



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:
 __snake_case						:Any      		=     False


__snake_case						:List[str]      		=     logging.get_logger(__name__)
__snake_case						:int      		=     '''ybelkada/fonts'''





def      __snake_case	(			):
    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	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    requires_backends(_UpperCAmelCase   , ['''torch''']				)
    _check_torch_version()

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

    __a						       =				textwrap.TextWrapper(width=80				)
    __a						       =				wrapper.wrap(text=_UpperCAmelCase				)
    __a						       =				'''\n'''.join(_UpperCAmelCase				)

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

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

    # 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)   , _UpperCAmelCase				)
    __a						       =				ImageDraw.Draw(_UpperCAmelCase				)
    draw.text(xy=(left_padding, top_padding)   , text=_UpperCAmelCase   , fill=_UpperCAmelCase   , font=_UpperCAmelCase				)
    return image





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , **_UpperCAmelCase				):
    requires_backends(_UpperCAmelCase   , '''vision'''				)

    # Convert to PIL image if necessary
    __a						       =				to_pil_image(_UpperCAmelCase				)

    __a						       =				render_text(_UpperCAmelCase   , **_UpperCAmelCase				)
    __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(_UpperCAmelCase				)

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

    return new_image

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    List[str]											=						['''flattened_patches''']



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, int] = None     ,		__SCREAMING_SNAKE_CASE							:     int = 2_048     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)
           __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 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     dict     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

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

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

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

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

           # [1, rows, columns, patch_height * patch_width * image_channels]
           __a						       =				torch_extract_patches(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __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(__SCREAMING_SNAKE_CASE).reshape([rows, 1]).repeat(1     ,		__SCREAMING_SNAKE_CASE).reshape([rows * columns, 1])
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE).reshape([1, columns]).repeat(__SCREAMING_SNAKE_CASE     ,		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(__SCREAMING_SNAKE_CASE     ,		[0, 0, 0, max_patches - (rows * columns)]).float()

           __a						       =				to_numpy_array(__SCREAMING_SNAKE_CASE)

           return result



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, ChannelDimension]] = None     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

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

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

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





       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     ImageInput     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = None     ,		__SCREAMING_SNAKE_CASE							:     bool = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[bool] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dict[str, int]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, TensorType]] = None     ,		__SCREAMING_SNAKE_CASE							:     ChannelDimension = ChannelDimension.FIRST     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		):


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

           __a						       =				make_list_of_images(__SCREAMING_SNAKE_CASE)

           if not valid_images(__SCREAMING_SNAKE_CASE):
               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(__SCREAMING_SNAKE_CASE) for image in images]

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

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

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

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

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

           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

__snake_case						:Optional[Any]      		=     list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__snake_case						:Any      		=     [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''')

__snake_case						:Tuple      		=     [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''')

__snake_case						:Optional[int]      		=     [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''')

__snake_case						:Optional[int]      		=     [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''')

__snake_case						:int      		=     len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)





