


'''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:
						lowerCamelCase__					=   False


lowerCamelCase__					=   logging.get_logger(__name__)
lowerCamelCase__					=   '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						:				Optional[Any]	    =					image_tensor.unsqueeze(0      )
		_UpperCAmelCase						:				List[Any]	    =					torch.nn.functional.unfold(__lowerCAmelCase					,       (patch_height, patch_width)					,       stride=(patch_height, patch_width)      )
		_UpperCAmelCase						:				List[Any]	    =					patches.reshape(image_tensor.size(0      )					,       image_tensor.size(1      )					,       __lowerCAmelCase					,       __lowerCAmelCase					,       -1      )
		_UpperCAmelCase						:				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        __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						:				Optional[int]	    =					textwrap.TextWrapper(width=80      )
		_UpperCAmelCase						:				Union[str, Any]	    =					wrapper.wrap(text=__lowerCAmelCase      )
		_UpperCAmelCase						:				Union[str, Any]	    =					"\n".join(__lowerCAmelCase      )

		if font_bytes is not None and font_path is None:
				_UpperCAmelCase						:				List[Any]	    =					io.BytesIO(__lowerCAmelCase      )
		elif font_path is not None:
				_UpperCAmelCase						:				Dict	    =					font_path
		else:
				_UpperCAmelCase						:				Any	    =					hf_hub_download(__lowerCAmelCase					,       "Arial.TTF"      )
		_UpperCAmelCase						:				Any	    =					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						:				Union[str, Any]	    =					ImageDraw.Draw(Image.new("RGB"					,       (1, 1)					,       __lowerCAmelCase      )      )
		_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				Union[str, Any]	    =					temp_draw.textbbox((0, 0)					,       __lowerCAmelCase					,       __lowerCAmelCase      )

		# Create the actual image with a bit of padding around the text.
		_UpperCAmelCase						:				Dict	    =					text_width + left_padding + right_padding
		_UpperCAmelCase						:				List[str]	    =					text_height + top_padding + bottom_padding
		_UpperCAmelCase						:				Optional[int]	    =					Image.new("RGB"					,       (image_width, image_height)					,       __lowerCAmelCase      )
		_UpperCAmelCase						:				Union[str, Any]	    =					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						:				Any	    =					to_pil_image(__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					render_text(__lowerCAmelCase					,       **__lowerCAmelCase      )
		_UpperCAmelCase						:				List[str]	    =					max(header_image.width					,       image.width      )

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

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

		if infer_channel_dimension_format(__lowerCAmelCase      ) == ChannelDimension.LAST:
				_UpperCAmelCase						:				str	    =					to_channel_dimension_format(__lowerCAmelCase					,       ChannelDimension.LAST      )

		return new_image






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Optional[int]				      =   ["flattened_patches"]




			def __init__(						self     :   Union[str, Any]		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   int = 20_48		,     lowerCamelCase__     :   bool = False		,     **lowerCamelCase__     :   str		,     )      ->None:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					patch_size if patch_size is not None else {"height": 16, "width": 16}
					_UpperCAmelCase						:				List[Any]	    =					do_normalize
					_UpperCAmelCase						:				int	    =					do_convert_rgb
					_UpperCAmelCase						:				Tuple	    =					max_patches
					_UpperCAmelCase						:				Optional[int]	    =					is_vqa




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   dict		,     **lowerCamelCase__     :   str       )      ->np.ndarray:



					'''simple docstring'''







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

					# convert to torch
					_UpperCAmelCase						:				List[Any]	    =					to_channel_dimension_format(lowerCamelCase__		,     ChannelDimension.FIRST       )
					_UpperCAmelCase						:				Union[str, Any]	    =					torch.from_numpy(lowerCamelCase__       )

					_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					patch_size["height"], patch_size["width"]
					_UpperCAmelCase    ,			_UpperCAmelCase						:				Union[str, Any]	    =					get_image_size(lowerCamelCase__       )

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

					_UpperCAmelCase						:				List[str]	    =					torch.nn.functional.interpolate(
					    image.unsqueeze(0       )		,     size=(resized_height, resized_width)		,     mode="bilinear"		,     align_corners=lowerCamelCase__		,     antialias=lowerCamelCase__		,     ).squeeze(0       )

					# [1, rows, columns, patch_height * patch_width * image_channels]
					_UpperCAmelCase						:				int	    =					torch_extract_patches(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__       )

					_UpperCAmelCase						:				List[Any]	    =					patches.shape
					_UpperCAmelCase						:				List[Any]	    =					patches_shape[1]
					_UpperCAmelCase						:				Optional[Any]	    =					patches_shape[2]
					_UpperCAmelCase						:				Any	    =					patches_shape[3]

