


'''simple docstring'''



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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


@require_tokenizers
class 						_snake_case    (   lowerCamelCase_				,      unittest.TestCase		):
					lowerCAmelCase					:Optional[int]            =    LEDTokenizer
					lowerCAmelCase					:int            =    LEDTokenizerFast
					lowerCAmelCase					:Dict            =    True




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

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




					def  snake_case__  (					self							,							**_lowerCamelCase):
								kwargs.update(self.special_tokens_map)
								return self.tokenizer_class.from_pretrained(self.tmpdirname							,							**__snake_case)




					def  snake_case__  (					self							,							**_lowerCamelCase):
								kwargs.update(self.special_tokens_map)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,							**__snake_case)




					def  snake_case__  (					self							,							_lowerCamelCase):
								return "lower newer", "lower newer"




					@cached_property
					def  snake_case__  (					self):
								return LEDTokenizer.from_pretrained("""allenai/led-base-16384""")




					@cached_property
					def  snake_case__  (					self):
								return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""")




					@require_torch
					def  snake_case__  (					self):
								UpperCAmelCase__     :    str         =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								UpperCAmelCase__     :    List[Any]         =   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

											self.assertEqual((2, 9)							,							batch.input_ids.shape)
											self.assertEqual((2, 9)							,							batch.attention_mask.shape)
											UpperCAmelCase__     :    Optional[int]         =   batch.input_ids.tolist()[0]
											self.assertListEqual(__snake_case							,							__snake_case)




					@require_torch
					def  snake_case__  (					self):
								UpperCAmelCase__     :    Optional[int]         =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCAmelCase__     :    str         =   tokenizer(__snake_case							,							padding=__snake_case							,							return_tensors="""pt""")
											self.assertIn("""input_ids"""							,							__snake_case)
											self.assertIn("""attention_mask"""							,							__snake_case)
											self.assertNotIn("""labels"""							,							__snake_case)
											self.assertNotIn("""decoder_attention_mask"""							,							__snake_case)




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




					@require_torch
					def  snake_case__  (					self):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCAmelCase__     :    Optional[Any]         =   tokenizer(
											    ["""I am a small frog""" * 1024, """I am a small frog"""]							,							padding=__snake_case							,							truncation=__snake_case							,							return_tensors="""pt""")
											self.assertIsInstance(__snake_case							,							__snake_case)
											self.assertEqual(batch.input_ids.shape							,							(2, 5122))




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




					@require_torch
					def  snake_case__  (					self):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCAmelCase__     :    Dict         =   ["""Summary of the text.""", """Another summary."""]
											UpperCAmelCase__     :    Tuple         =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											UpperCAmelCase__     :    Any         =   tokenizer(__snake_case							,							padding=__snake_case)
											UpperCAmelCase__     :    Tuple         =   [[0] * len(__snake_case) for x in encoded_output["""input_ids"""]]
											UpperCAmelCase__     :    Dict         =   tokenizer.pad(__snake_case)
											self.assertSequenceEqual(outputs["""global_attention_mask"""]							,							__snake_case)




					def  snake_case__  (					self):
								pass




					def  snake_case__  (					self):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
														UpperCAmelCase__     :    Any         =   self.rust_tokenizer_class.from_pretrained(__snake_case							,							**__snake_case)
														UpperCAmelCase__     :    Any         =   self.tokenizer_class.from_pretrained(__snake_case							,							**__snake_case)
														UpperCAmelCase__     :    Dict         =   """A, <mask> AllenNLP sentence."""
														UpperCAmelCase__     :    Optional[Any]         =   tokenizer_r.encode_plus(__snake_case							,							add_special_tokens=__snake_case							,							return_token_type_ids=__snake_case)
														UpperCAmelCase__     :    List[Any]         =   tokenizer_p.encode_plus(__snake_case							,							add_special_tokens=__snake_case							,							return_token_type_ids=__snake_case)
														self.assertEqual(sum(tokens_r["""token_type_ids"""])							,							sum(tokens_p["""token_type_ids"""]))
														self.assertEqual(
														    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])							,							sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])							,							)

														UpperCAmelCase__     :    Union[str, Any]         =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
														UpperCAmelCase__     :    Union[str, Any]         =   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])
														self.assertSequenceEqual(tokens_p["""input_ids"""]							,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
														self.assertSequenceEqual(tokens_r["""input_ids"""]							,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

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



def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> str:
														a__             =     []
														a__             =     min(len(_stra   )					,    len(_stra   )   ) // 2
														for i, l in enumerate(_stra   ):
																					a__             =     int(max(0					,    i - limit   )   )
																					a__             =     int(min(i + limit + 1					,    len(_stra   )   )   )
																					if l in _stra[left:right]:
																												matched.append(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

							# transposition
							a__             =     (
							    len([(ca, ca) for ca, ca in zip(__lowerCAmelCase					,    __lowerCAmelCase   ) if ca != ca]   ) // 2
							)

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							a__             =     0
							for ca, ca in zip(stra[:4]					,    stra[:4]   ):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler('''hello''', '''world'''))




