValueError("data_format is not an accepted input as the outputs are "    )

									UpperCamelCase_		  =   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:
														UpperCamelCase_		  =   [convert_to_rgb(snake_case__    ) for image in images]

									# All transformations expect numpy arrays.
									UpperCamelCase_		  =   [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."    )
														UpperCamelCase_		  =   kwargs.pop("font_bytes"     , snake_case__    )
														UpperCamelCase_		  =   kwargs.pop("font_path"     , snake_case__    )

														if isinstance(snake_case__     , snake_case__    ):
																			UpperCamelCase_		  =   [header_text] * len(snake_case__    )

														UpperCamelCase_		  =   [
														    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:
														UpperCamelCase_		  =   [self.normalize(image=snake_case__    ) for image in images]

									# convert to torch tensor and permute
									UpperCamelCase_		  =   [
									    self.extract_flattened_patches(image=snake_case__     , max_patches=snake_case__     , patch_size=snake_case__    )
									    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=snake_case__    )

									return encoded_outputs





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase   :							Dict							 =logging.get_logger(__name__)


class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						["""pixel_values"""]

				def __init__( self     , snake_case__ = True     , snake_case__ = None     , snake_case__ = PILImageResampling.BILINEAR     , snake_case__ = True     , snake_case__ = None     , snake_case__ = True     , snake_case__ = 1 / 255     , snake_case__ = True     , snake_case__ = None     , snake_case__ = None     , **snake_case__     , ):

									'''simple docstring'''


									super().__init__(**snake_case__    )
									UpperCamelCase_		  =   size if size is not None else {"shortest_edge": 256}
									UpperCamelCase_		  =   get_size_dict(snake_case__     , default_to_square=snake_case__    )
									UpperCamelCase_		  =   crop_size if crop_size is not None else {"height": 224, "width": 224}
									UpperCamelCase_		  =   get_size_dict(snake_case__    )
									UpperCamelCase_		  =   do_resize
									UpperCamelCase_		  =   size
									UpperCamelCase_		  =   resample
									UpperCamelCase_		  =   do_center_crop
									UpperCamelCase_		  =   crop_size
									UpperCamelCase_		  =   do_rescale
									UpperCamelCase_		  =   rescale_factor
									UpperCamelCase_		  =   do_normalize
									UpperCamelCase_		  =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCamelCase_		  =   image_std if image_std is not None else IMAGENET_STANDARD_STD

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = PILImageResampling.BICUBIC     , snake_case__ = None     , **snake_case__     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   get_size_dict(snake_case__     , default_to_square=snake_case__    )
									if "shortest_edge" not in size:
														raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
									UpperCamelCase_		  =   get_resize_output_image_size(snake_case__     , size=size["shortest_edge"]     , default_to_square=snake_case__    )
									return resize(snake_case__     , size=snake_case__     , resample=snake_case__     , data_format=snake_case__     , **snake_case__    )

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = None     , **snake_case__     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   get_size_dict(snake_case__    )
									return center_crop(snake_case__     , size=(size["height"], size["width"])     , data_format=snake_case__     , **snake_case__    )

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = None     , **snake_case__    ):

									'''simple docstring'''


									return rescale(snake_case__     , scale=snake_case__     , data_format=snake_case__     , **snake_case__    )

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__     , snake_case__ = None     , **snake_case__     , ):

									'''simple docstring'''


									return normalize(snake_case__     , mean=snake_case__     , std=snake_case__     , data_format=snake_case__     , **snake_case__    )

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = None     , snake_case__ = ChannelDimension.FIRST     , **snake_case__     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   do_resize if do_resize is not None else self.do_resize
									UpperCamelCase_		  =   size if size is not None else self.size
									UpperCamelCase_		  =   get_size_dict(snake_case__     , default_to_square=snake_case__    )
									UpperCamelCase_		  =   resample if resample is not None else self.resample
									UpperCamelCase_		  =   do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCamelCase_		  =   crop_size if crop_size is not None else self.crop_size
									UpperCamelCase_		  =   get_size_dict(snake_case__    )
									UpperCamelCase_		  =   do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase_		  =   rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCamelCase_		  =   do_normalize if do_normalize is not None else self.do_normalize
									UpperCamelCase_		  =   image_mean if image_mean is not None else self.image_mean
									UpperCamelCase_		  =   image_std if image_std is not None else self.image_std

									UpperCamelCase_		  =   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."    )

									if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize is True."    )

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

									if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."    )

									if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and std must be specified if do_normalize is True."    )

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

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

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

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

									if do_normalize:
														UpperCamelCase_		  =   [self.normalize(image=snake_case__     , mean=snake_case__     , std=snake_case__    ) for image in images]

									UpperCamelCase_		  =   [to_channel_dimension_format(snake_case__     , snake_case__    ) for image in images]

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