




"""simple docstring"""







from __future__ import annotations

SCREAMING_SNAKE_CASE_      =					[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]

class 							snake_case_ :







			"""simple docstring"""




			def __init__(		self					,				lowerCamelCase_)							->      None:
							UpperCamelCase										=  -1
							UpperCamelCase										=  -1
							UpperCamelCase										=  [-1] * length
							UpperCamelCase										=  None
							UpperCamelCase										=  None







			def __repr__(		self)							->      str:
							return F'Node(min_value={self.minn} max_value={self.maxx})'





def     __snake_case   (							_lowercase						):


				"""simple docstring"""







				UpperCamelCase										=  Node(len(__lowerCamelCase						)						)
				UpperCamelCase					,  UpperCamelCase										=  min(__lowerCamelCase						), max(__lowerCamelCase						)
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				UpperCamelCase										=  (root.minn + root.maxx) // 2

				UpperCamelCase										=  []
				UpperCamelCase										=  []

				for index, num in enumerate(__lowerCamelCase						):
								if num <= pivot:
												left_arr.append(__lowerCamelCase						)
								else:
												right_arr.append(__lowerCamelCase						)
								UpperCamelCase										=  len(__lowerCamelCase						)
				UpperCamelCase										=  build_tree(__lowerCamelCase						)
				UpperCamelCase										=  build_tree(__lowerCamelCase						)
				return root





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


				"""simple docstring"""







				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				UpperCamelCase										=  (node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left ,__lowerCamelCase ,node.map_left[index] - 1						)
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right ,__lowerCamelCase ,index - node.map_left[index]						)





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase						):


				"""simple docstring"""







				if start > end:
								return 0
				UpperCamelCase										=  rank_till_index(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase						)
				UpperCamelCase										=  rank_till_index(__lowerCamelCase ,__lowerCamelCase ,start - 1						)
				return rank_till_end - rank_before_start





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase						):


				"""simple docstring"""







				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				UpperCamelCase										=  node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left ,__lowerCamelCase ,(node.map_left[start - 1] if start else 0) ,node.map_left[end] - 1 ,)
				else:
								return quantile(
								    node.right ,index - num_elements_in_left_tree ,start - (node.map_left[start - 1] if start else 0) ,end - node.map_left[end] ,)





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


				"""simple docstring"""







				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				UpperCamelCase										=  range_counting(
				    node.left ,(node.map_left[start - 1] if start else 0) ,node.map_left[end] - 1 ,__lowerCamelCase ,__lowerCamelCase ,)
				UpperCamelCase										=  range_counting(
				    node.right ,start - (node.map_left[start - 1] if start else 0) ,end - node.map_left[end] ,__lowerCamelCase ,__lowerCamelCase ,)
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()





"""simple docstring"""







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


SCREAMING_SNAKE_CASE_      =					get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					ErnieMTokenizer
   A_										=					False
   A_										=					True
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[str]:
       super().setUp()

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  '''this is a test'''
       UpperCamelCase										=  '''this is a test'''
       return input_text, output_text







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       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      UpperCAmelCase__ (		self)							->      Optional[Any]:
       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_)					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      int:
       self.assertEqual(self.get_tokenizer().vocab_size					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       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      UpperCAmelCase__ (		self)							->      List[Any]:
       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_)					,				[4_8, 2_5, 2_1, 1_2_8_9])

       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_					,				[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 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      UpperCAmelCase__ (		self)							->      Dict:
       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      UpperCAmelCase__ (		self)							->      List[str]:
       # fmt: off
       UpperCamelCase										=  {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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.''',
           ]					,				)