






"""simple docstring"""





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
  import torch







class 		_a       (						lowercase_   ):





   '''simple docstring'''






   UpperCamelCase__      					=    ["""image_processor""", """tokenizer"""]
   UpperCamelCase__      					=    """OneFormerImageProcessor"""
   UpperCamelCase__      					=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")





   def __init__(   self			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				UpperCAmelCase_ = 77			,				UpperCAmelCase_ = 77			,				**UpperCAmelCase_)	-> Dict:




          '''simple docstring'''




          if image_processor is None:
                 raise ValueError("You need to specify an `image_processor`.")
          if tokenizer is None:
                 raise ValueError("You need to specify a `tokenizer`.")

          lowercase__:    Any        =      max_seq_length
          lowercase__:    List[str]        =      task_seq_length

          super().__init__(UpperCAmelCase_			,				UpperCAmelCase_)





   def __lowercase    (   self			,				UpperCAmelCase_=None			,				UpperCAmelCase_=77)	-> Union[str, Any]:




          '''simple docstring'''




          if text_list is None:
                 raise ValueError("tokens cannot be None.")

          lowercase__:    str        =      self.tokenizer(UpperCAmelCase_			,				padding="max_length"			,				max_length=UpperCAmelCase_			,				truncation=UpperCAmelCase_)

          lowercase__  ,     lowercase__:    Optional[Any]        =      tokens["attention_mask"], tokens["input_ids"]

          lowercase__:    Optional[Any]        =      []
          for attn_mask, input_id in zip(UpperCAmelCase_			,				UpperCAmelCase_):
                 lowercase__:    List[Any]        =      torch.tensor(UpperCAmelCase_) * torch.tensor(UpperCAmelCase_)
                 token_inputs.append(token.unsqueeze(0))

          lowercase__:    str        =      torch.cat(UpperCAmelCase_			,				dim=0)
          return token_inputs





   def __call__(   self			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				**UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          if task_inputs is None:
                 raise ValueError("You have to specify the task_input. Found None.")
          elif images is None:
                 raise ValueError("You have to specify the image. Found None.")

          if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs):
                 raise ValueError("task_inputs must be semantic, instance, or panoptic.")

          lowercase__:    Optional[int]        =      self.image_processor(UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				**UpperCAmelCase_)

          if isinstance(UpperCAmelCase_			,				UpperCAmelCase_):
                 lowercase__:    Optional[Any]        =      [task_inputs]

          if isinstance(UpperCAmelCase_			,				UpperCAmelCase_) and all(isinstance(UpperCAmelCase_			,				UpperCAmelCase_) for task_input in task_inputs):
                 lowercase__:    List[Any]        =      []
                 for task in task_inputs:
                        lowercase__:    Union[str, Any]        =      F"""the task is {task}"""
                        task_token_inputs.append(UpperCAmelCase_)
                 lowercase__:    Optional[Any]        =      self._preprocess_text(UpperCAmelCase_			,				max_length=self.task_seq_length)
          else:
                 raise TypeError("Task Inputs should be a string or a list of strings.")

          if hasattr(UpperCAmelCase_			,				"text_inputs"):
                 lowercase__:    int        =      encoded_inputs.text_inputs

                 lowercase__:    List[str]        =      []
                 for texts in texts_list:
                        lowercase__:    Optional[Any]        =      self._preprocess_text(UpperCAmelCase_			,				max_length=self.max_seq_length)
                        text_inputs.append(text_input_list.unsqueeze(0))

                 lowercase__:    List[str]        =      torch.cat(UpperCAmelCase_			,				dim=0)

          return encoded_inputs





   def __lowercase    (   self			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				**UpperCAmelCase_)	-> Tuple:




          '''simple docstring'''




          if task_inputs is None:
                 raise ValueError("You have to specify the task_input. Found None.")
          elif images is None:
                 raise ValueError("You have to specify the image. Found None.")

          if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs):
                 raise ValueError("task_inputs must be semantic, instance, or panoptic.")

          lowercase__:    Union[str, Any]        =      self.image_processor.encode_inputs(UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				**UpperCAmelCase_)

          if isinstance(UpperCAmelCase_			,				UpperCAmelCase_):
                 lowercase__:    Optional[Any]        =      [task_inputs]

          if isinstance(UpperCAmelCase_			,				UpperCAmelCase_) and all(isinstance(UpperCAmelCase_			,				UpperCAmelCase_) for task_input in task_inputs):
                 lowercase__:    int        =      []
                 for task in task_inputs:
                        lowercase__:    Optional[Any]        =      F"""the task is {task}"""
                        task_token_inputs.append(UpperCAmelCase_)
                 lowercase__:    Optional[Any]        =      self._preprocess_text(UpperCAmelCase_			,				max_length=self.task_seq_length)
          else:
                 raise TypeError("Task Inputs should be a string or a list of strings.")

