




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       _A				(		__snake_case       ,     unittest.TestCase     ):
			__a					    =       LEDTokenizer
			__a					    =       LEDTokenizerFast
			__a					    =       True






			def 				UpperCAmelCase (    self			):
									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(_lowercase						,		range(len(_lowercase			)			)			)			)
									_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(_lowercase			) + """\n"""			)
									with open(self.merges_file						,		"""w"""						,		encoding="""utf-8"""			) as fp:
															fp.write("""\n""".join(_lowercase			)			)






			def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
									kwargs.update(self.special_tokens_map			)
									return self.tokenizer_class.from_pretrained(self.tmpdirname						,		**_lowercase			)






			def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
									kwargs.update(self.special_tokens_map			)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,		**_lowercase			)






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
									return "lower newer", "lower newer"






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






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






			@require_torch
			def 				UpperCAmelCase (    self			):
									_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(_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			)
															_UpperCAmelCase      =			batch.input_ids.tolist()[0]
															self.assertListEqual(_lowercase						,		_lowercase			)






			@require_torch
			def 				UpperCAmelCase (    self			):
									_UpperCAmelCase      =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase      =			tokenizer(_lowercase						,		padding=_lowercase						,		return_tensors="""pt"""			)
															self.assertIn("""input_ids"""						,		_lowercase			)
															self.assertIn("""attention_mask"""						,		_lowercase			)
															self.assertNotIn("""labels"""						,		_lowercase			)
															self.assertNotIn("""decoder_attention_mask"""						,		_lowercase			)






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






			@require_torch
			def 				UpperCAmelCase (    self			):

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase      =			tokenizer(
															    ["""I am a small frog""" * 1024, """I am a small frog"""]						,		padding=_lowercase						,		truncation=_lowercase						,		return_tensors="""pt"""			)
															self.assertIsInstance(_lowercase						,		_lowercase			)
															self.assertEqual(batch.input_ids.shape						,		(2, 5122)			)






			@require_torch
			def 				UpperCAmelCase (    self			):
									_UpperCAmelCase      =			["""A long paragraph for summarization."""]
									_UpperCAmelCase      =			[
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase      =			tokenizer(_lowercase						,		return_tensors="""pt"""			)
															_UpperCAmelCase      =			tokenizer(text_target=_lowercase						,		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			):

									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(_lowercase						,		padding=_lowercase			)
															_UpperCAmelCase      =			[[0] * len(_lowercase			) for x in encoded_output["""input_ids"""]]
															_UpperCAmelCase      =			tokenizer.pad(_lowercase			)
															self.assertSequenceEqual(outputs["""global_attention_mask"""]						,		_lowercase			)






			def 				UpperCAmelCase (    self			):
									pass






			def 				UpperCAmelCase (    self			):

									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(_lowercase						,		**_lowercase			)
																					_UpperCAmelCase      =			self.tokenizer_class.from_pretrained(_lowercase						,		**_lowercase			)
																					_UpperCAmelCase      =			"""A, <mask> AllenNLP sentence."""
																					_UpperCAmelCase      =			tokenizer_r.encode_plus(_lowercase						,		add_special_tokens=_lowercase						,		return_token_type_ids=_lowercase			)
																					_UpperCAmelCase      =			tokenizer_p.encode_plus(_lowercase						,		add_special_tokens=_lowercase						,		return_token_type_ids=_lowercase			)
																					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(
																					    _lowercase						,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)
																					self.assertSequenceEqual(
																					    _lowercase						,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """Expected the same number of rows for A and B. """
												    f"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """Expected the same number of columns for B and C. """
												    f"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""       )

						return mat_c - mat_b.T @ a_inv @ mat_b




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()