




"""simple docstring"""

import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        lowercase      (    __UpperCAmelCase     ,	unittest.TestCase):

				'''simple docstring'''





				__lowerCAmelCase    :    Dict         =   LongformerTokenizer
				__lowerCAmelCase    :    Optional[Any]         =   True
				__lowerCAmelCase    :    Union[str, Any]         =   LongformerTokenizerFast
				__lowerCAmelCase    :    Optional[Any]         =   True







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					A_  :				Optional[int]	   =		[
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					A_  :				Optional[Any]	   =		dict(zip(A__      ,     range(len(A__       )       )       )       )
					A_  :				Any	   =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					A_  :				str	   =		{"""unk_token""": """<unk>"""}

					A_  :				Dict	   =		os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['''vocab_file''']       )
					A_  :				Optional[int]	   =		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(A__       ) + '''\n'''       )
					with open(self.merges_file      ,     '''w'''      ,     encoding='''utf-8'''       ) as fp:
						fp.write('''\n'''.join(A__       )       )







				def 					a_  (							self   :					List[Any]      ,     **_lowerCamelCase   :					List[Any]       ):



					"""simple docstring"""

					kwargs.update(self.special_tokens_map       )
					return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **A__       )







				def 					a_  (							self   :					Dict      ,     **_lowerCamelCase   :					Any       ):



					"""simple docstring"""

					kwargs.update(self.special_tokens_map       )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,     **A__       )







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Dict	   =		"""lower newer"""
					A_  :				Dict	   =		"""lower newer"""
					return input_text, output_text







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.tokenizer_class(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map       )
					A_  :				Union[str, Any]	   =		"""lower newer"""
					A_  :				Dict	   =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
					A_  :				List[Any]	   =		tokenizer.tokenize(A__       )  # , add_prefix_space=True)
					self.assertListEqual(A__      ,     A__       )

					A_  :				Dict	   =		tokens + [tokenizer.unk_token]
					A_  :				List[str]	   =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A__       )      ,     A__       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		self.get_tokenizer()

					self.assertListEqual(tokenizer.encode('''Hello world!'''      ,     add_special_tokens=A__       )      ,     [0, 3_14_14, 2_32, 3_28, 2]       )
					self.assertListEqual(
					    tokenizer.encode('''Hello world! cécé herlolip 418'''      ,     add_special_tokens=A__       )      ,     [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,     )







				@slow
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				Tuple	   =		self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''       )

					A_  :				Dict	   =		tokenizer.encode('''sequence builders'''      ,     add_special_tokens=A__       )
					A_  :				Optional[int]	   =		tokenizer.encode('''multi-sequence build'''      ,     add_special_tokens=A__       )

					A_  :				Any	   =		tokenizer.encode(
					    '''sequence builders'''      ,     add_special_tokens=A__      ,     add_prefix_space=A__       )
					A_  :				List[str]	   =		tokenizer.encode(
					    '''sequence builders'''      ,     '''multi-sequence build'''      ,     add_special_tokens=A__      ,     add_prefix_space=A__       )

					A_  :				Tuple	   =		tokenizer.build_inputs_with_special_tokens(A__       )
					A_  :				List[Any]	   =		tokenizer.build_inputs_with_special_tokens(A__      ,     A__       )

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		self.get_tokenizer()

					A_  :				str	   =		"""Encode this sequence."""
					A_  :				List[Any]	   =		tokenizer.byte_encoder[""" """.encode('''utf-8'''       )[0]]

					# Testing encoder arguments
					A_  :				Optional[Any]	   =		tokenizer.encode(A__      ,     add_special_tokens=A__      ,     add_prefix_space=A__       )
					A_  :				Optional[int]	   =		tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
					self.assertNotEqual(A__      ,     A__       )

					A_  :				str	   =		tokenizer.encode(A__      ,     add_special_tokens=A__      ,     add_prefix_space=A__       )
					A_  :				Optional[int]	   =		tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
					self.assertEqual(A__      ,     A__       )

					tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}       )
					A_  :				Optional[int]	   =		tokenizer.encode(A__      ,     add_special_tokens=A__       )
					A_  :				Any	   =		tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
					self.assertNotEqual(A__      ,     A__       )

					# Testing spaces after special tokens
					A_  :				Dict	   =		"""<mask>"""
					tokenizer.add_special_tokens(
					    {'''mask_token''': AddedToken(A__      ,     lstrip=A__      ,     rstrip=A__       )}       )  # mask token has a left space
					A_  :				Dict	   =		tokenizer.convert_tokens_to_ids(A__       )

					A_  :				List[Any]	   =		"""Encode <mask> sequence"""
					A_  :				Tuple	   =		"""Encode <mask>sequence"""

