






"""simple docstring"""







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      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		["""image_processor""", """tokenizer"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		"""Pix2StructImageProcessor"""
						_SCREAMING_SNAKE_CASE  : str												=		("""T5Tokenizer""", """T5TokenizerFast""")






						def __init__(		self  :Dict			, __magic_name__  :Any			, __magic_name__  :List[str]				)      ->List[Any]:
										lowercase    :      Tuple       = False
										super().__init__(__magic_name__			, __magic_name__				)






						def __call__(		self  :List[Any]			, __magic_name__  :Tuple=None			, __magic_name__  :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[bool, str, PaddingStrategy] = False			, __magic_name__  :Union[bool, str, TruncationStrategy] = None			, __magic_name__  :Optional[int] = None			, __magic_name__  :Optional[int] = 2_048			, __magic_name__  :int = 0			, __magic_name__  :Optional[int] = None			, __magic_name__  :Optional[bool] = None			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, TensorType]] = None			, **__magic_name__  :Optional[Any]			, )      ->BatchEncoding:
										if images is None and text is None:
														raise ValueError("""You have to specify either images or text."""				)

										# Get only text
										if images is None and not self.image_processor.is_vqa:
														lowercase    :      Optional[int]       = self.tokenizer
														lowercase    :      int       = self.tokenizer(
														    text=__magic_name__			, add_special_tokens=__magic_name__			, padding=__magic_name__			, truncation=__magic_name__			, max_length=__magic_name__			, stride=__magic_name__			, pad_to_multiple_of=__magic_name__			, return_attention_mask=__magic_name__			, return_overflowing_tokens=__magic_name__			, return_special_tokens_mask=__magic_name__			, return_offsets_mapping=__magic_name__			, return_token_type_ids=__magic_name__			, return_length=__magic_name__			, verbose=__magic_name__			, return_tensors=__magic_name__			, **__magic_name__			, )
														return text_encoding

										if not self.image_processor.is_vqa:
														# add pixel_values
														lowercase    :      Union[str, Any]       = self.image_processor(
														    __magic_name__			, return_tensors=__magic_name__			, max_patches=__magic_name__			, **__magic_name__				)
										else:
														# add pixel_values and bbox
														lowercase    :      str       = self.image_processor(
														    __magic_name__			, return_tensors=__magic_name__			, max_patches=__magic_name__			, header_text=__magic_name__			, **__magic_name__				)

										if text is not None and not self.image_processor.is_vqa:
														lowercase    :      int       = self.tokenizer(
														    text=__magic_name__			, add_special_tokens=__magic_name__			, padding=__magic_name__			, truncation=__magic_name__			, max_length=__magic_name__			, stride=__magic_name__			, pad_to_multiple_of=__magic_name__			, return_attention_mask=__magic_name__			, return_overflowing_tokens=__magic_name__			, return_special_tokens_mask=__magic_name__			, return_offsets_mapping=__magic_name__			, return_token_type_ids=__magic_name__			, return_length=__magic_name__			, verbose=__magic_name__			, return_tensors=__magic_name__			, **__magic_name__			, )

														if "attention_mask" in text_encoding:
																		lowercase    :      Union[str, Any]       = text_encoding.pop("""attention_mask"""				)
														if "input_ids" in text_encoding:
																		lowercase    :      Optional[Any]       = text_encoding.pop("""input_ids"""				)
										else:
														lowercase    :      Any       = None

										if text_encoding is not None:
														encoding_image_processor.update(__magic_name__				)

										return encoding_image_processor






						def      __snake_case      (		self  :List[Any]			, *__magic_name__  :Optional[Any]			, **__magic_name__  :List[str]				)      ->Optional[Any]:
										return self.tokenizer.batch_decode(*__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Any			, *__magic_name__  :Tuple			, **__magic_name__  :List[Any]				)      ->Tuple:
										return self.tokenizer.decode(*__magic_name__			, **__magic_name__				)






						@property
						def      __snake_case      (		self  :Any				)      ->Union[str, Any]:
										lowercase    :      List[Any]       = self.tokenizer.model_input_names
										lowercase    :      Any       = self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       int:
				lowercase    :      int       = 1  # To kept the Calculated Value
				# Since C(n, k) = C(n, n-k)
				if k > (n - k):
								lowercase    :      List[Any]       = n - k
				# Calculate C(n,k)
				for i in range(_A ):
								result *= n - i
								result //= i + 1
				return result


def      UpperCamelCase       ( _A )     ->       int:
				return binomial_coefficient(2 * node_count  ,				_A ) // (node_count + 1)


def      UpperCamelCase       ( _A )     ->       int:
				if n < 0:
								raise ValueError("""factorial() not defined for negative values""" )
				lowercase    :      Union[str, Any]       = 1
				for i in range(1  ,				n + 1 ):
								result *= i
				return result


def      UpperCamelCase       ( _A )     ->       int:
				return catalan_number(_A ) * factorial(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	int(input('Enter the number of nodes: ').strip() or 0)
				if node_count <= 0:
								raise ValueError('We need some nodes to work with.')
				print(
				    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
				    F'binary trees and {catalan_number(node_count)} binary search trees.'
				)
