
'''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 			lowerCAmelCase (				UpperCAmelCase_		,     unittest.TestCase			):
		a							:					int       =					LongformerTokenizer
		a							:					Optional[int]       =					True
		a							:					Any       =					LongformerTokenizerFast
		a							:					Dict       =					True




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

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

								_SCREAMING_SNAKE_CASE				= os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES["vocab_file"]      )
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						**UpperCamelCase      ):
								kwargs.update(self.special_tokens_map      )
								return self.tokenizer_class.from_pretrained(self.tmpdirname					,						**_snake_case      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								kwargs.update(self.special_tokens_map      )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,						**_snake_case      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "lower newer"
								_SCREAMING_SNAKE_CASE				= "lower newer"
								return input_text, output_text




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class(self.vocab_file					,						self.merges_file					,						**self.special_tokens_map      )
								_SCREAMING_SNAKE_CASE				= "lower newer"
								_SCREAMING_SNAKE_CASE				= ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize(_snake_case      )  # , add_prefix_space=True)
								self.assertListEqual(_snake_case					,						_snake_case      )

								_SCREAMING_SNAKE_CASE				= tokens + [tokenizer.unk_token]
								_SCREAMING_SNAKE_CASE				= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case      )					,						_snake_case      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()

								self.assertListEqual(tokenizer.encode("Hello world!"					,						add_special_tokens=_snake_case      )					,						[0, 31_414, 232, 328, 2]      )
								self.assertListEqual(
								    tokenizer.encode("Hello world! cécé herlolip 418"					,						add_special_tokens=_snake_case      )					,						[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]					,						)




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=_snake_case      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode(
								    "sequence builders"					,						add_special_tokens=_snake_case					,						add_prefix_space=_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode(
								    "sequence builders"					,						"multi-sequence build"					,						add_special_tokens=_snake_case					,						add_prefix_space=_snake_case      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(_snake_case					,						_snake_case      )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()

								_SCREAMING_SNAKE_CASE				= "Encode this sequence."
								_SCREAMING_SNAKE_CASE				= tokenizer.byte_encoder[" ".encode("utf-8"      )[0]]

								# Testing encoder arguments
								_SCREAMING_SNAKE_CASE				= tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case					,						add_prefix_space=_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
								self.assertNotEqual(_snake_case					,						_snake_case      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case					,						add_prefix_space=_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
								self.assertEqual(_snake_case					,						_snake_case      )

								tokenizer.add_special_tokens({"bos_token": "<s>"}      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
								self.assertNotEqual(_snake_case					,						_snake_case      )

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

								_SCREAMING_SNAKE_CASE				= "Encode <mask> sequence"
								_SCREAMING_SNAKE_CASE				= "Encode <mask>sequence"

								_SCREAMING_SNAKE_CASE				= tokenizer.encode(_snake_case      )
								_SCREAMING_SNAKE_CASE				= encoded.index(_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
								self.assertEqual(_snake_case					,						_snake_case      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode(_snake_case      )
								_SCREAMING_SNAKE_CASE				= encoded.index(_snake_case      )
								_SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
								self.assertNotEqual(_snake_case					,						_snake_case      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(_snake_case					,						**_snake_case      )
																				_SCREAMING_SNAKE_CASE				= self.tokenizer_class.from_pretrained(_snake_case					,						**_snake_case      )
																				_SCREAMING_SNAKE_CASE				= "A, <mask> AllenNLP sentence."
																				_SCREAMING_SNAKE_CASE				= tokenizer_r.encode_plus(_snake_case					,						add_special_tokens=_snake_case					,						return_token_type_ids=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_p.encode_plus(_snake_case					,						add_special_tokens=_snake_case					,						return_token_type_ids=_snake_case      )

																				# 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"]      )					,						)

																				_SCREAMING_SNAKE_CASE				= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
																				_SCREAMING_SNAKE_CASE				= 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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )
																				self.assertSequenceEqual(tokens_r["input_ids"]					,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 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       (				self      ):


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

														_SCREAMING_SNAKE_CASE				= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
														_SCREAMING_SNAKE_CASE				= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

														self.assertEqual(pre_tokenizer_state["add_prefix_space"]					,						_snake_case      )

														self.assertEqual(post_processor_state["add_prefix_space"]					,						_snake_case      )
														self.assertEqual(post_processor_state["trim_offsets"]					,						_snake_case      )




		def     lowercase       (				self      ):


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																				_SCREAMING_SNAKE_CASE				= "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																				_SCREAMING_SNAKE_CASE				= F'{text_of_1_token} {text_of_1_token}'

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(0, len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(len(_snake_case      ) + 1, len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(0, len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(len(_snake_case      ) + 1, len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(0, len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(len(_snake_case      ), len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(0, len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(len(_snake_case      ), len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

																				_SCREAMING_SNAKE_CASE				= 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)),
																				# )

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(1, 1 + len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(1 + len(_snake_case      ) + 1, 1 + len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(0, 1 + len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(1 + len(_snake_case      ), 1 + len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

																				_SCREAMING_SNAKE_CASE				= self.rust_tokenizer_class.from_pretrained(
																				    _snake_case					,						use_fast=_snake_case					,						add_prefix_space=_snake_case					,						trim_offsets=_snake_case      )
																				_SCREAMING_SNAKE_CASE				= tokenizer_r(_snake_case					,						return_offsets_mapping=_snake_case					,						add_special_tokens=_snake_case      )
																				self.assertEqual(encoding.offset_mapping[0]					,						(0, 1 + len(_snake_case      ))      )
																				self.assertEqual(
																				    encoding.offset_mapping[1]					,						(1 + len(_snake_case      ), 1 + len(_snake_case      ) + 1 + len(_snake_case      ))					,						)

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:   int     =				{
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Optional[int]     =				["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)