


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			: Optional[int]				   =					False


SCREAMING_SNAKE_CASE			: str				   =					logging.get_logger(__name__)
SCREAMING_SNAKE_CASE			: str				   =					"ybelkada/fonts"



def 							UpperCamelCase_( )       ->		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_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Any:
    requires_backends(lowerCamelCase_				,					['torch']					)
    _check_torch_version()

    _lowercase			:				Tuple 						=						image_tensor.unsqueeze(0					)
    _lowercase			:				str 						=						torch.nn.functional.unfold(lowerCamelCase_				,					(patch_height, patch_width)				,					stride=(patch_height, patch_width)					)
    _lowercase			:				Dict 						=						patches.reshape(image_tensor.size(0					)				,					image_tensor.size(1					)				,					lowerCamelCase_				,					lowerCamelCase_				,					-1					)
    _lowercase			:				Optional[int] 						=						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_( lowerCamelCase_				,					lowerCamelCase_ = 36				,					lowerCamelCase_ = "black"				,					lowerCamelCase_ = "white"				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = None				,					lowerCamelCase_ = None				,					)       ->		Image.Image:
    requires_backends(lowerCamelCase_				,					'vision'					)
    # Add new lines so that each line is no more than 80 characters.

    _lowercase			:				List[str] 						=						textwrap.TextWrapper(width=80					)
    _lowercase			:				str 						=						wrapper.wrap(text=lowerCamelCase_					)
    _lowercase			:				int 						=						'\n'.join(lowerCamelCase_					)

    if font_bytes is not None and font_path is None:
        _lowercase			:				Union[str, Any] 						=						io.BytesIO(lowerCamelCase_					)
    elif font_path is not None:
        _lowercase			:				str 						=						font_path
    else:
        _lowercase			:				Optional[int] 						=						hf_hub_download(lowerCamelCase_				,					'Arial.TTF'					)
    _lowercase			:				Tuple 						=						ImageFont.truetype(lowerCamelCase_				,					encoding='UTF-8'				,					size=lowerCamelCase_					)

    # Use a temporary canvas to determine the width and height in pixels when
    # rendering the text.
    _lowercase			:				Union[str, Any] 						=						ImageDraw.Draw(Image.new('RGB'				,					(1, 1)				,					lowerCamelCase_					)					)
    _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						temp_draw.textbbox((0, 0)				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Create the actual image with a bit of padding around the text.
    _lowercase			:				Dict 						=						text_width + left_padding + right_padding
    _lowercase			:				Tuple 						=						text_height + top_padding + bottom_padding
    _lowercase			:				List[str] 						=						Image.new('RGB'				,					(image_width, image_height)				,					lowerCamelCase_					)
    _lowercase			:				Tuple 						=						ImageDraw.Draw(lowerCamelCase_					)
    draw.text(xy=(left_padding, top_padding)				,					text=lowerCamelCase_				,					fill=lowerCamelCase_				,					font=lowerCamelCase_					)
    return image



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					**lowerCamelCase_					)       ->		Any:
    requires_backends(lowerCamelCase_				,					'vision'					)

    # Convert to PIL image if necessary
    _lowercase			:				str 						=						to_pil_image(lowerCamelCase_					)

    _lowercase			:				Optional[int] 						=						render_text(lowerCamelCase_				,					**lowerCamelCase_					)
    _lowercase			:				str 						=						max(header_image.width				,					image.width					)

    _lowercase			:				Optional[int] 						=						int(image.height * (new_width / image.width)					)
    _lowercase			:				Any 						=						int(header_image.height * (new_width / header_image.width)					)

    _lowercase			:				List[str] 						=						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			:				str 						=						to_numpy_array(lowerCamelCase_					)

    if infer_channel_dimension_format(lowerCamelCase_					) == ChannelDimension.LAST:
        _lowercase			:				int 						=						to_channel_dimension_format(lowerCamelCase_				,					ChannelDimension.LAST					)

    return new_image






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[Any]       					=     ["""flattened_patches"""]
       def __init__(   self, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = 20_48, lowerCamelCase = False, **lowerCamelCase, )			->  None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase			:				Optional[int] 						=						patch_size if patch_size is not None else {'height': 16, 'width': 16}
           _lowercase			:				Union[str, Any] 						=						do_normalize
           _lowercase			:				Optional[int] 						=						do_convert_rgb
           _lowercase			:				int 						=						max_patches
           _lowercase			:				List[str] 						=						is_vqa
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  np.ndarray:




           """simple docstring"""

