





"""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:
							_UpperCamelCase					      =   False


_UpperCamelCase					      =   logging.get_logger(__name__)
_UpperCamelCase					      =   'ybelkada/fonts'



def 					lowerCAmelCase_      (   ):


							'''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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[int]     ,    SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				Optional[int] ):


							'''simple docstring'''







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

							__lowerCamelCase   :   int       =image_tensor.unsqueeze(0 )
							__lowerCamelCase   :   Dict       =torch.nn.functional.unfold(SCREAMING_SNAKE_CASE     ,    (patch_height, patch_width)     ,    stride=(patch_height, patch_width) )
							__lowerCamelCase   :   int       =patches.reshape(image_tensor.size(0 )     ,    image_tensor.size(1 )     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    -1 )
							__lowerCamelCase   :   List[Any]       =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_      (   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     ,    ):


							'''simple docstring'''







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

							__lowerCamelCase   :   Dict       =textwrap.TextWrapper(width=80 )
							__lowerCamelCase   :   Tuple       =wrapper.wrap(text=SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Dict       ='''\n'''.join(SCREAMING_SNAKE_CASE )

							if font_bytes is not None and font_path is None:
														__lowerCamelCase   :   List[str]       =io.BytesIO(SCREAMING_SNAKE_CASE )
							elif font_path is not None:
														__lowerCamelCase   :   str       =font_path
							else:
														__lowerCamelCase   :   str       =hf_hub_download(SCREAMING_SNAKE_CASE     ,    '''Arial.TTF''' )
							__lowerCamelCase   :   List[Any]       =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.
							__lowerCamelCase   :   Optional[int]       =ImageDraw.Draw(Image.new('''RGB'''     ,    (1, 1)     ,    SCREAMING_SNAKE_CASE ) )
							__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =temp_draw.textbbox((0, 0)     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							# Create the actual image with a bit of padding around the text.
							__lowerCamelCase   :   Dict       =text_width + left_padding + right_padding
							__lowerCamelCase   :   str       =text_height + top_padding + bottom_padding
							__lowerCamelCase   :   Any       =Image.new('''RGB'''     ,    (image_width, image_height)     ,    SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   List[str]       =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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				np.ndarray     ,    SCREAMING_SNAKE_CASE							:				str     ,    **SCREAMING_SNAKE_CASE							:				Dict ):


							'''simple docstring'''







							requires_backends(SCREAMING_SNAKE_CASE     ,    '''vision''' )

							# Convert to PIL image if necessary
							__lowerCamelCase   :   List[Any]       =to_pil_image(SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Any       =render_text(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   List[Any]       =max(header_image.width     ,    image.width )

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

							__lowerCamelCase   :   Union[str, 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
							__lowerCamelCase   :   Dict       =to_numpy_array(SCREAMING_SNAKE_CASE )

							if infer_channel_dimension_format(SCREAMING_SNAKE_CASE ) == ChannelDimension.LAST:
														__lowerCamelCase   :   Optional[Any]       =to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,    ChannelDimension.LAST )

							return new_image
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Union[str, Any]									=		["""flattened_patches"""]




				def __init__(  self	:Dict    ,     __lowercase	:bool = True    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:int = 2048    ,     __lowercase	:bool = False    ,     **__lowercase	:Dict    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   Union[str, Any]       =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
											__lowerCamelCase   :   Tuple       =do_normalize
											__lowerCamelCase   :   List[str]       =do_convert_rgb
											__lowerCamelCase   :   int       =max_patches
											__lowerCamelCase   :   str       =is_vqa




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:int    ,     __lowercase	:dict    ,     **__lowercase	:Any      ):
											requires_backends(self.extract_flattened_patches    ,     '''torch'''      )
											_check_torch_version()

											# convert to torch
											__lowerCamelCase   :   List[Any]       =to_channel_dimension_format(__lowercase    ,     ChannelDimension.FIRST      )
											__lowerCamelCase   :   Dict       =torch.from_numpy(__lowercase      )

											__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =patch_size['''height'''], patch_size['''width''']
											__lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =get_image_size(__lowercase      )

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

											__lowerCamelCase   :   Optional[int]       =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]
											__lowerCamelCase   :   Dict       =torch_extract_patches(__lowercase    ,     __lowercase    ,     __lowercase      )

											__lowerCamelCase   :   Any       =patches.shape
											__lowerCamelCase   :   List[Any]       =patches_shape[1]
											__lowerCamelCase   :   Tuple       =patches_shape[2]
											__lowerCamelCase   :   Tuple       =patches_shape[3]

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

											# [rows * columns, 1]
											__lowerCamelCase   :   Any       =torch.arange(__lowercase      ).reshape([rows, 1]      ).repeat(1    ,     __lowercase      ).reshape([rows * columns, 1]      )
											__lowerCamelCase   :   int       =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]
											__lowerCamelCase   :   Optional[Any]       =row_ids.to(torch.floataa      )
											__lowerCamelCase   :   Dict       =col_ids.to(torch.floataa      )

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

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

											__lowerCamelCase   :   Any       =to_numpy_array(__lowercase      )

											return result




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str      ):
											if image.dtype == np.uinta:
																		__lowerCamelCase   :   List[str]       =image.astype(np.floataa      )

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

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




				def        __lowercase	(  self	:Union[str, 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[Any]    ,     ):
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   List[str]       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
											__lowerCamelCase   :   Optional[Any]       =patch_size if patch_size is not None else self.patch_size
											__lowerCamelCase   :   int       =max_patches if max_patches is not None else self.max_patches
											__lowerCamelCase   :   Optional[int]       =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 '''      )

											__lowerCamelCase   :   List[str]       =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:
																		__lowerCamelCase   :   Dict       =[convert_to_rgb(__lowercase      ) for image in images]

											# All transformations expect numpy arrays.
											__lowerCamelCase   :   Tuple       =[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.'''      )
																		__lowerCamelCase   :   Union[str, Any]       =kwargs.pop('''font_bytes'''    ,     __lowercase      )
																		__lowerCamelCase   :   Any       =kwargs.pop('''font_path'''    ,     __lowercase      )

																		if isinstance(__lowercase    ,     __lowercase      ):
																									__lowerCamelCase   :   Union[str, Any]       =[header_text] * len(__lowercase      )

																		__lowerCamelCase   :   Optional[Any]       =[
																		    render_header(__lowercase    ,     header_text[i]    ,     font_bytes=__lowercase    ,     font_path=__lowercase      )
																		    for i, image in enumerate(__lowercase      )
																		]

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

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

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

											__lowerCamelCase   :   List[Any]       =BatchFeature(
											    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}    ,     tensor_type=__lowercase      )

											return encoded_outputs











"""simple docstring"""





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
							import accelerate



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if not is_accelerate_available():
														return method
							__lowerCamelCase   :   Dict       =version.parse(accelerate.__version__ ).base_version
							if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('''0.17.0''' ):
														return method

							def wrapper(self							:				List[Any]     ,    *SCREAMING_SNAKE_CASE							:				Any     ,    **SCREAMING_SNAKE_CASE							:				List[str] ):
														if hasattr(self     ,    '''_hf_hook''' ) and hasattr(self._hf_hook     ,    '''pre_forward''' ):
																					self._hf_hook.pre_forward(self )
														return method(self     ,    *SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE )

							return wrapper




