



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					(      __lowerCamelCase	,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__ :					Union[str, Any]			   =    BartTokenizer
							UpperCamelCase__ :					Union[str, Any]			   =    BartTokenizerFast
							UpperCamelCase__ :					Any			   =    True
							UpperCamelCase__ :					Optional[Any]			   =    filter_roberta_detectors


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

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


							def _a					(							self     ,     **a_						):
											kwargs.update(self.special_tokens_map						)
											return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **_UpperCamelCase						)


							def _a					(							self     ,     **a_						):
											kwargs.update(self.special_tokens_map						)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,     **_UpperCamelCase						)


							def _a					(							self     ,     a_						):
											return "lower newer", "lower newer"


							@cached_property
							def _a					(							self						):
											return BartTokenizer.from_pretrained("""facebook/bart-large"""						)


							@cached_property
							def _a					(							self						):
											return BartTokenizerFast.from_pretrained("""facebook/bart-large"""						)


							@require_torch
							def _a					(							self						):
											a__ 					=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											a__ 					=						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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


							@require_torch
							def _a					(							self						):
											a__ 					=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__ 					=						tokenizer(_UpperCamelCase     ,     padding=_UpperCamelCase     ,     return_tensors="""pt"""						)
															# check if input_ids are returned and no labels
															self.assertIn("""input_ids"""     ,     _UpperCamelCase						)
															self.assertIn("""attention_mask"""     ,     _UpperCamelCase						)
															self.assertNotIn("""labels"""     ,     _UpperCamelCase						)
															self.assertNotIn("""decoder_attention_mask"""     ,     _UpperCamelCase						)


							@require_torch
							def _a					(							self						):
											a__ 					=						[
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__ 					=						tokenizer(text_target=_UpperCamelCase     ,     max_length=32     ,     padding="""max_length"""     ,     return_tensors="""pt"""						)
															self.assertEqual(32     ,     targets["""input_ids"""].shape[1]						)


							@require_torch
							def _a					(							self						):





											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__ 					=						tokenizer(
															    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,     padding=_UpperCamelCase     ,     truncation=_UpperCamelCase     ,     return_tensors="""pt"""						)
															self.assertIsInstance(_UpperCamelCase     ,     _UpperCamelCase						)
															self.assertEqual(batch.input_ids.shape     ,     (2, 1_024)						)


							@require_torch
							def _a					(							self						):
											a__ 					=						["""A long paragraph for summarization."""]
											a__ 					=						[
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__ 					=						tokenizer(_UpperCamelCase     ,     return_tensors="""pt"""						)
															a__ 					=						tokenizer(text_target=_UpperCamelCase     ,     return_tensors="""pt"""						)
															a__ 					=						inputs["""input_ids"""]
															a__ 					=						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						):
											pass







							def _a					(							self						):





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
																			a__ 					=						self.rust_tokenizer_class.from_pretrained(_UpperCamelCase     ,     **_UpperCamelCase						)
																			a__ 					=						self.tokenizer_class.from_pretrained(_UpperCamelCase     ,     **_UpperCamelCase						)
																			a__ 					=						"""A, <mask> AllenNLP sentence."""
																			a__ 					=						tokenizer_r.encode_plus(_UpperCamelCase     ,     add_special_tokens=_UpperCamelCase     ,     return_token_type_ids=_UpperCamelCase						)
																			a__ 					=						tokenizer_p.encode_plus(_UpperCamelCase     ,     add_special_tokens=_UpperCamelCase     ,     return_token_type_ids=_UpperCamelCase						)

																			# 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__ 					=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
																			a__ 					=						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(
																			    _UpperCamelCase     ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
																			self.assertSequenceEqual(
																			    _UpperCamelCase     ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)







def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=1.0
    for i in range(1,				r + 1	):
        ret *= (n + 1 - i) / i
    return round(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return choose(8 + n,				__UpperCamelCase	) + choose(9 + n,				__UpperCamelCase	) - 10



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return sum(non_bouncy_exact(__UpperCamelCase	) for i in range(1,				n + 1	)	)



def 	UpperCAmelCase_    (	__UpperCamelCase = 100	):
    return non_bouncy_upto(__UpperCamelCase	)


if __name__ == "__main__":
    print(f"""{solution() = }""")






