"""simple docstring"""






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = BlenderbotSmallTokenizer
					snake_case__				    = False







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									super().setUp()

									UpperCAmelCase__             =							['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
									UpperCAmelCase__             =							dict(zip(lowerCamelCase__    ,range(len(lowerCamelCase__			)			)			)			)

									UpperCAmelCase__             =							['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
									UpperCAmelCase__             =							{'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

									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:
													fp.write(json.dumps(lowerCamelCase__			) + '\n'			)
									with open(self.merges_file    ,'w'    ,encoding='utf-8'			) as fp:
													fp.write('\n'.join(lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,**lowerCamelCase__   :	Any			):
									kwargs.update(self.special_tokens_map			)
									return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	Optional[int]			):
									UpperCAmelCase__             =							'adapt act apte'
									UpperCAmelCase__             =							'adapt act apte'
									return input_text, output_text







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							BlenderbotSmallTokenizer(self.vocab_file    ,self.merges_file    ,**self.special_tokens_map			)
									UpperCAmelCase__             =							'adapt act apte'
									UpperCAmelCase__             =							['adapt', 'act', 'ap@@', 'te']
									UpperCAmelCase__             =							tokenizer.tokenize(lowerCamelCase__			)
									self.assertListEqual(lowerCamelCase__    ,lowerCamelCase__			)

									UpperCAmelCase__             =							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

									UpperCAmelCase__             =							[0, 1, 2, 3, 4, 5]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__			)    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'			)
									assert tok('sam'			).input_ids == [1_384]
									UpperCAmelCase__             =							'I am a small frog.'
									UpperCAmelCase__             =							tok([src_text]    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__			)['input_ids']
									UpperCAmelCase__             =							tok.batch_decode(lowerCamelCase__    ,skip_special_tokens=lowerCamelCase__    ,clean_up_tokenization_spaces=lowerCamelCase__			)[0]
									assert src_text != decoded  # I wish it did!
									assert decoded == "i am a small frog ."







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'			)
									UpperCAmelCase__             =							'I am a small frog .'
									UpperCAmelCase__             =							'.'
									UpperCAmelCase__             =							tok(lowerCamelCase__			)['input_ids']
									UpperCAmelCase__             =							tok(lowerCamelCase__			)['input_ids']

									assert encoded[-1] == encoded_dot[0]




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    '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

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



