



import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str       						=["""image_processor""", """tokenizer"""]
				UpperCamelCase__			:   Union[str, Any]       						="""OwlViTImageProcessor"""
				UpperCamelCase__			:   Union[str, Any]       						=("""CLIPTokenizer""", """CLIPTokenizerFast""")




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

							lowercase__   					=			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__(__lowercase,   __lowercase  )




				def __call__(  self   :  Any,   __lowercase   :  Optional[Any]=None,   __lowercase   :  List[Any]=None,   __lowercase   :  Optional[int]=None,   __lowercase   :  Dict="max_length",   __lowercase   :  Any="np",   **__lowercase   :  Optional[Any]  ):

							if text is None and query_images is None and images is None:
										raise ValueError(
										    "You have to specify at least one text or query image or image. All three cannot be none."  )

							if text is not None:
										if isinstance(__lowercase,   __lowercase  ) or (isinstance(__lowercase,   __lowercase  ) and not isinstance(text[0],   __lowercase  )):
													lowercase__   					=			[self.tokenizer(__lowercase,   padding=__lowercase,   return_tensors=__lowercase,   **__lowercase  )]

										elif isinstance(__lowercase,   __lowercase  ) and isinstance(text[0],   __lowercase  ):
													lowercase__   					=			[]

													# Maximum number of queries across batch
													lowercase__   					=			max([len(__lowercase  ) for t in text]  )

													# Pad all batch samples to max number of text queries
													for t in text:
																if len(__lowercase  ) != max_num_queries:
																			lowercase__   					=			t + [" "] * (max_num_queries - len(__lowercase  ))

																lowercase__   					=			self.tokenizer(__lowercase,   padding=__lowercase,   return_tensors=__lowercase,   **__lowercase  )
																encodings.append(__lowercase  )
										else:
													raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

										if return_tensors == "np":
													lowercase__   					=			np.concatenate([encoding["input_ids"] for encoding in encodings],   axis=0  )
													lowercase__   					=			np.concatenate([encoding["attention_mask"] for encoding in encodings],   axis=0  )

										elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													lowercase__   					=			jnp.concatenate([encoding["input_ids"] for encoding in encodings],   axis=0  )
													lowercase__   					=			jnp.concatenate([encoding["attention_mask"] for encoding in encodings],   axis=0  )

										elif return_tensors == "pt" and is_torch_available():
													import torch

													lowercase__   					=			torch.cat([encoding["input_ids"] for encoding in encodings],   dim=0  )
													lowercase__   					=			torch.cat([encoding["attention_mask"] for encoding in encodings],   dim=0  )

										elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													lowercase__   					=			tf.stack([encoding["input_ids"] for encoding in encodings],   axis=0  )
													lowercase__   					=			tf.stack([encoding["attention_mask"] for encoding in encodings],   axis=0  )

										else:
													raise ValueError("Target return tensor type could not be returned"  )

										lowercase__   					=			BatchEncoding()
										lowercase__   					=			input_ids
										lowercase__   					=			attention_mask

							if query_images is not None:
										lowercase__   					=			BatchEncoding()
										lowercase__   					=			self.image_processor(
										    __lowercase,   return_tensors=__lowercase,   **__lowercase  ).pixel_values
										lowercase__   					=			query_pixel_values

							if images is not None:
										lowercase__   					=			self.image_processor(__lowercase,   return_tensors=__lowercase,   **__lowercase  )

							if text is not None and images is not None:
										lowercase__   					=			image_features.pixel_values
										return encoding
							elif query_images is not None and images is not None:
										lowercase__   					=			image_features.pixel_values
										return encoding
							elif text is not None or query_images is not None:
										return encoding
							else:
										return BatchEncoding(data=dict(**__lowercase  ),   tensor_type=__lowercase  )




				def 							A__ (  self   :  Any,   *__lowercase   :  List[str],   **__lowercase   :  Optional[int]  ):
							return self.image_processor.post_process(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  List[str],   *__lowercase   :  List[Any],   **__lowercase   :  List[str]  ):
							return self.image_processor.post_process_object_detection(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  List[Any],   *__lowercase   :  List[Any],   **__lowercase   :  Union[str, Any]  ):
							return self.image_processor.post_process_image_guided_detection(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  Dict,   *__lowercase   :  Union[str, Any],   **__lowercase   :  str  ):
							return self.tokenizer.batch_decode(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  List[Any],   *__lowercase   :  Optional[Any],   **__lowercase   :  List[Any]  ):
							return self.tokenizer.decode(*__lowercase,   **__lowercase  )




				@property
				def 							A__ (  self   :  List[Any]  ):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",   __lowercase,   )
							return self.image_processor_class




				@property
				def 							A__ (  self   :  List[str]  ):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",   __lowercase,   )
							return self.image_processor









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						lowercase__   					=			n - k
			# Calculate C(n,k)
			for i in range(SCREAMING_SNAKE_CASE_					):
						result *= n - i
						result //= i + 1
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return binomial_coefficient(2 * node_count  ,					SCREAMING_SNAKE_CASE_					) // (node_count + 1)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if n < 0:
						raise ValueError("factorial() not defined for negative values"					)
			lowercase__   					=			1
			for i in range(1  ,					n + 1					):
						result *= i
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return catalan_number(SCREAMING_SNAKE_CASE_					) * factorial(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							lowercase_						=       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.'
							)




