




"""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:
 _A													=      False


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



def 	lowercase_				(			)						->    Any:
      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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      requires_backends(a_    ,		["""torch"""]				)
      _check_torch_version()

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

      lowerCAmelCase__      :			Union[str, Any]   =	textwrap.TextWrapper(width=80				)
      lowerCAmelCase__      :			Optional[int]   =	wrapper.wrap(text=a_				)
      lowerCAmelCase__      :			Optional[int]   =	"\n".join(a_				)

      if font_bytes is not None and font_path is None:
            lowerCAmelCase__      :			List[str]   =	io.BytesIO(a_				)
      elif font_path is not None:
            lowerCAmelCase__      :			str   =	font_path
      else:
            lowerCAmelCase__      :			Union[str, Any]   =	hf_hub_download(a_    ,		"""Arial.TTF"""				)
      lowerCAmelCase__      :			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.
      lowerCAmelCase__      :			Dict   =	ImageDraw.Draw(Image.new("""RGB"""    ,		(1, 1)    ,		a_				)				)
      lowerCAmelCase__      :			Union[str, Any]   =	temp_draw.textbbox((0, 0)    ,		a_    ,		a_				)

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		**__UpperCAmelCase				)						->    Dict:
      requires_backends(a_    ,		"""vision"""				)

      # Convert to PIL image if necessary
      lowerCAmelCase__      :			Optional[Any]   =	to_pil_image(a_				)

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

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

      lowerCAmelCase__      :			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__      :			Optional[Any]   =	to_numpy_array(a_				)

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

      return new_image


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[int]     				=     ['''flattened_patches''']






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Dict[str, int] = None   ,     UpperCamelCase       :		int = 20_48   ,     UpperCamelCase       :		bool = False   ,     **UpperCamelCase       :		Union[str, Any]   ,     )			->						None:

         """simple docstring"""
         super().__init__(**lowerCAmelCase__   )
         lowerCAmelCase__      :			str   =	patch_size if patch_size is not None else {"height": 16, "width": 16}
         lowerCAmelCase__      :			List[str]   =	do_normalize
         lowerCAmelCase__      :			List[str]   =	do_convert_rgb
         lowerCAmelCase__      :			List[Any]   =	max_patches
         lowerCAmelCase__      :			Any   =	is_vqa






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		dict   ,     **UpperCamelCase       :		Union[str, Any]   )			->						np.ndarray:

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

         # convert to torch
         lowerCAmelCase__      :			Dict   =	to_channel_dimension_format(lowerCAmelCase__   ,     ChannelDimension.FIRST   )
         lowerCAmelCase__      :			Any   =	torch.from_numpy(lowerCAmelCase__   )

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

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

         lowerCAmelCase__      :			Tuple   =	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]
         lowerCAmelCase__      :			Optional[Any]   =	torch_extract_patches(lowerCAmelCase__   ,     lowerCAmelCase__   ,     lowerCAmelCase__   )

         lowerCAmelCase__      :			int   =	patches.shape
         lowerCAmelCase__      :			Optional[Any]   =	patches_shape[1]
         lowerCAmelCase__      :			List[Any]   =	patches_shape[2]
         lowerCAmelCase__      :			Optional[int]   =	patches_shape[3]

         # [rows * columns, patch_height * patch_width * image_channels]
         lowerCAmelCase__      :			List[Any]   =	patches.reshape([rows * columns, depth]   )

         # [rows * columns, 1]
         lowerCAmelCase__      :			List[str]   =	torch.arange(lowerCAmelCase__   ).reshape([rows, 1]   ).repeat(1   ,     lowerCAmelCase__   ).reshape([rows * columns, 1]   )
         lowerCAmelCase__      :			List[str]   =	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]
         lowerCAmelCase__      :			str   =	row_ids.to(torch.floataa   )
         lowerCAmelCase__      :			Tuple   =	col_ids.to(torch.floataa   )

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

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

         lowerCAmelCase__      :			Optional[int]   =	to_numpy_array(lowerCAmelCase__   )

         return result






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		Dict   )			->						np.ndarray:

         """simple docstring"""




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

         # take mean across the whole `image`
         lowerCAmelCase__      :			Tuple   =	np.mean(lowerCAmelCase__   )
         lowerCAmelCase__      :			Optional[int]   =	np.std(lowerCAmelCase__   )
         lowerCAmelCase__      :			int   =	max(lowerCAmelCase__   ,     1.0 / math.sqrt(np.prod(image.shape   )   )   )

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






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		ImageInput   ,     UpperCamelCase       :		Optional[str] = None   ,     UpperCamelCase       :		bool = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[Dict[str, int]] = None   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     UpperCamelCase       :		ChannelDimension = ChannelDimension.FIRST   ,     **UpperCamelCase       :		Optional[Any]   ,     )			->						ImageInput:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__      :			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__      :			Optional[Any]   =	max_patches if max_patches is not None else self.max_patches
         lowerCAmelCase__      :			Tuple   =	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 """   )

         lowerCAmelCase__      :			Any   =	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:
               lowerCAmelCase__      :			Tuple   =	[convert_to_rgb(lowerCAmelCase__   ) for image in images]

         # All transformations expect numpy arrays.
         lowerCAmelCase__      :			List[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."""   )
               lowerCAmelCase__      :			List[Any]   =	kwargs.pop("""font_bytes"""   ,     lowerCAmelCase__   )
               lowerCAmelCase__      :			List[str]   =	kwargs.pop("""font_path"""   ,     lowerCAmelCase__   )

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

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

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

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

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

         lowerCAmelCase__      :			Optional[Any]   =	BatchFeature(
             data={"""flattened_patches""": images, """attention_mask""": attention_masks}   ,     tensor_type=lowerCAmelCase__   )

         return encoded_outputs











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





