




"""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     A__      (      lowercase_     ,	unittest.TestCase     ):
	A_							:				List[Any]						     =      BartTokenizer
	A_							:				Tuple						     =      BartTokenizerFast
	A_							:				str						     =      True
	A_							:				Any						     =      filter_roberta_detectors




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

						__snake_case          =	os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]	)
						__snake_case          =	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(lowercase	) + """\n"""	)
						with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8"""	) as fp:
											fp.write("""\n""".join(lowercase	)	)




	def 							a_		(   self  :	List[str]      ,    **lowercase  :	Tuple	)					-> Tuple:
						kwargs.update(self.special_tokens_map	)
						return self.tokenizer_class.from_pretrained(self.tmpdirname      ,    **lowercase	)




	def 							a_		(   self  :	Optional[Any]      ,    **lowercase  :	List[str]	)					-> Dict:
						kwargs.update(self.special_tokens_map	)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,    **lowercase	)




	def 							a_		(   self  :	Any      ,    lowercase  :	Optional[int]	)					-> str:
						return "lower newer", "lower newer"




	@cached_property
	def 							a_		(   self  :	Optional[Any]	)					-> Optional[Any]:
						return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




	@cached_property
	def 							a_		(   self  :	Optional[int]	)					-> List[Any]:
						return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




	@require_torch
	def 							a_		(   self  :	int	)					-> Any:
						__snake_case          =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						__snake_case          =	[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__snake_case          =	tokenizer(lowercase      ,    max_length=len(lowercase	)      ,    padding=lowercase      ,    return_tensors="""pt"""	)
											self.assertIsInstance(lowercase      ,    lowercase	)

											self.assertEqual((2, 9)      ,    batch.input_ids.shape	)
											self.assertEqual((2, 9)      ,    batch.attention_mask.shape	)
											__snake_case          =	batch.input_ids.tolist()[0]
											self.assertListEqual(lowercase      ,    lowercase	)
											# Test that special tokens are reset




	@require_torch
	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__snake_case          =	tokenizer(lowercase      ,    padding=lowercase      ,    return_tensors="""pt"""	)
											# check if input_ids are returned and no labels
											self.assertIn("""input_ids"""      ,    lowercase	)
											self.assertIn("""attention_mask"""      ,    lowercase	)
											self.assertNotIn("""labels"""      ,    lowercase	)
											self.assertNotIn("""decoder_attention_mask"""      ,    lowercase	)




	@require_torch
	def 							a_		(   self  :	Union[str, Any]	)					-> Optional[int]:
						__snake_case          =	[
						    """Summary of the text.""",
						    """Another summary.""",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__snake_case          =	tokenizer(text_target=lowercase      ,    max_length=32      ,    padding="""max_length"""      ,    return_tensors="""pt"""	)
											self.assertEqual(32      ,    targets["""input_ids"""].shape[1]	)




	@require_torch
	def 							a_		(   self  :	Union[str, Any]	)					-> Tuple:
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__snake_case          =	tokenizer(
											    ["""I am a small frog""" * 1_024, """I am a small frog"""]      ,    padding=lowercase      ,    truncation=lowercase      ,    return_tensors="""pt"""	)
											self.assertIsInstance(lowercase      ,    lowercase	)
											self.assertEqual(batch.input_ids.shape      ,    (2, 1_024)	)




	@require_torch
	def 							a_		(   self  :	Optional[Any]	)					-> Dict:
						__snake_case          =	["""A long paragraph for summarization."""]
						__snake_case          =	[
						    """Summary of the text.""",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__snake_case          =	tokenizer(lowercase      ,    return_tensors="""pt"""	)
											__snake_case          =	tokenizer(text_target=lowercase      ,    return_tensors="""pt"""	)
											__snake_case          =	inputs["""input_ids"""]
											__snake_case          =	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 							a_		(   self  :	Tuple	)					-> Union[str, Any]:
						pass




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

																# 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"""]	)      ,    )

																__snake_case          =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																__snake_case          =	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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																self.assertSequenceEqual(tokens_r["""input_ids"""]      ,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

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








"""simple docstring"""

from functools import lru_cache




@lru_cache
def       snake_case__	(       lowerCAmelCase   :		int		):
					if not isinstance(lowerCAmelCase				,					lowerCAmelCase		):
										raise TypeError("""k must be an integer."""		)
					if k < 0:
										raise ValueError("""k integer must be greater or equal to zero."""		)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1		) + fibonacci(k - 2		)




def       snake_case__	(       lowerCAmelCase   :		list				,					lowerCAmelCase   :		int		):
					__snake_case          =	len(lowerCAmelCase		)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					__snake_case          =	0
					while True:
										if fibonacci(lowerCAmelCase		) >= len_list:
															__snake_case          =	i
															break
										i += 1
					__snake_case          =	0
					while fibb_k > 0:
										__snake_case          =	min(
										    offset + fibonacci(fibb_k - 1		)				,					len_list - 1		)  # Prevent out of range
										__snake_case          =	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1		)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


