



'''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__				(			a_	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     LongformerTokenizer
      UpperCamelCase__			 =     True
      UpperCamelCase__			 =     LongformerTokenizerFast
      UpperCamelCase__			 =     True




      def lowerCAmelCase_					(       self							:     Any   ):
             super().setUp()

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

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




      def lowerCAmelCase_					(       self							:     Tuple			,**a__							:     Tuple   ):
             kwargs.update(self.special_tokens_map   )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,**a__   )




      def lowerCAmelCase_					(       self							:     Optional[int]			,a__							:     Union[str, Any]   ):
             a__								= "lower newer"
             a__								= "lower newer"
             return input_text, output_text




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

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




      def lowerCAmelCase_					(       self							:     Tuple   ):
             a__								= 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 lowerCAmelCase_					(       self							:     Dict   ):
             a__								= self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"   )

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

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

             a__								= tokenizer.build_inputs_with_special_tokens(a__   )
             a__								= tokenizer.build_inputs_with_special_tokens(a__			,a__   )

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode




      def lowerCAmelCase_					(       self							:     Tuple   ):
             a__								= self.get_tokenizer()

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

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

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

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

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

             a__								= "Encode <mask> sequence"
             a__								= "Encode <mask>sequence"

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

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




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             pass




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







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                           a__								= self.rust_tokenizer_class.from_pretrained(a__			,**a__   )
                           a__								= self.tokenizer_class.from_pretrained(a__			,**a__   )
                           a__								= "A, <mask> AllenNLP sentence."
                           a__								= tokenizer_r.encode_plus(a__			,add_special_tokens=a__			,return_token_type_ids=a__   )
                           a__								= 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__								= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
                           a__								= 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 lowerCAmelCase_					(       self							:     str   ):







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

                    a__								= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
                    a__								= 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 lowerCAmelCase_					(       self							:     Union[str, Any]   ):







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

                           a__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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__								= 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__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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__								= self.rust_tokenizer_class.from_pretrained(
                               a__			,use_fast=a__			,add_prefix_space=a__			,trim_offsets=a__   )
                           a__								= 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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase   :    Any     =		{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Union[str, Any]     =		[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		UpperCAmelCase   :    Tuple     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

