






"""simple docstring"""






from collections import deque

from .hash_table import HashTable





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					super().__init__(*__a  ,					**__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       deque([]						) if self.values[key] is None else self.values[key]
					self.values[key].appendleft(__a						)
					A__							 =       self.values[key]


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return (
					    sum(self.charge_factor - len(__a						) for slot in self.values						)
					    / self.size_table
					    * self.charge_factor
					)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=None						):


					"""simple docstring"""

					if not (
					    len(self.values[key]						) == self.charge_factor and self.values.count(__a						) == 0
					):
									return key
					return super()._collision_resolution(__a  ,					__a						)










"""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:
					SCREAMING_SNAKE_CASE						:						str					   =       False


SCREAMING_SNAKE_CASE						:						str					   =       logging.get_logger(__name__)
SCREAMING_SNAKE_CASE						:						Optional[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()

				A__							 =       image_tensor.unsqueeze(0				)
				A__							 =       torch.nn.functional.unfold(lowerCAmelCase__       ,(patch_height, patch_width)       ,stride=(patch_height, patch_width)				)
				A__							 =       patches.reshape(image_tensor.size(0				)       ,image_tensor.size(1				)       ,lowerCAmelCase__       ,lowerCAmelCase__       ,-1				)
				A__							 =       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.

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

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

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

				# Create the actual image with a bit of padding around the text.
				A__							 =       text_width + left_padding + right_padding
				A__							 =       text_height + top_padding + bottom_padding
				A__							 =       Image.new('RGB'       ,(image_width, image_height)       ,lowerCAmelCase__				)
				A__							 =       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
				A__							 =       to_pil_image(lowerCAmelCase__				)

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

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

				A__							 =       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__							 =       to_numpy_array(lowerCAmelCase__				)

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

				return new_image







class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					["""flattened_patches"""]


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


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					**__a						):


					"""simple docstring"""

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

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

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

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

					A__							 =       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]
					A__							 =       torch_extract_patches(__a  ,					__a  ,					__a						)

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

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

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

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

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

					A__							 =       to_numpy_array(__a						)

					return result


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					**__a						):


					"""simple docstring"""

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

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

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






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = ChannelDimension.FIRST  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       do_normalize if do_normalize is not None else self.do_normalize
					A__							 =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
					A__							 =       patch_size if patch_size is not None else self.patch_size
					A__							 =       max_patches if max_patches is not None else self.max_patches
					A__							 =       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 '						)

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

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

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

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

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

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

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

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

					return encoded_outputs


