
"""simple docstring"""


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Tuple									=						['image_processor', 'tokenizer']
				a__:			List[Any]									=						'LayoutLMv2ImageProcessor'
				a__:			List[str]									=						('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




				def __init__(							self       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	**UpperCAmelCase      ):
							lowerCamelCase_ 		=     None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
										    ''' instead.'''       ,	UpperCAmelCase       ,	)
										lowerCamelCase_ 		=     kwargs.pop('''feature_extractor'''      )

							lowerCamelCase_ 		=     image_processor if image_processor is not None else feature_extractor
							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`.'''      )

							super().__init__(UpperCAmelCase       ,	UpperCAmelCase      )




				def __call__(							self       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = True       ,	UpperCAmelCase = False       ,	UpperCAmelCase = False       ,	UpperCAmelCase = None       ,	UpperCAmelCase = 0       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = False       ,	UpperCAmelCase = False       ,	UpperCAmelCase = False       ,	UpperCAmelCase = False       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							# verify input
							if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''      )

							if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''      )

							if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''      )

							# first, apply the image processor
							lowerCamelCase_ 		=     self.image_processor(images=UpperCAmelCase       ,	return_tensors=UpperCAmelCase      )

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(UpperCAmelCase       ,	UpperCAmelCase      ):
													lowerCamelCase_ 		=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
										lowerCamelCase_ 		=     features['''words''']

							lowerCamelCase_ 		=     self.tokenizer(
							    text=text if text is not None else features['''words''']       ,	text_pair=text_pair if text_pair is not None else None       ,	boxes=boxes if boxes is not None else features['''boxes''']       ,	word_labels=UpperCAmelCase       ,	add_special_tokens=UpperCAmelCase       ,	padding=UpperCAmelCase       ,	truncation=UpperCAmelCase       ,	max_length=UpperCAmelCase       ,	stride=UpperCAmelCase       ,	pad_to_multiple_of=UpperCAmelCase       ,	return_token_type_ids=UpperCAmelCase       ,	return_attention_mask=UpperCAmelCase       ,	return_overflowing_tokens=UpperCAmelCase       ,	return_special_tokens_mask=UpperCAmelCase       ,	return_offsets_mapping=UpperCAmelCase       ,	return_length=UpperCAmelCase       ,	verbose=UpperCAmelCase       ,	return_tensors=UpperCAmelCase       ,	**UpperCAmelCase       ,	)

							# add pixel values
							lowerCamelCase_ 		=     features.pop('''pixel_values'''      )
							if return_overflowing_tokens is True:
										lowerCamelCase_ 		=     self.get_overflowing_images(UpperCAmelCase       ,	encoded_inputs['''overflow_to_sample_mapping''']      )
							lowerCamelCase_ 		=     images

							return encoded_inputs




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							lowerCamelCase_ 		=     []
							for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]      )

							if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
										raise ValueError(
										    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
										    f" {len(UpperCAmelCase      )} and {len(UpperCAmelCase      )}"      )

							return images_with_overflow




				def 					UpperCAmelCase__	(							self       ,	*UpperCAmelCase       ,	**UpperCAmelCase      ):
							return self.tokenizer.batch_decode(*UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	*UpperCAmelCase       ,	**UpperCAmelCase      ):
							return self.tokenizer.decode(*UpperCAmelCase       ,	**UpperCAmelCase      )




				@property
				def 					UpperCAmelCase__	(							self      ):
							return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




				@property
				def 					UpperCAmelCase__	(							self      ):
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,	UpperCAmelCase       ,	)
							return self.image_processor_class





				@property
				def 					UpperCAmelCase__	(							self      ):
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,	UpperCAmelCase       ,	)
							return self.image_processor





"""simple docstring"""


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

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     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    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


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

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				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_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				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_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				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_						       =				12
						A_						       =				10000.0
						A_						       =				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)



