



"""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:
							lowercase__			   =  False


lowercase__			   =  logging.get_logger(__name__)
lowercase__			   =  "ybelkada/fonts"





def       __magic_name__    (   ):
							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       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: Optional[int]					):
							requires_backends(snake_case__  ,       ["""torch"""]					)
							_check_torch_version()

							__a		:		Optional[int]									=       image_tensor.unsqueeze(0					)
							__a		:		Optional[Any]									=       torch.nn.functional.unfold(snake_case__  ,       (patch_height, patch_width)  ,       stride=(patch_height, patch_width)					)
							__a		:		List[str]									=       patches.reshape(image_tensor.size(0					)  ,       image_tensor.size(1					)  ,       snake_case__  ,       snake_case__  ,       -1					)
							__a		:		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       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: int = 3_6  ,       _lowerCamelCase				: str = "black"  ,       _lowerCamelCase				: str = "white"  ,       _lowerCamelCase				: int = 5  ,       _lowerCamelCase				: int = 5  ,       _lowerCamelCase				: int = 5  ,       _lowerCamelCase				: int = 5  ,       _lowerCamelCase				: Optional[bytes] = None  ,       _lowerCamelCase				: Optional[str] = None  ,       ):
							requires_backends(snake_case__  ,       """vision"""					)
							# Add new lines so that each line is no more than 80 characters.

							__a		:		Any									=       textwrap.TextWrapper(width=8_0					)
							__a		:		Union[str, Any]									=       wrapper.wrap(text=snake_case__					)
							__a		:		Tuple									=       """\n""".join(snake_case__					)

							if font_bytes is not None and font_path is None:
														__a		:		List[Any]									=       io.BytesIO(snake_case__					)
							elif font_path is not None:
														__a		:		Any									=       font_path
							else:
														__a		:		int									=       hf_hub_download(snake_case__  ,       """Arial.TTF"""					)
							__a		:		Optional[Any]									=       ImageFont.truetype(snake_case__  ,       encoding="""UTF-8"""  ,       size=snake_case__					)

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

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





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: str  ,       **_lowerCamelCase				: List[str]					):
							requires_backends(snake_case__  ,       """vision"""					)

							# Convert to PIL image if necessary
							__a		:		str									=       to_pil_image(snake_case__					)

							__a		:		Optional[Any]									=       render_text(snake_case__  ,       **snake_case__					)
							__a		:		List[str]									=       max(header_image.width  ,       image.width					)

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

							__a		:		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
							__a		:		Union[str, Any]									=       to_numpy_array(snake_case__					)

							if infer_channel_dimension_format(snake_case__					) == ChannelDimension.LAST:
														__a		:		Union[str, Any]									=       to_channel_dimension_format(snake_case__  ,       ChannelDimension.LAST					)

							return new_image







class SCREAMING_SNAKE_CASE__					(		__lowerCAmelCase      ):
						_lowerCAmelCase							=					['''flattened_patches''']



						def __init__(self						,     _lowercase = True						,     _lowercase = True						,     _lowercase = None						,     _lowercase = 2048						,     _lowercase = False						,     **_lowercase						,     ):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

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

													# convert to torch
													__a		:		int									=       to_channel_dimension_format(lowerCAmelCase_						,     ChannelDimension.FIRST							)
													__a		:		Union[str, Any]									=       torch.from_numpy(lowerCAmelCase_							)

													__a							,     __a		:		Optional[int]									=       patch_size["""height"""], patch_size["""width"""]
													__a							,     __a		:		str									=       get_image_size(lowerCAmelCase_							)

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

													__a		:		Optional[Any]									=       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]
													__a		:		Tuple									=       torch_extract_patches(lowerCAmelCase_						,     lowerCAmelCase_						,     lowerCAmelCase_							)

													__a		:		Tuple									=       patches.shape
													__a		:		Tuple									=       patches_shape[1]
													__a		:		Union[str, Any]									=       patches_shape[2]
													__a		:		Optional[int]									=       patches_shape[3]

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

													# [rows * columns, 1]
													__a		:		Dict									=       torch.arange(lowerCAmelCase_							).reshape([rows, 1]							).repeat(1						,     lowerCAmelCase_							).reshape([rows * columns, 1]							)
													__a		:		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]
													__a		:		Union[str, Any]									=       row_ids.to(torch.floataa							)
													__a		:		Union[str, Any]									=       col_ids.to(torch.floataa							)

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

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

													__a		:		Optional[int]									=       to_numpy_array(lowerCAmelCase_							)

													return result



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     **_lowercase							):





													'''simple docstring'''

													if image.dtype == np.uinta:
																				__a		:		List[str]									=       image.astype(np.floataa							)

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

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





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None						,     _lowercase = None						,     _lowercase = None						,     _lowercase = None						,     _lowercase = None						,     _lowercase = ChannelDimension.FIRST						,     **_lowercase						,     ):





													'''simple docstring'''

													__a		:		Any									=       do_normalize if do_normalize is not None else self.do_normalize
													__a		:		str									=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
													__a		:		str									=       patch_size if patch_size is not None else self.patch_size
													__a		:		str									=       max_patches if max_patches is not None else self.max_patches
													__a		:		Dict									=       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 """							)

													__a		:		Dict									=       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:
																				__a		:		Optional[int]									=       [convert_to_rgb(lowerCAmelCase_							) for image in images]

													# All transformations expect numpy arrays.
													__a		:		Optional[Any]									=       [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."""							)
																				__a		:		Tuple									=       kwargs.pop("""font_bytes"""						,     lowerCAmelCase_							)
																				__a		:		int									=       kwargs.pop("""font_path"""						,     lowerCAmelCase_							)

																				if isinstance(lowerCAmelCase_						,     lowerCAmelCase_							):
																											__a		:		Optional[int]									=       [header_text] * len(lowerCAmelCase_							)

																				__a		:		List[Any]									=       [
																				    render_header(lowerCAmelCase_						,     header_text[i]						,     font_bytes=lowerCAmelCase_						,     font_path=lowerCAmelCase_							)
																				    for i, image in enumerate(lowerCAmelCase_							)
																				]

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

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

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

													__a		:		Any									=       BatchFeature(
													    data={"""flattened_patches""": images, """attention_mask""": attention_masks}						,     tensor_type=lowerCAmelCase_							)

													return encoded_outputs






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

