



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__ =						False


UpperCamelCase__ =						logging.get_logger(__name__)
UpperCamelCase__ =						"ybelkada/fonts"






def  UpperCAmelCase__    (    ) ->      List[str]:
						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__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      str:
						requires_backends(lowercase__						,       ["""torch"""]   )
						_check_torch_version()

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

						__lowercase					=				textwrap.TextWrapper(width=80   )
						__lowercase					=				wrapper.wrap(text=lowercase__   )
						__lowercase					=				"""\n""".join(lowercase__   )

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

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

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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       **lowercase__   ) ->      Optional[Any]:
						requires_backends(lowercase__						,       """vision"""   )

						# Convert to PIL image if necessary
						__lowercase					=				to_pil_image(lowercase__   )

						__lowercase					=				render_text(lowercase__						,       **lowercase__   )
						__lowercase					=				max(header_image.width						,       image.width   )

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

						__lowercase					=				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
						__lowercase					=				to_numpy_array(lowercase__   )

						if infer_channel_dimension_format(lowercase__   ) == ChannelDimension.LAST:
												__lowercase					=				to_channel_dimension_format(lowercase__						,       ChannelDimension.LAST   )

						return new_image







class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Optional[Any]      =			["""flattened_patches"""]





			def __init__(				self	:       Optional[int]			,     lowercase	:       bool = True			,     lowercase	:       bool = True			,     lowercase	:       Dict[str, int] = None			,     lowercase	:       int = 2_048			,     lowercase	:       bool = False			,     **lowercase	:       str			,     )						->					None:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       np.ndarray			,     lowercase	:       int			,     lowercase	:       dict			,     **lowercase	:       int   )						->					np.ndarray:
									"""simple docstring"""







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

									# convert to torch
									__lowercase					=				to_channel_dimension_format(lowercase			,     ChannelDimension.FIRST   )
									__lowercase					=				torch.from_numpy(lowercase   )

									__lowercase	,			__lowercase					=				patch_size["""height"""], patch_size["""width"""]
									__lowercase	,			__lowercase					=				get_image_size(lowercase   )

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

									__lowercase					=				torch.nn.functional.interpolate(
									    image.unsqueeze(0   )			,     size=(resized_height, resized_width)			,     mode="""bilinear"""			,     align_corners=lowercase			,     antialias=lowercase			,     ).squeeze(0   )

									# [1, rows, columns, patch_height * patch_width * image_channels]
									__lowercase					=				torch_extract_patches(lowercase			,     lowercase			,     lowercase   )

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

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

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

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

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

									__lowercase					=				to_numpy_array(lowercase   )

									return result





			def 		snake_case__			(				self	:       Any			,     lowercase	:       np.ndarray			,     lowercase	:       Optional[Union[str, ChannelDimension]] = None			,     **lowercase	:       Optional[int]   )						->					np.ndarray:
									"""simple docstring"""







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

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

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







			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       ImageInput			,     lowercase	:       Optional[str] = None			,     lowercase	:       bool = None			,     lowercase	:       Optional[bool] = None			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[Dict[str, int]] = None			,     lowercase	:       Optional[Union[str, TensorType]] = None			,     lowercase	:       ChannelDimension = ChannelDimension.FIRST			,     **lowercase	:       List[str]			,     )						->					ImageInput:
									"""simple docstring"""







									__lowercase					=				do_normalize if do_normalize is not None else self.do_normalize
									__lowercase					=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
									__lowercase					=				patch_size if patch_size is not None else self.patch_size
									__lowercase					=				max_patches if max_patches is not None else self.max_patches
									__lowercase					=				self.is_vqa

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

									__lowercase					=				make_list_of_images(lowercase   )

									if not valid_images(lowercase   ):
															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:
															__lowercase					=				[convert_to_rgb(lowercase   ) for image in images]

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

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

															if isinstance(lowercase			,     lowercase   ):
																					__lowercase					=				[header_text] * len(lowercase   )

															__lowercase					=				[
															    render_header(lowercase			,     header_text[i]			,     font_bytes=lowercase			,     font_path=lowercase   )
															    for i, image in enumerate(lowercase   )
															]

									if do_normalize:
															__lowercase					=				[self.normalize(image=lowercase   ) for image in images]

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

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

									__lowercase					=				BatchFeature(
									    data={"""flattened_patches""": images, """attention_mask""": attention_masks}			,     tensor_type=lowercase   )

									return encoded_outputs










from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase			)
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  str      =			field(default="""summarization"""    ,					metadata={"""include_in_asdict_even_if_is_default""": True}			)
			lowercase__      :  ClassVar[Features]      =			Features({"""text""": Value("""string"""			)}			)
			lowercase__      :  ClassVar[Features]      =			Features({"""summary""": Value("""string"""			)}			)
			lowercase__      :  str      =			"text"
			lowercase__      :  str      =			"summary"





			@property
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict[str, str]:
									"""simple docstring"""







									return {self.text_column: "text", self.summary_column: "summary"}





