



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

import numpy
from matplotlib import pyplot

A__						:						List[Any]          =      0.2_5 + 0.0J
A__						:						Any          =      -0.4 + 0.6J
A__						:						List[str]          =      -0.1 + 0.651J
A__						:						int          =      -2.0
A__						:						Optional[int]          =      56
A__						:						Optional[int]          =      2.0
A__						:						Dict          =      666


def     UpperCamelCase(						__UpperCamelCase							:	Any  ,__UpperCamelCase							:	Optional[Any]				):
  return numpy.exp(_UpperCAmelCase				) + c_parameter


def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Optional[int]				):
  return z_values * z_values + c_parameter


def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]  ,__UpperCamelCase							:	Dict				):
  lowerCAmelCase_    :  Union[str, Any]             =				numpy.linspace(-window_size  ,_UpperCAmelCase  ,_UpperCAmelCase				)
  lowerCAmelCase_    :  Optional[int]             =				x.reshape((nb_pixels, 1)				)
  lowerCAmelCase_    :  Tuple             =				numpy.linspace(-window_size  ,_UpperCAmelCase  ,_UpperCAmelCase				)
  lowerCAmelCase_    :  int             =				y.reshape((1, nb_pixels)				)
  return x + 1.0j * y


def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	List[str] = None  ,):
  lowerCAmelCase_    :  List[str]             =				z_a.astype('''complex64'''				)
  for _ in range(_UpperCAmelCase				):
    lowerCAmelCase_    :  List[Any]             =				eval_function(_UpperCAmelCase  ,_UpperCAmelCase				)
    if infinity is not None:
      numpy.nan_to_num(_UpperCAmelCase  ,copy=_UpperCAmelCase  ,nan=_UpperCAmelCase				)
      lowerCAmelCase_    :  List[str]             =				infinity
  return z_n


def     UpperCamelCase(						__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	Optional[int]  ,__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	int  ,):
  lowerCAmelCase_    :  List[Any]             =				(abs(_UpperCAmelCase				)).transpose()
  lowerCAmelCase_    :  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     UpperCamelCase(						):
  warnings.filterwarnings(
      '''ignore'''  ,category=_UpperCAmelCase  ,message='''overflow encountered in multiply'''				)
  warnings.filterwarnings(
      '''ignore'''  ,category=_UpperCAmelCase  ,message='''invalid value encountered in multiply'''  ,)
  warnings.filterwarnings(
      '''ignore'''  ,category=_UpperCAmelCase  ,message='''overflow encountered in absolute'''				)
  warnings.filterwarnings(
      '''ignore'''  ,category=_UpperCAmelCase  ,message='''overflow encountered in exp'''				)


if __name__ == "__main__":
      A__						:						List[Any]          =      prepare_grid(window_size, nb_pixels)

      ignore_overflow_warnings()  # See file header for explanations

      A__						:						Tuple          =      24
      A__						:						Union[str, Any]          =      2 * abs(c_cauliflower) + 1
      A__						:						Union[str, Any]          =      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)

      A__						:						Dict          =      64
      A__						:						List[str]          =      2 * abs(c_polynomial_a) + 1
      A__						:						List[Any]          =      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)

      A__						:						List[Any]          =      161
      A__						:						List[str]          =      2 * abs(c_polynomial_a) + 1
      A__						:						Tuple          =      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)

      A__						:						Tuple          =      12
      A__						:						List[str]          =      1_0000.0
      A__						:						Optional[Any]          =      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)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Any          =      [
						    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SegformerDecodeHead''',
						    '''SegformerForImageClassification''',
						    '''SegformerForSemanticSegmentation''',
						    '''SegformerLayer''',
						    '''SegformerModel''',
						    '''SegformerPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSegformerDecodeHead''',
						    '''TFSegformerForImageClassification''',
						    '''TFSegformerForSemanticSegmentation''',
						    '''TFSegformerModel''',
						    '''TFSegformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





