"""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      UpperCAmelCase_       (						UpperCamelCase__			,							unittest.TestCase):
							snake_case__        =      LEDTokenizer
							snake_case__        =      LEDTokenizerFast
							snake_case__        =      True






							def       _UpperCamelCase			(		self :      Dict  )       ->       Any:
												super().setUp()
												_UpperCamelCase      						=	[
												    '''l''',
												    '''o''',
												    '''w''',
												    '''e''',
												    '''r''',
												    '''s''',
												    '''t''',
												    '''i''',
												    '''d''',
												    '''n''',
												    '''\u0120''',
												    '''\u0120l''',
												    '''\u0120n''',
												    '''\u0120lo''',
												    '''\u0120low''',
												    '''er''',
												    '''\u0120lowest''',
												    '''\u0120newer''',
												    '''\u0120wider''',
												    '''<unk>''',
												]
												_UpperCamelCase      						=	dict(zip(__lowerCamelCase     ,						range(len(__lowerCamelCase  )  )  )  )
												_UpperCamelCase      						=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
												_UpperCamelCase      						=	{'''unk_token''': '''<unk>'''}

												_UpperCamelCase      						=	os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES['''vocab_file''']  )
												_UpperCamelCase      						=	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       _UpperCamelCase			(		self :      Union[str, Any]     ,						**__UpperCamelCase :      Optional[Any]  )       ->       List[str]:
												kwargs.update(self.special_tokens_map  )
												return self.tokenizer_class.from_pretrained(self.tmpdirname     ,						**__lowerCamelCase  )






							def       _UpperCamelCase			(		self :      int     ,						**__UpperCamelCase :      Union[str, Any]  )       ->       List[Any]:
												kwargs.update(self.special_tokens_map  )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,						**__lowerCamelCase  )






							def       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      Any  )       ->       int:
												return "lower newer", "lower newer"






							@cached_property
							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       Any:
												return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''  )






							@cached_property
							def       _UpperCamelCase			(		self :      str  )       ->       Any:
												return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''  )






							@require_torch
							def       _UpperCamelCase			(		self :      Tuple  )       ->       List[str]:
												_UpperCamelCase      						=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												_UpperCamelCase      						=	[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase      						=	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  )
																	_UpperCamelCase      						=	batch.input_ids.tolist()[0]
																	self.assertListEqual(__lowerCamelCase     ,						__lowerCamelCase  )






							@require_torch
							def       _UpperCamelCase			(		self :      List[Any]  )       ->       Any:
												_UpperCamelCase      						=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase      						=	tokenizer(__lowerCamelCase     ,						padding=__lowerCamelCase     ,						return_tensors='''pt'''  )
																	self.assertIn('''input_ids'''     ,						__lowerCamelCase  )
																	self.assertIn('''attention_mask'''     ,						__lowerCamelCase  )
																	self.assertNotIn('''labels'''     ,						__lowerCamelCase  )
																	self.assertNotIn('''decoder_attention_mask'''     ,						__lowerCamelCase  )






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






							@require_torch
							def       _UpperCamelCase			(		self :      str  )       ->       List[Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase      						=	tokenizer(
																	    ['''I am a small frog''' * 1024, '''I am a small frog''']     ,						padding=__lowerCamelCase     ,						truncation=__lowerCamelCase     ,						return_tensors='''pt'''  )
																	self.assertIsInstance(__lowerCamelCase     ,						__lowerCamelCase  )
																	self.assertEqual(batch.input_ids.shape     ,						(2, 5122)  )






							@require_torch
							def       _UpperCamelCase			(		self :      Dict  )       ->       Optional[int]:
												_UpperCamelCase      						=	['''A long paragraph for summarization.''']
												_UpperCamelCase      						=	[
												    '''Summary of the text.''',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase      						=	tokenizer(__lowerCamelCase     ,						return_tensors='''pt'''  )
																	_UpperCamelCase      						=	tokenizer(text_target=__lowerCamelCase     ,						return_tensors='''pt'''  )
																	_UpperCamelCase      						=	inputs['''input_ids''']
																	_UpperCamelCase      						=	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       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       Optional[int]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase      						=	['''Summary of the text.''', '''Another summary.''']
																	_UpperCamelCase      						=	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																	_UpperCamelCase      						=	tokenizer(__lowerCamelCase     ,						padding=__lowerCamelCase  )
																	_UpperCamelCase      						=	[[0] * len(__lowerCamelCase  ) for x in encoded_output['''input_ids''']]
																	_UpperCamelCase      						=	tokenizer.pad(__lowerCamelCase  )
																	self.assertSequenceEqual(outputs['''global_attention_mask''']     ,						__lowerCamelCase  )






							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       int:
												pass






							def       _UpperCamelCase			(		self :      Tuple  )       ->       Any:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																						_UpperCamelCase      						=	self.rust_tokenizer_class.from_pretrained(__lowerCamelCase     ,						**__lowerCamelCase  )
																						_UpperCamelCase      						=	self.tokenizer_class.from_pretrained(__lowerCamelCase     ,						**__lowerCamelCase  )
																						_UpperCamelCase      						=	'''A, <mask> AllenNLP sentence.'''
																						_UpperCamelCase      						=	tokenizer_r.encode_plus(__lowerCamelCase     ,						add_special_tokens=__lowerCamelCase     ,						return_token_type_ids=__lowerCamelCase  )
																						_UpperCamelCase      						=	tokenizer_p.encode_plus(__lowerCamelCase     ,						add_special_tokens=__lowerCamelCase     ,						return_token_type_ids=__lowerCamelCase  )
																						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      						=	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																						_UpperCamelCase      						=	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(
																						    __lowerCamelCase     ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
																						self.assertSequenceEqual(
																						    __lowerCamelCase     ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )







import numpy as np


def 	UpperCamelCase__(       UpperCamelCase__			:       np.array  )->np.array:
       return (2 / (1 + np.exp(-2 * vector  ))) - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()





