
"""simple docstring"""


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class    A_     :



							"""simple docstring"""






							def __init__(  self  :int					,							lowerCamelCase_  :str					,							lowerCamelCase_  :int	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=key
										lowerCamelCase__     :					Union[str, Any]		=freq





							def __str__(  self  :List[str]	):





										"""simple docstring"""

										return f"""Node(key={self.key}, freq={self.freq})"""






def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      str							,   snake_case_		:      Union[str, Any]							,   snake_case_		:      Optional[int]							,   snake_case_		:      Optional[int]							,   snake_case_		:      List[Any]      )       ->List[Any]:
			if i > j or i < 0 or j > len(snake_case_      ) - 1:
						return

			lowerCamelCase__     :					List[str]		=root[i][j]
			if parent == -1:  # root does not have a parent
						print(f"""{key[node]} is the root of the binary search tree."""      )
			elif is_left:
						print(f"""{key[node]} is the left child of key {parent}."""      )
			else:
						print(f"""{key[node]} is the right child of key {parent}."""      )

			print_binary_search_tree(snake_case_							,   snake_case_							,   snake_case_							,   node - 1							,   key[node]							,   snake_case_      )
			print_binary_search_tree(snake_case_							,   snake_case_							,   node + 1							,   snake_case_							,   key[node]							,   snake_case_      )






def        lowerCAmelCase_       (		snake_case_		:      List[str]      )       ->Optional[Any]:
			nodes.sort(key=lambda snake_case_      : node.key      )

			lowerCamelCase__     :					Any		=len(snake_case_      )

			lowerCamelCase__     :					Any		=[nodes[i].key for i in range(snake_case_      )]
			lowerCamelCase__     :					Dict		=[nodes[i].freq for i in range(snake_case_      )]

			# This 2D array stores the overall tree cost (which's as minimized as possible);
			# for a single key, cost is equal to frequency of the key.
			lowerCamelCase__     :					int		=[[freqs[i] if i == j else 0 for j in range(snake_case_      )] for i in range(snake_case_      )]
			# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
			# array
			lowerCamelCase__     :					Optional[Any]		=[[freqs[i] if i == j else 0 for j in range(snake_case_      )] for i in range(snake_case_      )]
			# stores tree roots that will be used later for constructing binary search tree
			lowerCamelCase__     :					Union[str, Any]		=[[i if i == j else 0 for j in range(snake_case_      )] for i in range(snake_case_      )]

			for interval_length in range(2							,   n + 1      ):
						for i in range(n - interval_length + 1      ):
									lowerCamelCase__     :					List[Any]		=i + interval_length - 1

									lowerCamelCase__     :					Union[str, Any]		=sys.maxsize  # set the value to "infinity"
									lowerCamelCase__     :					Union[str, Any]		=total[i][j - 1] + freqs[j]

									# Apply Knuth's optimization
									# Loop without optimization: for r in range(i, j + 1):
									for r in range(root[i][j - 1]							,   root[i + 1][j] + 1      ):  # r is a temporal root
												lowerCamelCase__     :					int		=dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
												lowerCamelCase__     :					Optional[Any]		=dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
												lowerCamelCase__     :					Union[str, Any]		=left + total[i][j] + right

												if dp[i][j] > cost:
															lowerCamelCase__     :					str		=cost
															lowerCamelCase__     :					str		=r

			print('Binary search tree nodes:'      )
			for node in nodes:
						print(snake_case_      )

			print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""      )
			print_binary_search_tree(snake_case_							,   snake_case_							,   0							,   n - 1							,   -1							,   snake_case_      )






def        lowerCAmelCase_       (		)       ->Optional[int]:
			# A sample binary search tree
			lowerCamelCase__     :					List[str]		=[Node(snake_case_							,   randint(1							,   5_0      )      ) for i in range(1_0							,   0							,   -1      )]
			find_optimal_binary_search_tree(snake_case_      )


if __name__ == "__main__":
							main()

"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase								=						{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase								=						{
    """openai/clip-vit-base-patch32""": 77,
}

class    A_     (					A__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_     				=       PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_     				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_     				=       ["""input_ids""", """attention_mask"""]
							SCREAMING_SNAKE_CASE_     				=       CLIPTokenizer





							def __init__(  self  :List[str]					,							lowerCamelCase_  :Tuple=None					,							lowerCamelCase_  :Any=None					,							lowerCamelCase_  :Optional[Any]=None					,							lowerCamelCase_  :List[Any]="<|endoftext|>"					,							lowerCamelCase_  :Tuple="<|startoftext|>"					,							lowerCamelCase_  :Dict="<|endoftext|>"					,							lowerCamelCase_  :str="<|endoftext|>"					,							**lowerCamelCase_  :int					,							):





										"""simple docstring"""

										super().__init__(
										    lowerCamelCase_					,							lowerCamelCase_					,							tokenizer_file=lowerCamelCase_					,							unk_token=lowerCamelCase_					,							bos_token=lowerCamelCase_					,							eos_token=lowerCamelCase_					,							pad_token=lowerCamelCase_					,							**lowerCamelCase_					,							)

										if not isinstance(self.backend_tokenizer.pre_tokenizer					,							pre_tokenizers.Sequence	):
													raise ValueError(
													    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
													    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
													    ' to be compatible with this version.The easiest way to do so is'
													    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
													    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
													    ' transformers.'	)

										self._wrap_decode_method_backend_tokenizer()





							def 				UpperCAmelCase__							(  self  :Union[str, Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=self.backend_tokenizer.decode

										def new_decode_method(*lowerCamelCase_  :Optional[int]					,							**lowerCamelCase_  :List[Any]	):
													lowerCamelCase__     :					Any		=orig_decode_method(*lowerCamelCase_					,							**lowerCamelCase_	)
													lowerCamelCase__     :					Optional[int]		=text.replace(self.backend_tokenizer.model.end_of_word_suffix					,							' '	).strip()
													return text

										lowerCamelCase__     :					Optional[Any]		=new_decode_method





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :List[int]					,							lowerCamelCase_  :Optional[List[int]] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=[self.bos_token_id]
										lowerCamelCase__     :					Any		=[self.eos_token_id]

										if token_ids_a is None:
													return bos_token + token_ids_a + eos_token
										return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :List[int]					,							lowerCamelCase_  :Optional[List[int]] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=[self.bos_token_id]
										lowerCamelCase__     :					Optional[Any]		=[self.eos_token_id]

										if token_ids_a is None:
													return len(bos_token + token_ids_a + eos_token	) * [0]
										return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token	) * [0]





							def 				UpperCAmelCase__							(  self  :Tuple					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Optional[str] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					int		=self._tokenizer.model.save(lowerCamelCase_					,							name=lowerCamelCase_	)
										return tuple(lowerCamelCase_	)