          if hasattr(UpperCAmelCase_			,				"text_inputs"):
                 lowercase__:    Union[str, Any]        =      encoded_inputs.text_inputs

                 lowercase__:    str        =      []
                 for texts in texts_list:
                        lowercase__:    int        =      self._preprocess_text(UpperCAmelCase_			,				max_length=self.max_seq_length)
                        text_inputs.append(text_input_list.unsqueeze(0))

                 lowercase__:    List[str]        =      torch.cat(UpperCAmelCase_			,				dim=0)

          return encoded_inputs





   def __lowercase    (   self			,				*UpperCAmelCase_			,				**UpperCAmelCase_)	-> int:




          '''simple docstring'''




          return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase_			,				**UpperCAmelCase_)





   def __lowercase    (   self			,				*UpperCAmelCase_			,				**UpperCAmelCase_)	-> int:




          '''simple docstring'''




          return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase_			,				**UpperCAmelCase_)





   def __lowercase    (   self			,				*UpperCAmelCase_			,				**UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase_			,				**UpperCAmelCase_)














"""simple docstring"""





import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

UpperCamelCase			=      0.25 + 0.0j
UpperCamelCase			=      -0.4 + 0.6j
UpperCamelCase			=      -0.1 + 0.651j
UpperCamelCase			=      -2.0
UpperCamelCase			=      56
UpperCamelCase			=      2.0
UpperCamelCase			=      6_66






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       return numpy.exp(snake_case_		) + c_parameter






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       return z_values * z_values + c_parameter






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__:    str        =      numpy.linspace(-window_size						,       snake_case_						,       snake_case_		)
       lowercase__:    Union[str, Any]        =      x.reshape((nb_pixels, 1)		)
       lowercase__:    Any        =      numpy.linspace(-window_size						,       snake_case_						,       snake_case_		)
       lowercase__:    List[Any]        =      y.reshape((1, nb_pixels)		)
       return x + 1.0J * y






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_ = None						,       ):
       """simple docstring"""





       lowercase__:    List[Any]        =      z_a.astype("complex64"		)
       for _ in range(snake_case_		):
              lowercase__:    Any        =      eval_function(snake_case_						,       snake_case_		)
              if infinity is not None:
                     numpy.nan_to_num(snake_case_						,       copy=snake_case_						,       nan=snake_case_		)
                     lowercase__:    Union[str, Any]        =      infinity
       return z_n






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       ):
       """simple docstring"""





       lowercase__:    Union[str, Any]        =      (abs(snake_case_		)).transpose()
       lowercase__:    List[Any]        =      abs_z_final[::-1, :]
       pyplot.matshow(abs_z_final < escape_radius		)
       pyplot.title(F"""Julia set of ${function_label}$, $c={function_params}$"""		)
       pyplot.show()






def     A(  ):
       """simple docstring"""





       warnings.filterwarnings(
           "ignore"						,       category=snake_case_						,       message="overflow encountered in multiply"		)
       warnings.filterwarnings(
           "ignore"						,       category=snake_case_						,       message="invalid value encountered in multiply"						,       )
       warnings.filterwarnings(
           "ignore"						,       category=snake_case_						,       message="overflow encountered in absolute"		)
       warnings.filterwarnings(
           "ignore"						,       category=snake_case_						,       message="overflow encountered in exp"		)


if __name__ == "__main__":
  UpperCamelCase			=      prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  UpperCamelCase			=      24
  UpperCamelCase			=      2 * abs(c_cauliflower) + 1
  UpperCamelCase			=      iterate_function(
      eval_quadratic_polynomial,
      c_cauliflower,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

  UpperCamelCase			=      64
  UpperCamelCase			=      2 * abs(c_polynomial_a) + 1
  UpperCamelCase			=      iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

  UpperCamelCase			=      1_61
  UpperCamelCase			=      2 * abs(c_polynomial_a) + 1
  UpperCamelCase			=      iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

  UpperCamelCase			=      12
  UpperCamelCase			=      10_000.0
  UpperCamelCase			=      iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0e10,
  )
  show_results("""e^z+c""", c_exponential, escape_radius, z_final)






