
import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class        SCREAMING_SNAKE_CASE__    (							UpperCamelCase_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  PhobertTokenizer
							_snake_case     		=  False



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													__UpperCamelCase       =			['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
													__UpperCamelCase       =			dict(zip(__a     ,	range(len(__a			)			)			)			)
													__UpperCamelCase       =			['#version: 0.2', 'l à</w>']
													__UpperCamelCase       =			{'unk_token': '<unk>'}

													__UpperCamelCase       =			os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['''vocab_file''']			)
													__UpperCamelCase       =			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     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													kwargs.update(self.special_tokens_map			)
													return PhobertTokenizer.from_pretrained(self.tmpdirname     ,	**__a			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			'Tôi là VinAI Research'
													__UpperCamelCase       =			'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
													return input_text, output_text



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			PhobertTokenizer(self.vocab_file     ,	self.merges_file     ,	**self.special_tokens_map			)
													__UpperCamelCase       =			'Tôi là VinAI Research'
													__UpperCamelCase       =			'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
													__UpperCamelCase       =			tokenizer.tokenize(__a			)
													print(__a			)
													self.assertListEqual(__a     ,	__a			)

													__UpperCamelCase       =			tokens + [tokenizer.unk_token]

													__UpperCamelCase       =			[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 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_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				lowercase__							:							List[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




