
"""simple docstring"""
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class  _snake_case	(    _a			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    BartTokenizer
							SCREAMING_SNAKE_CASE		  =    BartTokenizerFast
							SCREAMING_SNAKE_CASE		  =    True
							SCREAMING_SNAKE_CASE		  =    filter_roberta_detectors






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												super().setUp()
												__a:    Tuple		     =       [
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__a:    List[Any]		     =       dict(zip(__lowerCamelCase    ,			range(len(__lowerCamelCase     )     )     )     )
												__a:    Union[str, Any]		     =       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__a:    List[str]		     =       {"""unk_token""": """<unk>"""}

												__a:    Optional[Any]		     =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES["""vocab_file"""]     )
												__a:    List[str]		     =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES["""merges_file"""]     )
												with open(self.vocab_file    ,			"""w"""    ,			encoding="""utf-8"""     ) as fp:
																	fp.write(json.dumps(__lowerCamelCase     ) + """\n"""     )
												with open(self.merges_file    ,			"""w"""    ,			encoding="""utf-8"""     ) as fp:
																	fp.write("""\n""".join(__lowerCamelCase     )     )






							def 				__lowercase	(       self    :      List[Any]    ,			**snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												kwargs.update(self.special_tokens_map     )
												return self.tokenizer_class.from_pretrained(self.tmpdirname    ,			**__lowerCamelCase     )






							def 				__lowercase	(       self    :      int    ,			**snake_case__    :      List[Any]     ):






												"""simple docstring"""



												kwargs.update(self.special_tokens_map     )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,			**__lowerCamelCase     )






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												return "lower newer", "lower newer"






							@cached_property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return BartTokenizer.from_pretrained("""facebook/bart-large"""     )






							@cached_property
							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												return BartTokenizerFast.from_pretrained("""facebook/bart-large"""     )






							@require_torch
							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												__a:    Tuple		     =       [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__a:    Optional[Any]		     =       tokenizer(__lowerCamelCase    ,			max_length=len(__lowerCamelCase     )    ,			padding=__lowerCamelCase    ,			return_tensors="""pt"""     )
																	self.assertIsInstance(__lowerCamelCase    ,			__lowerCamelCase     )

																	self.assertEqual((2, 9)    ,			batch.input_ids.shape     )
																	self.assertEqual((2, 9)    ,			batch.attention_mask.shape     )
																	__a:    Optional[Any]		     =       batch.input_ids.tolist()[0]
																	self.assertListEqual(__lowerCamelCase    ,			__lowerCamelCase     )
																	# Test that special tokens are reset






							@require_torch
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__a:    Tuple		     =       tokenizer(__lowerCamelCase    ,			padding=__lowerCamelCase    ,			return_tensors="""pt"""     )
																	# check if input_ids are returned and no labels
																	self.assertIn("""input_ids"""    ,			__lowerCamelCase     )
																	self.assertIn("""attention_mask"""    ,			__lowerCamelCase     )
																	self.assertNotIn("""labels"""    ,			__lowerCamelCase     )
																	self.assertNotIn("""decoder_attention_mask"""    ,			__lowerCamelCase     )






							@require_torch
							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Tuple		     =       [
												    """Summary of the text.""",
												    """Another summary.""",
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__a:    str		     =       tokenizer(text_target=__lowerCamelCase    ,			max_length=3_2    ,			padding="""max_length"""    ,			return_tensors="""pt"""     )
																	self.assertEqual(3_2    ,			targets["""input_ids"""].shape[1]     )






							@require_torch
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__a:    Optional[int]		     =       tokenizer(
																	    ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]    ,			padding=__lowerCamelCase    ,			truncation=__lowerCamelCase    ,			return_tensors="""pt"""     )
																	self.assertIsInstance(__lowerCamelCase    ,			__lowerCamelCase     )
																	self.assertEqual(batch.input_ids.shape    ,			(2, 1_0_2_4)     )






