


'''simple docstring'''







from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Optional[int]												=["""pixel_values"""]

  def __init__(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Optional[Dict[str, int]] = None				,							SCREAMING_SNAKE_CASE__     :							PILImageResampling = PILImageResampling.BILINEAR				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Union[int, float] = 1 / 2_5_5				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[float, List[float]]] = None				,							**SCREAMING_SNAKE_CASE__     :							Tuple				,							):





       '''simple docstring'''

       super().__init__(**SCREAMING_SNAKE_CASE__						)
       __a       =   size if size is not None else {"""shortest_edge""": 2_5_6}
       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__				,							default_to_square=SCREAMING_SNAKE_CASE__						)
       __a       =   crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__				,							param_name="""crop_size"""						)
       __a       =   do_resize
       __a       =   size
       __a       =   resample
       __a       =   do_center_crop
       __a       =   crop_size
       __a       =   do_rescale
       __a       =   rescale_factor
       __a       =   do_normalize
       __a       =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __a       =   image_std if image_std is not None else IMAGENET_STANDARD_STD

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int]				,							SCREAMING_SNAKE_CASE__     :							PILImageResampling = PILImageResampling.BICUBIC				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE__     :							Dict				,							):





       '''simple docstring'''

       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__				,							default_to_square=SCREAMING_SNAKE_CASE__						)
       if "shortest_edge" not in size:
            raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
       __a       =   get_resize_output_image_size(SCREAMING_SNAKE_CASE__				,							size=size["""shortest_edge"""]				,							default_to_square=SCREAMING_SNAKE_CASE__						)
       return resize(SCREAMING_SNAKE_CASE__				,							size=SCREAMING_SNAKE_CASE__				,							resample=SCREAMING_SNAKE_CASE__				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int]				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





       '''simple docstring'''

       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__						)
       if "height" not in size or "width" not in size:
            raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''						)
       return center_crop(SCREAMING_SNAKE_CASE__				,							size=(size["""height"""], size["""width"""])				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							float				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       return rescale(SCREAMING_SNAKE_CASE__				,							scale=SCREAMING_SNAKE_CASE__				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							Union[float, List[float]]				,							SCREAMING_SNAKE_CASE__     :							Union[float, List[float]]				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





       '''simple docstring'''

       return normalize(SCREAMING_SNAKE_CASE__				,							mean=SCREAMING_SNAKE_CASE__				,							std=SCREAMING_SNAKE_CASE__				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							ImageInput				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE__     :							PILImageResampling = None				,							SCREAMING_SNAKE_CASE__     :							bool = None				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[float] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, TensorType]] = None				,							SCREAMING_SNAKE_CASE__     :							Union[str, ChannelDimension] = ChannelDimension.FIRST				,							**SCREAMING_SNAKE_CASE__     :							str				,							):





       '''simple docstring'''

       __a       =   do_resize if do_resize is not None else self.do_resize
       __a       =   size if size is not None else self.size
       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__				,							default_to_square=SCREAMING_SNAKE_CASE__						)
       __a       =   resample if resample is not None else self.resample
       __a       =   do_center_crop if do_center_crop is not None else self.do_center_crop
       __a       =   crop_size if crop_size is not None else self.crop_size
       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__				,							param_name="""crop_size"""						)
       __a       =   do_rescale if do_rescale is not None else self.do_rescale
       __a       =   rescale_factor if rescale_factor is not None else self.rescale_factor
       __a       =   do_normalize if do_normalize is not None else self.do_normalize
       __a       =   image_mean if image_mean is not None else self.image_mean
       __a       =   image_std if image_std is not None else self.image_std

       __a       =   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."""						)

       if do_resize and size is None:
            raise ValueError("""Size must be specified if do_resize is True."""						)

       if do_center_crop and crop_size is None:
            raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

       if do_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

       # All transformations expect numpy arrays.
       __a       =   [to_numpy_array(SCREAMING_SNAKE_CASE__						) for image in images]

       if do_resize:
            __a       =   [self.resize(image=SCREAMING_SNAKE_CASE__				,							size=SCREAMING_SNAKE_CASE__				,							resample=SCREAMING_SNAKE_CASE__						) for image in images]

       if do_center_crop:
            __a       =   [self.center_crop(image=SCREAMING_SNAKE_CASE__				,							size=SCREAMING_SNAKE_CASE__						) for image in images]

       if do_rescale:
            __a       =   [self.rescale(image=SCREAMING_SNAKE_CASE__				,							scale=SCREAMING_SNAKE_CASE__						) for image in images]

       if do_normalize:
            __a       =   [self.normalize(image=SCREAMING_SNAKE_CASE__				,							mean=SCREAMING_SNAKE_CASE__				,							std=SCREAMING_SNAKE_CASE__						) for image in images]

       __a       =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						) for image in images]

       __a       =   {"""pixel_values""": images}
       return BatchFeature(data=SCREAMING_SNAKE_CASE__				,							tensor_type=SCREAMING_SNAKE_CASE__						)


  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							List[Tuple] = None						):





       '''simple docstring'''

       __a       =   outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
            if len(SCREAMING_SNAKE_CASE__						) != len(SCREAMING_SNAKE_CASE__						):
                 raise ValueError(
                     """Make sure that you pass in as many target sizes as the batch dimension of the logits"""						)

            if is_torch_tensor(SCREAMING_SNAKE_CASE__						):
                 __a       =   target_sizes.numpy()

            __a       =   []

            for idx in range(len(SCREAMING_SNAKE_CASE__						)						):
                 __a       =   torch.nn.functional.interpolate(
                     logits[idx].unsqueeze(dim=0						)				,							size=target_sizes[idx]				,							mode="""bilinear"""				,							align_corners=SCREAMING_SNAKE_CASE__						)
                 __a       =   resized_logits[0].argmax(dim=0						)
                 semantic_segmentation.append(SCREAMING_SNAKE_CASE__						)
       else:
            __a       =   logits.argmax(dim=1						)
            __a       =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

       return semantic_segmentation










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




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

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

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

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




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


if __name__ == "__main__":
 main()






