

import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a:      str								=     get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase      (   lowercase       ,       unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       ErnieMTokenizer
	SCREAMING_SNAKE_CASE__      			=       False
	SCREAMING_SNAKE_CASE__      			=       True
	SCREAMING_SNAKE_CASE__      			=       False
	SCREAMING_SNAKE_CASE__      			=       False




	def  __A   (						self		:  int							):


		'''simple docstring'''







		super().setUp()

		# We have a SentencePiece fixture for testing
		UpperCAmelCase_    			=     ErnieMTokenizer(lowerCAmelCase						,		unk_token="<unk>"						,		pad_token="<pad>"							)
		tokenizer.save_pretrained(self.tmpdirname							)




	def  __A   (						self		:  List[Any]						,		lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     "this is a test"
		UpperCAmelCase_    			=     "this is a test"
		return input_text, output_text




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     "<pad>"
		UpperCAmelCase_    			=     0

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase							)						,		lowerCAmelCase							)
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase							)						,		lowerCAmelCase							)




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     list(self.get_tokenizer().get_vocab().keys()							)

		self.assertEqual(vocab_keys[0]						,		"<pad>"							)
		self.assertEqual(vocab_keys[1]						,		"<unk>"							)
		self.assertEqual(vocab_keys[-1]						,		"▁eloquent"							)
		self.assertEqual(len(lowerCAmelCase							)						,		30_000							)




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		self.assertEqual(self.get_tokenizer().vocab_size						,		30_000							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		if not self.test_rust_tokenizer:
			return

		UpperCAmelCase_    			=     self.get_tokenizer()
		UpperCAmelCase_    			=     self.get_rust_tokenizer()

		UpperCAmelCase_    			=     "I was born in 92000, and this is falsé."

		UpperCAmelCase_    			=     tokenizer.tokenize(lowerCAmelCase							)
		UpperCAmelCase_    			=     rust_tokenizer.tokenize(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase						,		lowerCAmelCase							)

		UpperCAmelCase_    			=     tokenizer.encode(lowerCAmelCase						,		add_special_tokens=lowerCAmelCase							)
		UpperCAmelCase_    			=     rust_tokenizer.encode(lowerCAmelCase						,		add_special_tokens=lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase						,		lowerCAmelCase							)

		UpperCAmelCase_    			=     self.get_rust_tokenizer()
		UpperCAmelCase_    			=     tokenizer.encode(lowerCAmelCase							)
		UpperCAmelCase_    			=     rust_tokenizer.encode(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase						,		lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     ErnieMTokenizer(lowerCAmelCase						,		do_lower_case=lowerCAmelCase						,		unk_token="<unk>"						,		pad_token="<pad>"							)

		UpperCAmelCase_    			=     tokenizer.tokenize("This is a test"							)
		self.assertListEqual(lowerCAmelCase						,		["▁this", "▁is", "▁a", "▁test"]							)

		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase							)						,		[48, 25, 21, 1_289]							)

		UpperCAmelCase_    			=     tokenizer.tokenize("I was born in 92000, and this is falsé."							)
		# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
		self.assertListEqual(
		    lowerCAmelCase						,		["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]							)
		UpperCAmelCase_    			=     tokenizer.convert_tokens_to_ids(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase						,		[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]							)

		UpperCAmelCase_    			=     tokenizer.convert_ids_to_tokens(lowerCAmelCase							)
		self.assertListEqual(
		    lowerCAmelCase						,		["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]						,		)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     ErnieMTokenizer(lowerCAmelCase						,		unk_token="<unk>"						,		pad_token="<pad>"							)

		UpperCAmelCase_    			=     tokenizer.encode("sequence builders"							)
		UpperCAmelCase_    			=     tokenizer.encode("multi-sequence build"							)

		UpperCAmelCase_    			=     tokenizer.build_inputs_with_special_tokens(lowerCAmelCase							)
		UpperCAmelCase_    			=     tokenizer.build_inputs_with_special_tokens(lowerCAmelCase						,		lowerCAmelCase							)

		assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
		assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
		    tokenizer.sep_token_id
		] + text_a + [tokenizer.sep_token_id]




	@slow
	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {"input_ids": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
		# fmt: on

		self.tokenizer_integration_test_util(
		    expected_encoding=lowerCAmelCase						,		model_name="susnato/ernie-m-base_pytorch"						,		sequences=[
		        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
		        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
		        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
		        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
		        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
		        "conditioning on both left and right context in all layers.",
		        "The quick brown fox jumps over the lazy dog.",
		    ]						,		)


# 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   __UpperCamelCase      :




	def __init__(						self		:  Optional[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     key
		UpperCAmelCase_    			=     freq




	def __str__(						self		:  List[str]							):


		'''simple docstring'''







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



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A    ,       A    ,       A      ):
	if i > j or i < 0 or j > len(A      ) - 1:
		return

	UpperCAmelCase_    			=     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(A    ,       A    ,       A    ,       node - 1    ,       key[node]    ,       A      )
	print_binary_search_tree(A    ,       A    ,       node + 1    ,       A    ,       key[node]    ,       A      )



def  __lowerCAmelCase (  A      ):
	nodes.sort(key=lambda A      : node.key      )

	UpperCAmelCase_    			=     len(A      )

	UpperCAmelCase_    			=     [nodes[i].key for i in range(A      )]
	UpperCAmelCase_    			=     [nodes[i].freq for i in range(A      )]

	# 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.
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# stores tree roots that will be used later for constructing binary search tree
	UpperCAmelCase_    			=     [[i if i == j else 0 for j in range(A      )] for i in range(A      )]

	for interval_length in range(2    ,       n + 1      ):
		for i in range(n - interval_length + 1      ):
			UpperCAmelCase_    			=     i + interval_length - 1

			UpperCAmelCase_    			=     sys.maxsize  # set the value to "infinity"
			UpperCAmelCase_    			=     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
				UpperCAmelCase_    			=     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				UpperCAmelCase_    			=     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				UpperCAmelCase_    			=     left + total[i][j] + right

				if dp[i][j] > cost:
					UpperCAmelCase_    			=     cost
					UpperCAmelCase_    			=     r

	print("Binary search tree nodes:"      )
	for node in nodes:
		print(A      )

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



def  __lowerCAmelCase (  ):
	# A sample binary search tree
	UpperCAmelCase_    			=     [Node(A    ,       randint(1    ,       50      )      ) for i in range(10    ,       0    ,       -1      )]
	find_optimal_binary_search_tree(A      )


if __name__ == "__main__":
				main()