					A_  :				Optional[int]	   =		tokenizer.encode(A__       )
					A_  :				Union[str, Any]	   =		encoded.index(A__       )
					A_  :				Optional[Any]	   =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
					self.assertEqual(A__      ,     A__       )

					A_  :				Optional[int]	   =		tokenizer.encode(A__       )
					A_  :				Union[str, Any]	   =		encoded.index(A__       )
					A_  :				Union[str, Any]	   =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
					self.assertNotEqual(A__      ,     A__       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
							A_  :				List[Any]	   =		self.rust_tokenizer_class.from_pretrained(A__      ,     **A__       )
							A_  :				int	   =		self.tokenizer_class.from_pretrained(A__      ,     **A__       )
							A_  :				Optional[Any]	   =		"""A, <mask> AllenNLP sentence."""
							A_  :				Union[str, Any]	   =		tokenizer_r.encode_plus(A__      ,     add_special_tokens=A__      ,     return_token_type_ids=A__       )
							A_  :				Dict	   =		tokenizer_p.encode_plus(A__      ,     add_special_tokens=A__      ,     return_token_type_ids=A__       )

							# 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_  :				Any	   =		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
							A_  :				int	   =		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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )
							self.assertSequenceEqual(tokens_r['''input_ids''']      ,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )

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







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					for trim_offsets, add_prefix_space in itertools.product([True, False]      ,     repeat=2       ):
						A_  :				str	   =		self.rust_tokenizer_class.from_pretrained(
						    self.tmpdirname      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )

						A_  :				Dict	   =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
						A_  :				Optional[int]	   =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

						self.assertEqual(pre_tokenizer_state['''add_prefix_space''']      ,     A__       )

						self.assertEqual(post_processor_state['''add_prefix_space''']      ,     A__       )
						self.assertEqual(post_processor_state['''trim_offsets''']      ,     A__       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
							A_  :				List[Any]	   =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
							A_  :				int	   =		F"""{text_of_1_token} {text_of_1_token}"""

							A_  :				Union[str, Any]	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				Union[str, Any]	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (0, len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (len(A__       ) + 1, len(A__       ) + 1 + len(A__       ))      ,     )

							A_  :				Optional[Any]	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				Union[str, Any]	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (0, len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (len(A__       ) + 1, len(A__       ) + 1 + len(A__       ))      ,     )

							A_  :				List[str]	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				str	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (0, len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (len(A__       ), len(A__       ) + 1 + len(A__       ))      ,     )

							A_  :				str	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				Tuple	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (0, len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (len(A__       ), len(A__       ) + 1 + len(A__       ))      ,     )

							A_  :				Any	   =		F""" {text}"""

							# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
							#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
							# )
							# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
							# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
							# self.assertEqual(
							#     encoding.offset_mapping[1],
							#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
							# )

							A_  :				Optional[Any]	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				Optional[Any]	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (1, 1 + len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (1 + len(A__       ) + 1, 1 + len(A__       ) + 1 + len(A__       ))      ,     )

							A_  :				int	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				Any	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (0, 1 + len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (1 + len(A__       ), 1 + len(A__       ) + 1 + len(A__       ))      ,     )

							A_  :				int	   =		self.rust_tokenizer_class.from_pretrained(
							    A__      ,     use_fast=A__      ,     add_prefix_space=A__      ,     trim_offsets=A__       )
							A_  :				List[str]	   =		tokenizer_r(A__      ,     return_offsets_mapping=A__      ,     add_special_tokens=A__       )
							self.assertEqual(encoding.offset_mapping[0]      ,     (0, 1 + len(A__       ))       )
							self.assertEqual(
							    encoding.offset_mapping[1]      ,     (1 + len(A__       ), 1 + len(A__       ) + 1 + len(A__       ))      ,     )











"""simple docstring"""

from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	return np.array_equal(_UpperCAmelCase  ,      matrix.conjugate().T	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		v.conjugate().T
	A_  :				Tuple	   =		v_star.dot(_UpperCAmelCase	)
	assert isinstance(_UpperCAmelCase  ,      np.ndarray	)
	return (v_star_dot.dot(_UpperCAmelCase	)) / (v_star.dot(_UpperCAmelCase	))

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]	)
	A_  :				str	   =		np.array([[1], [2], [3]]	)
	assert is_hermitian(_UpperCAmelCase	), f"""{a} is not hermitian."""
	print(rayleigh_quotient(_UpperCAmelCase  ,      _UpperCAmelCase	)	)

	A_  :				int	   =		np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]	)
	assert is_hermitian(_UpperCAmelCase	), f"""{a} is not hermitian."""
	assert rayleigh_quotient(_UpperCAmelCase  ,      _UpperCAmelCase	) == float(3	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		tests()





