


'''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:
		__lowerCAmelCase       		=      False


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





def 		_UpperCAmelCase   (							):
				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   (							__A							:   Any						,		__A							:   Union[str, Any]						,		__A							:   int      ):
				requires_backends(__A						,		['''torch''']      )
				_check_torch_version()

				a_		:  Union[str, Any]											=      image_tensor.unsqueeze(0      )
				a_		:  List[str]											=      torch.nn.functional.unfold(__A						,		(patch_height, patch_width)						,		stride=(patch_height, patch_width)      )
				a_		:  List[str]											=      patches.reshape(image_tensor.size(0      )						,		image_tensor.size(1      )						,		__A						,		__A						,		-1      )
				a_		:  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 		_UpperCAmelCase   (							__A							:   str						,		__A							:   int = 36						,		__A							:   str = "black"						,		__A							:   str = "white"						,		__A							:   int = 5						,		__A							:   int = 5						,		__A							:   int = 5						,		__A							:   int = 5						,		__A							:   Optional[bytes] = None						,		__A							:   Optional[str] = None						,		):
				requires_backends(__A						,		'''vision'''      )
				# Add new lines so that each line is no more than 80 characters.

				a_		:  List[Any]											=      textwrap.TextWrapper(width=80      )
				a_		:  int											=      wrapper.wrap(text=__A      )
				a_		:  Optional[Any]											=      '''\n'''.join(__A      )

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

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

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





def 		_UpperCAmelCase   (							__A							:   np.ndarray						,		__A							:   str						,		**__A							:   int      ):
				requires_backends(__A						,		'''vision'''      )

				# Convert to PIL image if necessary
				a_		:  Dict											=      to_pil_image(__A      )

				a_		:  int											=      render_text(__A						,		**__A      )
				a_		:  List[Any]											=      max(header_image.width						,		image.width      )

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

				a_		:  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
				a_		:  Optional[int]											=      to_numpy_array(__A      )

				if infer_channel_dimension_format(__A      ) == ChannelDimension.LAST:
								a_		:  Optional[Any]											=      to_channel_dimension_format(__A						,		ChannelDimension.LAST      )

				return new_image





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				["flattened_patches"]






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			dict  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			np.ndarray:
							requires_backends(self.extract_flattened_patches  ,						'''torch'''					)
							_check_torch_version()

							# convert to torch
							a_		:  str											=      to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,						ChannelDimension.FIRST					)
							a_		:  Any											=      torch.from_numpy(__SCREAMING_SNAKE_CASE					)

							a_		:  int											=      patch_size['''height'''], patch_size['''width''']
							a_		:  Dict											=      get_image_size(__SCREAMING_SNAKE_CASE					)

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

							a_		:  int											=      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_		:  Optional[int]											=      torch_extract_patches(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  int											=      patches.shape
							a_		:  List[Any]											=      patches_shape[1]
							a_		:  str											=      patches_shape[2]
							a_		:  Any											=      patches_shape[3]

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

							# [rows * columns, 1]
							a_		:  List[Any]											=      torch.arange(__SCREAMING_SNAKE_CASE					).reshape([rows, 1]					).repeat(1  ,						__SCREAMING_SNAKE_CASE					).reshape([rows * columns, 1]					)
							a_		:  List[Any]											=      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_		:  Union[str, Any]											=      row_ids.to(torch.floataa					)
							a_		:  Optional[int]											=      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(__SCREAMING_SNAKE_CASE  ,						[0, 0, 0, max_patches - (rows * columns)]					).float()

							a_		:  Tuple											=      to_numpy_array(__SCREAMING_SNAKE_CASE					)

							return result






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, ChannelDimension]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			np.ndarray:
							if image.dtype == np.uinta:
											a_		:  Dict											=      image.astype(np.floataa					)

							# take mean across the whole `image`
							a_		:  Any											=      np.mean(__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      np.std(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__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      :			List[str]  ,						)			->			ImageInput:
							a_		:  Union[str, Any]											=      do_normalize if do_normalize is not None else self.do_normalize
							a_		:  Optional[int]											=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							a_		:  Dict											=      patch_size if patch_size is not None else self.patch_size
							a_		:  Union[str, Any]											=      max_patches if max_patches is not None else self.max_patches
							a_		:  Union[str, Any]											=      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_		:  int											=      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_		:  Optional[Any]											=      [convert_to_rgb(__SCREAMING_SNAKE_CASE					) for image in images]

							# All transformations expect numpy arrays.
							a_		:  str											=      [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_		:  int											=      kwargs.pop('''font_bytes'''  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      kwargs.pop('''font_path'''  ,						__SCREAMING_SNAKE_CASE					)

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

											a_		:  int											=      [
											    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_		:  int											=      [self.normalize(image=__SCREAMING_SNAKE_CASE					) for image in images]

							# convert to torch tensor and permute
							a_		:  Tuple											=      [
							    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_		:  int											=      [(image.sum(axis=-1					) != 0).astype(np.floataa					) for image in images]

							a_		:  Dict											=      BatchFeature(
							    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}  ,						tensor_type=__SCREAMING_SNAKE_CASE					)

							return encoded_outputs












'''simple docstring'''







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