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

					# [rows * columns, 1]
					_UpperCAmelCase						:				List[Any]	    =					torch.arange(lowerCamelCase__       ).reshape([rows, 1]       ).repeat(1		,     lowerCamelCase__       ).reshape([rows * columns, 1]       )
					_UpperCAmelCase						:				Union[str, Any]	    =					torch.arange(lowerCamelCase__       ).reshape([1, columns]       ).repeat(lowerCamelCase__		,     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						:				Optional[int]	    =					row_ids.to(torch.floataa       )
					_UpperCAmelCase						:				Dict	    =					col_ids.to(torch.floataa       )

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

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

					_UpperCAmelCase						:				Optional[Any]	    =					to_numpy_array(lowerCamelCase__       )

					return result




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Union[str, Any]       )      ->np.ndarray:



					'''simple docstring'''







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

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

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






			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   ImageInput		,     lowerCamelCase__     :   Optional[str] = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Optional[bool] = None		,     lowerCamelCase__     :   Optional[int] = None		,     lowerCamelCase__     :   Optional[Dict[str, int]] = None		,     lowerCamelCase__     :   Optional[Union[str, TensorType]] = None		,     lowerCamelCase__     :   ChannelDimension = ChannelDimension.FIRST		,     **lowerCamelCase__     :   Any		,     )      ->ImageInput:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					do_normalize if do_normalize is not None else self.do_normalize
					_UpperCAmelCase						:				List[Any]	    =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
					_UpperCAmelCase						:				Dict	    =					patch_size if patch_size is not None else self.patch_size
					_UpperCAmelCase						:				Optional[int]	    =					max_patches if max_patches is not None else self.max_patches
					_UpperCAmelCase						:				Union[str, Any]	    =					self.is_vqa

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

					_UpperCAmelCase						:				str	    =					make_list_of_images(lowerCamelCase__       )

					if not valid_images(lowerCamelCase__       ):
							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						:				Dict	    =					[convert_to_rgb(lowerCamelCase__       ) for image in images]

					# All transformations expect numpy arrays.
					_UpperCAmelCase						:				List[str]	    =					[to_numpy_array(lowerCamelCase__       ) for image in images]

					if is_vqa:
							if header_text is None:
									raise ValueError("A header text must be provided for VQA models."       )
							_UpperCAmelCase						:				List[str]	    =					kwargs.pop("font_bytes"		,     lowerCamelCase__       )
							_UpperCAmelCase						:				List[Any]	    =					kwargs.pop("font_path"		,     lowerCamelCase__       )

							if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									_UpperCAmelCase						:				Optional[Any]	    =					[header_text] * len(lowerCamelCase__       )

							_UpperCAmelCase						:				Tuple	    =					[
							    render_header(lowerCamelCase__		,     header_text[i]		,     font_bytes=lowerCamelCase__		,     font_path=lowerCamelCase__       )
							    for i, image in enumerate(lowerCamelCase__       )
							]

					if do_normalize:
							_UpperCAmelCase						:				str	    =					[self.normalize(image=lowerCamelCase__       ) for image in images]

					# convert to torch tensor and permute
					_UpperCAmelCase						:				str	    =					[
					    self.extract_flattened_patches(image=lowerCamelCase__		,     max_patches=lowerCamelCase__		,     patch_size=lowerCamelCase__       )
					    for image in images
					]

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

					_UpperCAmelCase						:				Optional[int]	    =					BatchFeature(
					    data={"flattened_patches": images, "attention_mask": attention_masks}		,     tensor_type=lowerCamelCase__       )

					return encoded_outputs







'''simple docstring'''


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				int	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Any	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[Any]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Union[str, Any]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Dict	    =					{"text": "string"}
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       split=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"					,       [str, list]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Union[str, Any]	    =					text_path
		elif issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				int	    =					[text_path]
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Union[str, Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[int]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=("train",)      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		for split in splits:
				_UpperCAmelCase						:				List[str]	    =					dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Tuple	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Any	    =					TextDatasetReader({"train": text_path}					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		_UpperCAmelCase						:				List[Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				List[str]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Optional[int]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Tuple	    =					TextDatasetReader({"train": text_path}					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if split:
				_UpperCAmelCase						:				int	    =					{split: text_path}
		else:
				_UpperCAmelCase						:				Tuple	    =					"train"
				_UpperCAmelCase						:				List[str]	    =					{"train": text_path, "test": text_path}
		_UpperCAmelCase						:				Optional[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Optional[int]	    =					{"text": "string"}
		_UpperCAmelCase						:				int	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase					,       splits=list(path.keys()      )      )
		assert all(dataset[split].split == split for split in path.keys()      )



