

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:
				_a:    Dict 							=			False


_a:    Union[str, Any] 							=			logging.get_logger(__name__)
_a:    Tuple 							=			"""ybelkada/fonts"""



def       __lowerCAmelCase      (						):
	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      (						A       ,     A       ,     A    ):
	requires_backends(A       ,     ["torch"]    )
	_check_torch_version()

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

	UpperCAmelCase_       				=			textwrap.TextWrapper(width=80    )
	UpperCAmelCase_       				=			wrapper.wrap(text=A    )
	UpperCAmelCase_       				=			"\n".join(A    )

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

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

	# Create the actual image with a bit of padding around the text.
	UpperCAmelCase_       				=			text_width + left_padding + right_padding
	UpperCAmelCase_       				=			text_height + top_padding + bottom_padding
	UpperCAmelCase_       				=			Image.new("RGB"       ,     (image_width, image_height)       ,     A    )
	UpperCAmelCase_       				=			ImageDraw.Draw(A    )
	draw.text(xy=(left_padding, top_padding)       ,     text=A       ,     fill=A       ,     font=A    )
	return image



def       __lowerCAmelCase      (						A       ,     A       ,     **A    ):
	requires_backends(A       ,     "vision"    )

	# Convert to PIL image if necessary
	UpperCAmelCase_       				=			to_pil_image(A    )

	UpperCAmelCase_       				=			render_text(A       ,     **A    )
	UpperCAmelCase_       				=			max(header_image.width       ,     image.width    )

	UpperCAmelCase_       				=			int(image.height * (new_width / image.width)    )
	UpperCAmelCase_       				=			int(header_image.height * (new_width / header_image.width)    )

	UpperCAmelCase_       				=			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
	UpperCAmelCase_       				=			to_numpy_array(A    )

	if infer_channel_dimension_format(A    ) == ChannelDimension.LAST:
		UpperCAmelCase_       				=			to_channel_dimension_format(A       ,     ChannelDimension.LAST    )

	return new_image




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['flattened_patches']




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


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)
		UpperCAmelCase_       				=			patch_size if patch_size is not None else {"height": 16, "width": 16}
		UpperCAmelCase_       				=			do_normalize
		UpperCAmelCase_       				=			do_convert_rgb
		UpperCAmelCase_       				=			max_patches
		UpperCAmelCase_       				=			is_vqa




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						dict		,	**lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







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

		# convert to torch
		UpperCAmelCase_       				=			to_channel_dimension_format(lowerCAmelCase		,	ChannelDimension.FIRST							)
		UpperCAmelCase_       				=			torch.from_numpy(lowerCAmelCase							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			patch_size["height"], patch_size["width"]
		UpperCAmelCase_				,   UpperCAmelCase_       				=			get_image_size(lowerCAmelCase							)

		# maximize scale s.t.
		UpperCAmelCase_       				=			math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)							)
		UpperCAmelCase_       				=			max(min(math.floor(scale * image_height / patch_height							)		,	lowerCAmelCase							)		,	1							)
		UpperCAmelCase_       				=			max(min(math.floor(scale * image_width / patch_width							)		,	lowerCAmelCase							)		,	1							)
		UpperCAmelCase_       				=			max(num_feasible_rows * patch_height		,	1							)
		UpperCAmelCase_       				=			max(num_feasible_cols * patch_width		,	1							)

		UpperCAmelCase_       				=			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]
		UpperCAmelCase_       				=			torch_extract_patches(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			patches.shape
		UpperCAmelCase_       				=			patches_shape[1]
		UpperCAmelCase_       				=			patches_shape[2]
		UpperCAmelCase_       				=			patches_shape[3]

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

		# [rows * columns, 1]
		UpperCAmelCase_       				=			torch.arange(lowerCAmelCase							).reshape([rows, 1]							).repeat(1		,	lowerCAmelCase							).reshape([rows * columns, 1]							)
		UpperCAmelCase_       				=			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]
		UpperCAmelCase_       				=			row_ids.to(torch.floataa							)
		UpperCAmelCase_       				=			col_ids.to(torch.floataa							)

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

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

		UpperCAmelCase_       				=			to_numpy_array(lowerCAmelCase							)

		return result




	def       __A					(		self	:						int		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







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

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

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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			do_normalize if do_normalize is not None else self.do_normalize
		UpperCAmelCase_       				=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
		UpperCAmelCase_       				=			patch_size if patch_size is not None else self.patch_size
		UpperCAmelCase_       				=			max_patches if max_patches is not None else self.max_patches
		UpperCAmelCase_       				=			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 "							)

		UpperCAmelCase_       				=			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:
			UpperCAmelCase_       				=			[convert_to_rgb(lowerCAmelCase							) for image in images]

		# All transformations expect numpy arrays.
		UpperCAmelCase_       				=			[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."							)
			UpperCAmelCase_       				=			kwargs.pop("font_bytes"		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			kwargs.pop("font_path"		,	lowerCAmelCase							)

			if isinstance(lowerCAmelCase		,	lowerCAmelCase							):
				UpperCAmelCase_       				=			[header_text] * len(lowerCAmelCase							)

			UpperCAmelCase_       				=			[
			    render_header(lowerCAmelCase		,	header_text[i]		,	font_bytes=lowerCAmelCase		,	font_path=lowerCAmelCase							)
			    for i, image in enumerate(lowerCAmelCase							)
			]

		if do_normalize:
			UpperCAmelCase_       				=			[self.normalize(image=lowerCAmelCase							) for image in images]

		# convert to torch tensor and permute
		UpperCAmelCase_       				=			[
		    self.extract_flattened_patches(image=lowerCAmelCase		,	max_patches=lowerCAmelCase		,	patch_size=lowerCAmelCase							)
		    for image in images
		]

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

		UpperCAmelCase_       				=			BatchFeature(
		    data={"flattened_patches": images, "attention_mask": attention_masks}		,	tensor_type=lowerCAmelCase							)

		return encoded_outputs


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


_a:    List[str] 							=			logging.get_logger(__name__)

_a:    Any 							=			{
    """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 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'vit_msn'




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]=768		,	lowerCAmelCase	:						Dict=12		,	lowerCAmelCase	:						str=12		,	lowerCAmelCase	:						List[str]=3_072		,	lowerCAmelCase	:						Tuple="gelu"		,	lowerCAmelCase	:						int=0.0		,	lowerCAmelCase	:						Optional[int]=0.0		,	lowerCAmelCase	:						Union[str, Any]=0.02		,	lowerCAmelCase	:						str=1e-06		,	lowerCAmelCase	:						Dict=224		,	lowerCAmelCase	:						str=16		,	lowerCAmelCase	:						List[Any]=3		,	lowerCAmelCase	:						Union[str, Any]=True		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)

		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			image_size
		UpperCAmelCase_       				=			patch_size
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			qkv_bias