
"""simple docstring"""






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 					__a     ( __a					,   unittest.TestCase       ):



							'''simple docstring'''







							_lowerCamelCase					:			Optional[int]          =   MgpstrTokenizer
							_lowerCamelCase					:			Dict          =   False
							_lowerCamelCase					:			List[str]          =   {}
							_lowerCamelCase					:			str          =   False
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										super().setUp()

										# fmt: off
										__lowercase       =       ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
										# fmt: on
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )

										__lowercase       =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["vocab_file"]       )
										with open(self.vocab_file  ,    "w"  ,    encoding="utf-8"       ) as fp:
													fp.write(json.dumps(_lowerCamelCase       ) + "\n"       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,    **_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       "tester"
										__lowercase       =       "tester"
										return input_text, output_text
							@unittest.skip("MGP-STR always lower cases letters."       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										pass
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       self.get_tokenizers(do_lower_case=_lowerCamelCase       )
										for tokenizer in tokenizers:
													with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
																__lowercase       =       "[SPECIAL_TOKEN]"

																tokenizer.add_special_tokens({"cls_token": special_token}       )
																__lowercase       =       tokenizer.encode([special_token]  ,    add_special_tokens=_lowerCamelCase       )
																self.assertEqual(len(_lowerCamelCase       )  ,    1       )

																__lowercase       =       tokenizer.decode(_lowerCamelCase  ,    skip_special_tokens=_lowerCamelCase       )
																self.assertTrue(special_token not in decoded       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
																__lowercase   ,					__lowercase       =       self.get_input_output_texts(_lowerCamelCase       )

																__lowercase       =       tokenizer.tokenize(_lowerCamelCase       )
																__lowercase       =       tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
																__lowercase       =       tokenizer.encode(_lowerCamelCase  ,    add_special_tokens=_lowerCamelCase       )
																self.assertListEqual(_lowerCamelCase  ,    _lowerCamelCase       )

																__lowercase       =       tokenizer.convert_ids_to_tokens(_lowerCamelCase       )
																self.assertNotEqual(len(_lowerCamelCase       )  ,    0       )
																__lowercase       =       tokenizer.decode(_lowerCamelCase       )
																self.assertIsInstance(_lowerCamelCase  ,    _lowerCamelCase       )

																self.assertEqual(text_a.replace(" "  ,    ""       )  ,    _lowerCamelCase       )
							@unittest.skip("MGP-STR tokenizer only handles one sequence."       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										pass





							@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										pass







"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





