

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 						_snake_case(   )							->				Dict:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Dict:





    '''simple docstring'''






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

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





    '''simple docstring'''






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

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

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

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

    # 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)   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       ImageDraw.Draw(SCREAMING_SNAKE_CASE__				)
    draw.text(xy=(left_padding, top_padding)   ,      text=SCREAMING_SNAKE_CASE__   ,      fill=SCREAMING_SNAKE_CASE__   ,      font=SCREAMING_SNAKE_CASE__				)
    return image
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     np.ndarray   ,      SCREAMING_SNAKE_CASE__      :     str   ,      **SCREAMING_SNAKE_CASE__      :     int				)							->				List[Any]:





    '''simple docstring'''






    requires_backends(SCREAMING_SNAKE_CASE__   ,      'vision'				)

    # Convert to PIL image if necessary
    A__   				=       to_pil_image(SCREAMING_SNAKE_CASE__				)

    A__   				=       render_text(SCREAMING_SNAKE_CASE__   ,      **SCREAMING_SNAKE_CASE__				)
    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(SCREAMING_SNAKE_CASE__				)

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

    return new_image


class 			A						(							_UpperCAmelCase			):
    lowerCamelCase		     =							['flattened_patches']


    def __init__(				self			:	Optional[int],lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 2_0_4_8,lowercase_			:	bool = False,**lowercase_			:	str,)->   None:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        A__   				=       patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
        A__   				=       do_normalize
        A__   				=       do_convert_rgb
        A__   				=       max_patches
        A__   				=       is_vqa


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	np.ndarray,lowercase_			:	int,lowercase_			:	dict,**lowercase_			:	Optional[int]		)->   np.ndarray:





        '''simple docstring'''







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

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

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

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

        # [1, rows, columns, patch_height * patch_width * image_channels]
        A__   				=       torch_extract_patches(lowercase_,lowercase_,lowercase_		)

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

        A__   				=       to_numpy_array(lowercase_		)

        return result


    def 						snake_case__					(				self			:	str,lowercase_			:	np.ndarray,lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	int		)->   np.ndarray:





        '''simple docstring'''







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

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

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


    def 						snake_case__					(				self			:	str,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_			:	Dict,)->   ImageInput:





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

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

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

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

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

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

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

        return encoded_outputs




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