							@require_torch
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    List[Any]		     =       ["""A long paragraph for summarization."""]
												__a:    int		     =       [
												    """Summary of the text.""",
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__a:    Union[str, Any]		     =       tokenizer(__lowerCamelCase    ,			return_tensors="""pt"""     )
																	__a:    Union[str, Any]		     =       tokenizer(text_target=__lowerCamelCase    ,			return_tensors="""pt"""     )
																	__a:    Any		     =       inputs["""input_ids"""]
																	__a:    Optional[Any]		     =       targets["""input_ids"""]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()     )
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()     )
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()     )
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												pass






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																						__a:    Dict		     =       self.rust_tokenizer_class.from_pretrained(__lowerCamelCase    ,			**__lowerCamelCase     )
																						__a:    Optional[Any]		     =       self.tokenizer_class.from_pretrained(__lowerCamelCase    ,			**__lowerCamelCase     )
																						__a:    Optional[Any]		     =       """A, <mask> AllenNLP sentence."""
																						__a:    int		     =       tokenizer_r.encode_plus(__lowerCamelCase    ,			add_special_tokens=__lowerCamelCase    ,			return_token_type_ids=__lowerCamelCase     )
																						__a:    Optional[Any]		     =       tokenizer_p.encode_plus(__lowerCamelCase    ,			add_special_tokens=__lowerCamelCase    ,			return_token_type_ids=__lowerCamelCase     )

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r["""token_type_ids"""]     )    ,			sum(tokens_p["""token_type_ids"""]     )     )

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r["""attention_mask"""]     ) / len(tokens_r["""attention_mask"""]     )    ,			sum(tokens_p["""attention_mask"""]     ) / len(tokens_p["""attention_mask"""]     )    ,			)

																						__a:    List[Any]		     =       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]     )
																						__a:    List[Any]		     =       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]     )

																						# Rust correctly handles the space before the mask while python doesnt
																						self.assertSequenceEqual(tokens_p["""input_ids"""]    ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )
																						self.assertSequenceEqual(tokens_r["""input_ids"""]    ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )

																						self.assertSequenceEqual(
																						    __lowerCamelCase    ,			["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]     )
																						self.assertSequenceEqual(
																						    __lowerCamelCase    ,			["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]     )








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      [0]
		UpperCamelCase	:Optional[int]		=      0





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:List[str]		=      self.__heap[i]
		while i // 2 > 0:
			if self.__heap[i] > self.__heap[i // 2]:
				UpperCamelCase	:Any		=      self.__heap[i // 2]
				UpperCamelCase	:Optional[Any]		=      temporary
			i //= 2





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   int      ):
		self.__heap.append(__lowerCamelCase      )
		self.__size += 1
		self.__swap_up(self.__size      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   int      ):
		while self.__size >= 2 * i:
			if 2 * i + 1 > self.__size:
				UpperCamelCase	:List[Any]		=      2 * i
			else:
				if self.__heap[2 * i] > self.__heap[2 * i + 1]:
					UpperCamelCase	:Union[str, Any]		=      2 * i
				else:
					UpperCamelCase	:Dict		=      2 * i + 1
			UpperCamelCase	:Optional[Any]		=      self.__heap[i]
			if self.__heap[i] < self.__heap[bigger_child]:
				UpperCamelCase	:List[str]		=      self.__heap[bigger_child]
				UpperCamelCase	:List[str]		=      temporary
			UpperCamelCase	:Optional[int]		=      bigger_child





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.__heap[1]
		UpperCamelCase	:Tuple		=      self.__heap[self.__size]
		self.__size -= 1
		self.__heap.pop()
		self.__swap_down(1      )
		return max_value





	@property
	def 		_A					(						self							:   int      ):
		return self.__heap[1:]





	def __len__(						self							:   str      ):
		return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	UpperCAmelCase_					:		Optional[int]           = BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