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

           # convert to torch
           _lowercase			:				List[Any] 						=						to_channel_dimension_format(lowerCamelCase, ChannelDimension.FIRST)
           _lowercase			:				int 						=						torch.from_numpy(lowerCamelCase)

           _lowercase						,							_lowercase			:				Union[str, Any] 						=						patch_size['height'], patch_size['width']
           _lowercase						,							_lowercase			:				List[str] 						=						get_image_size(lowerCamelCase)

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

           _lowercase			:				Tuple 						=						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]
           _lowercase			:				Dict 						=						torch_extract_patches(lowerCamelCase, lowerCamelCase, lowerCamelCase)

           _lowercase			:				Tuple 						=						patches.shape
           _lowercase			:				List[str] 						=						patches_shape[1]
           _lowercase			:				Optional[Any] 						=						patches_shape[2]
           _lowercase			:				Any 						=						patches_shape[3]

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

           # [rows * columns, 1]
           _lowercase			:				str 						=						torch.arange(lowerCamelCase).reshape([rows, 1]).repeat(1, lowerCamelCase).reshape([rows * columns, 1])
           _lowercase			:				str 						=						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]
           _lowercase			:				Any 						=						row_ids.to(torch.floataa)
           _lowercase			:				List[str] 						=						col_ids.to(torch.floataa)

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

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

           _lowercase			:				Tuple 						=						to_numpy_array(lowerCamelCase)

           return result
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase)			->  np.ndarray:




           """simple docstring"""

           if image.dtype == np.uinta:
               _lowercase			:				Optional[int] 						=						image.astype(np.floataa)

           # take mean across the whole `image`
           _lowercase			:				Dict 						=						np.mean(lowerCamelCase)
           _lowercase			:				Dict 						=						np.std(lowerCamelCase)
           _lowercase			:				str 						=						max(lowerCamelCase, 1.0 / math.sqrt(np.prod(image.shape)))

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, )			->  ImageInput:




           """simple docstring"""

           _lowercase			:				List[str] 						=						do_normalize if do_normalize is not None else self.do_normalize
           _lowercase			:				Optional[Any] 						=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
           _lowercase			:				List[str] 						=						patch_size if patch_size is not None else self.patch_size
           _lowercase			:				Tuple 						=						max_patches if max_patches is not None else self.max_patches
           _lowercase			:				List[str] 						=						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 ')

           _lowercase			:				Optional[int] 						=						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:
               _lowercase			:				int 						=						[convert_to_rgb(lowerCamelCase) for image in images]

           # All transformations expect numpy arrays.
           _lowercase			:				Dict 						=						[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.')
               _lowercase			:				Optional[int] 						=						kwargs.pop('font_bytes', lowerCamelCase)
               _lowercase			:				Dict 						=						kwargs.pop('font_path', lowerCamelCase)

               if isinstance(lowerCamelCase, lowerCamelCase):
                   _lowercase			:				List[str] 						=						[header_text] * len(lowerCamelCase)

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

           if do_normalize:
               _lowercase			:				Any 						=						[self.normalize(image=lowerCamelCase) for image in images]

           # convert to torch tensor and permute
           _lowercase			:				Optional[Any] 						=						[
               self.extract_flattened_patches(image=lowerCamelCase, max_patches=lowerCamelCase, patch_size=lowerCamelCase)
               for image in images
           ]

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

           _lowercase			:				List[str] 						=						BatchFeature(
               data={'flattened_patches': images, 'attention_mask': attention_masks}, tensor_type=lowerCamelCase)

           return encoded_outputs






import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

SCREAMING_SNAKE_CASE			: int				   =					list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

SCREAMING_SNAKE_CASE			: Dict				   =					[file for file in filepaths if file != file.lower()]
if upper_files:
 print(F"{len(upper_files)} files contain uppercase characters:")
 print("\n".join(upper_files) + "\n")

SCREAMING_SNAKE_CASE			: List[Any]				   =					[file for file in filepaths if " " in file]
if space_files:
 print(F"{len(space_files)} files contain space characters:")
 print("\n".join(space_files) + "\n")

SCREAMING_SNAKE_CASE			: Any				   =					[file for file in filepaths if "-" in file]
if hyphen_files:
 print(F"{len(hyphen_files)} files contain hyphen characters:")
 print("\n".join(hyphen_files) + "\n")

SCREAMING_SNAKE_CASE			: str				   =					[file for file in filepaths if os.sep not in file]
if nodir_files:
 print(F"{len(nodir_files)} files are not in a directory:")
 print("\n".join(nodir_files) + "\n")

SCREAMING_SNAKE_CASE			: Tuple				   =					len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)


