




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:
       SCREAMING_SNAKE_CASE__			: str    =  False


SCREAMING_SNAKE_CASE__			: Union[str, Any]    =  logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__			: Optional[Any]    =  'ybelkada/fonts'

def 	A ( )      ->					int:





       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 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE       )      ->					Optional[int]:
       requires_backends(UpperCAmelCase_					,["torch"]       )
       _check_torch_version()

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

       lowerCamelCase  : Any     					=   textwrap.TextWrapper(width=80       )
       lowerCamelCase  : List[str]     					=   wrapper.wrap(text=UpperCAmelCase_       )
       lowerCamelCase  : List[Any]     					=   "\n".join(UpperCAmelCase_       )

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

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

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

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,**_SCREAMING_SNAKE_CASE       )      ->					Optional[Any]:
       requires_backends(UpperCAmelCase_					,"vision"       )

       # Convert to PIL image if necessary
       lowerCamelCase  : Tuple     					=   to_pil_image(UpperCAmelCase_       )

       lowerCamelCase  : Optional[Any]     					=   render_text(UpperCAmelCase_					,**UpperCAmelCase_       )
       lowerCamelCase  : Optional[Any]     					=   max(header_image.width					,image.width       )

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

       lowerCamelCase  : Tuple     					=   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  : Union[str, Any]     					=   to_numpy_array(UpperCAmelCase_       )

       if infer_channel_dimension_format(UpperCAmelCase_       ) == ChannelDimension.LAST:
              lowerCamelCase  : List[str]     					=   to_channel_dimension_format(UpperCAmelCase_					,ChannelDimension.LAST       )

       return new_image




class 						UpperCamelCase__      (_lowerCAmelCase	):

    '''simple docstring'''



    lowerCamelCase_ :      int      	= ["flattened_patches"]







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







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    **UpperCamelCase__	)	-> np.ndarray:
           requires_backends(self.extract_flattened_patches	,    "torch"	)
           _check_torch_version()

           # convert to torch
           lowerCamelCase  : List[Any]     					=   to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,    ChannelDimension.FIRST	)
           lowerCamelCase  : Any     					=   torch.from_numpy(SCREAMING_SNAKE_CASE__	)

           lowerCamelCase					, lowerCamelCase  : Union[str, Any]     					=   patch_size["height"], patch_size["width"]
           lowerCamelCase					, lowerCamelCase  : Optional[Any]     					=   get_image_size(SCREAMING_SNAKE_CASE__	)

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

           lowerCamelCase  : Optional[Any]     					=   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]
           lowerCamelCase  : Tuple     					=   torch_extract_patches(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	)

           lowerCamelCase  : Optional[int]     					=   patches.shape
           lowerCamelCase  : List[str]     					=   patches_shape[1]
           lowerCamelCase  : int     					=   patches_shape[2]
           lowerCamelCase  : int     					=   patches_shape[3]

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

           # [rows * columns, 1]
           lowerCamelCase  : Union[str, Any]     					=   torch.arange(SCREAMING_SNAKE_CASE__	).reshape([rows, 1]	).repeat(1	,    SCREAMING_SNAKE_CASE__	).reshape([rows * columns, 1]	)
           lowerCamelCase  : List[str]     					=   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]
           lowerCamelCase  : int     					=   row_ids.to(torch.floataa	)
           lowerCamelCase  : Optional[Any]     					=   col_ids.to(torch.floataa	)

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

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

           lowerCamelCase  : Tuple     					=   to_numpy_array(SCREAMING_SNAKE_CASE__	)

           return result







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__ = None	,    **UpperCamelCase__	)	-> np.ndarray:
           if image.dtype == np.uinta:
                  lowerCamelCase  : int     					=   image.astype(np.floataa	)

           # take mean across the whole `image`
           lowerCamelCase  : Optional[Any]     					=   np.mean(SCREAMING_SNAKE_CASE__	)
           lowerCamelCase  : Dict     					=   np.std(SCREAMING_SNAKE_CASE__	)
           lowerCamelCase  : Optional[Any]     					=   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        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = ChannelDimension.FIRST	,    **UpperCamelCase__	,    )	-> ImageInput:
           lowerCamelCase  : Any     					=   do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase  : Optional[Any]     					=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
           lowerCamelCase  : List[Any]     					=   patch_size if patch_size is not None else self.patch_size
           lowerCamelCase  : List[Any]     					=   max_patches if max_patches is not None else self.max_patches
           lowerCamelCase  : 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 "	)

           lowerCamelCase  : Optional[Any]     					=   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:
                  lowerCamelCase  : Union[str, Any]     					=   [convert_to_rgb(SCREAMING_SNAKE_CASE__	) for image in images]

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

                  if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	):
                         lowerCamelCase  : List[str]     					=   [header_text] * len(SCREAMING_SNAKE_CASE__	)

                  lowerCamelCase  : List[str]     					=   [
                      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:
                  lowerCamelCase  : Optional[Any]     					=   [self.normalize(image=SCREAMING_SNAKE_CASE__	) for image in images]

           # convert to torch tensor and permute
           lowerCamelCase  : List[str]     					=   [
               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
           lowerCamelCase  : str     					=   [(image.sum(axis=-1	) != 0).astype(np.floataa	) for image in images]

           lowerCamelCase  : List[str]     					=   BatchFeature(
               data={"flattened_patches": images, "attention_mask": attention_masks}	,    tensor_type=SCREAMING_SNAKE_CASE__	)

           return encoded_outputs











"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[Any]     )	-> Dict:






	"""simple docstring"""





	A__											=					[]
	A__											=					set({"(", "[", "{"}     )
	A__											=					set({")", "]", "}"}     )
	A__											=					{"{": "}", "[": "]", "(": ")"}

	for i in range(len(UpperCAmelCase_     )     ):
		if s[i] in open_brackets:
			stack.append(s[i]     )

		elif s[i] in closed_brackets and (
		    len(UpperCAmelCase_     ) == 0 or (len(UpperCAmelCase_     ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(UpperCAmelCase_     ) == 0

def       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					input("Enter sequence of brackets: "     )
	if is_balanced(UpperCAmelCase_     ):
		print(UpperCAmelCase_,	"is balanced"     )
	else:
		print(UpperCAmelCase_,	"is not balanced"     )


if __name__ == "__main__":
			main()




