






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowerCamelCase_     (	a_ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			BertweetTokenizer
							SCREAMING_SNAKE_CASE_						     =			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								a		=			['''I''', '''m''', '''V@@''', '''R@@''', '''r''', '''e@@''']
								a		=			dict(zip(A_							,range(len(A_					)					)					)					)
								a		=			['''#version: 0.2''', '''a m</w>''']
								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:
									for token in vocab_tokens:
										fp.write(F"""{token} {vocab_tokens[token]}\n"""					)
								with open(self.merges_file							,'''w'''							,encoding='''utf-8'''					) as fp:
									fp.write('''\n'''.join(A_					)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,**__lowerCamelCase		:					Any					):





								'''simple docstring'''



								kwargs.update(self.special_tokens_map					)
								return BertweetTokenizer.from_pretrained(self.tmpdirname							,**A_					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					Any					):





								'''simple docstring'''



								a		=			'''I am VinAI Research'''
								a		=			'''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
								return input_text, output_text




							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			BertweetTokenizer(self.vocab_file							,self.merges_file							,**self.special_tokens_map					)
								a		=			'''I am VinAI Research'''
								a		=			'''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
								a		=			tokenizer.tokenize(A_					)
								self.assertListEqual(A_							,A_					)

								a		=			tokens + [tokenizer.unk_token]

								a		=			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A_					)							,A_					)













from typing import TYPE_CHECKING

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


UpperCamelCase__						:      Tuple								=					{
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					[
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__						:      str								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





