

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    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	LongformerTokenizer
					UpperCAmelCase__:       Union[str, Any]  							=	True
					UpperCAmelCase__:       Optional[int]  							=	LongformerTokenizerFast
					UpperCAmelCase__:       int  							=	True






					def   __A      (			self      ):
						super().setUp()

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

						A__							:   Any					       =		os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["""vocab_file"""]      )
						A__							:   Union[str, Any]					       =		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   ,	**A__      ):
						kwargs.update(self.special_tokens_map      )
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**A__      )






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






					def   __A      (			self   ,	A__      ):
						A__							:   Dict					       =		"""lower newer"""
						A__							:   Optional[int]					       =		"""lower newer"""
						return input_text, output_text






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

						A__							:   Optional[Any]					       =		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      ):
						A__							:   int					       =		self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("""Hello world!"""   ,	add_special_tokens=A__      )   ,	[0, 3_1414, 232, 328, 2]      )
						self.assertListEqual(
						    tokenizer.encode("""Hello world! cécé herlolip 418"""   ,	add_special_tokens=A__      )   ,	[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]   ,	)






					@slow
					def   __A      (			self      ):
						A__							:   str					       =		self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""      )

						A__							:   Any					       =		tokenizer.encode("""sequence builders"""   ,	add_special_tokens=A__      )
						A__							:   int					       =		tokenizer.encode("""multi-sequence build"""   ,	add_special_tokens=A__      )

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

						A__							:   Union[str, Any]					       =		tokenizer.build_inputs_with_special_tokens(A__      )
						A__							:   Optional[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      ):
						A__							:   Optional[int]					       =		self.get_tokenizer()

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

						# Testing encoder arguments
						A__							:   Optional[int]					       =		tokenizer.encode(A__   ,	add_special_tokens=A__   ,	add_prefix_space=A__      )
						A__							:   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__							:   List[Any]					       =		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__							:   Dict					       =		tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
						self.assertNotEqual(A__   ,	A__      )

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

						A__							:   Tuple					       =		"""Encode <mask> sequence"""
						A__							:   Optional[int]					       =		"""Encode <mask>sequence"""

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

						A__							:   Dict					       =		tokenizer.encode(A__      )
						A__							:   Dict					       =		encoded.index(A__      )
						A__							:   int					       =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
						self.assertNotEqual(A__   ,	A__      )






					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__							:   Optional[Any]					       =		self.rust_tokenizer_class.from_pretrained(A__   ,	**A__      )
								A__							:   Tuple					       =		self.tokenizer_class.from_pretrained(A__   ,	**A__      )
								A__							:   List[Any]					       =		"""A, <mask> AllenNLP sentence."""
								A__							:   Optional[int]					       =		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__							:   Optional[int]					       =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]      )
								A__							:   Optional[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, 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(
								    A__   ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )
								self.assertSequenceEqual(
								    A__   ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )






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

							A__							:   Optional[Any]					       =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
							A__							:   List[Any]					       =		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      ):
						# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
						# `trim_offsets`
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
								A__							:   Optional[int]					       =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
								A__							:   str					       =		F"""{text_of_1_token} {text_of_1_token}"""

								A__							:   Dict					       =		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, len(A__      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]   ,	(len(A__      ) + 1, len(A__      ) + 1 + len(A__      ))   ,	)

								A__							:   Dict					       =		self.rust_tokenizer_class.from_pretrained(
								    A__   ,	use_fast=A__   ,	add_prefix_space=A__   ,	trim_offsets=A__      )
								A__							:   Dict					       =		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__							:   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__      ), len(A__      ) + 1 + len(A__      ))   ,	)

								A__							:   Any					       =		self.rust_tokenizer_class.from_pretrained(
								    A__   ,	use_fast=A__   ,	add_prefix_space=A__   ,	trim_offsets=A__      )
								A__							:   Dict					       =		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__							:   List[str]					       =		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__							:   Any					       =		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]   ,	(1, 1 + len(A__      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]   ,	(1 + len(A__      ) + 1, 1 + len(A__      ) + 1 + len(A__      ))   ,	)

								A__							:   Union[str, Any]					       =		self.rust_tokenizer_class.from_pretrained(
								    A__   ,	use_fast=A__   ,	add_prefix_space=A__   ,	trim_offsets=A__      )
								A__							:   int					       =		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__							:   Optional[int]					       =		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, 1 + len(A__      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]   ,	(1 + len(A__      ), 1 + len(A__      ) + 1 + len(A__      ))   ,	)






from typing import Any

def 						UpperCamelCase (lowercase_:			list )					->				list[Any]:
	if not input_list:
		return []
	A__							:   Any					       =		[input_list.count(lowercase_ ) for value in input_list]
	A__							:   List[Any]					       =		max(lowercase_ )  # Gets the maximum count in the input list.
	# Gets values of modes
	return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



