



"""simple docstring"""






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							TransfoXLTokenizer
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								super().setUp()

								lowerCamelCase__	:	Tuple				=[
								    '''<unk>''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''want''',
								    '''unwanted''',
								    '''wa''',
								    '''un''',
								    '''running''',
								    ''',''',
								    '''low''',
								    '''l''',
								]
								lowerCamelCase__	:	Any				=os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''vocab_file''']  )
								with open(self.vocab_file,						'''w''',						encoding='''utf-8'''  ) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )



					def 	snake_case ( self							:						List[str],						**lowerCamelCase							:						Any  )->      List[str]:
								lowerCamelCase__	:	Tuple				=True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase  )



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Tuple  )->      Optional[int]:
								lowerCamelCase__	:	Optional[int]				='''<unk> UNwanted , running'''
								lowerCamelCase__	:	Dict				='''<unk> unwanted, running'''
								return input_text, output_text



					def 	snake_case ( self							:						str  )->      Optional[int]:
								lowerCamelCase__	:	Optional[Any]				=TransfoXLTokenizer(vocab_file=self.vocab_file,						lower_case=lowerCamelCase  )

								lowerCamelCase__	:	Any				=tokenizer.tokenize('''<unk> UNwanted , running'''  )
								self.assertListEqual(lowerCamelCase,						['''<unk>''', '''unwanted''', ''',''', '''running''']  )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),						[0, 4, 8, 7]  )



					def 	snake_case ( self							:						List[str]  )->      List[str]:
								lowerCamelCase__	:	Dict				=TransfoXLTokenizer(lower_case=lowerCamelCase  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''  ),						['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )



					def 	snake_case ( self							:						Dict  )->      Optional[int]:
								lowerCamelCase__	:	Optional[Any]				=TransfoXLTokenizer(lower_case=lowerCamelCase  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''  ),						['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )



					def 	snake_case ( self							:						Any  )->      List[Any]:
								lowerCamelCase__	:	Optional[Any]				=TransfoXLTokenizer(lower_case=lowerCamelCase  )
								lowerCamelCase__	:	Dict				='''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
								lowerCamelCase__	:	Dict				=[
								    '''Hello''',
								    '''(''',
								    '''bracket''',
								    ''')''',
								    '''and''',
								    '''side''',
								    '''@-@''',
								    '''scrolled''',
								    '''[''',
								    '''and''',
								    ''']''',
								    '''Henry''',
								    '''\'s''',
								    '''$''',
								    '''5''',
								    '''@,@''',
								    '''000''',
								    '''with''',
								    '''3''',
								    '''@.@''',
								    '''34''',
								    '''m''',
								    '''.''',
								    '''What''',
								    '''\'s''',
								    '''up''',
								    '''!''',
								    '''?''',
								]

								self.assertListEqual(tokenizer.tokenize(lowerCamelCase  ),						lowerCamelCase  )

								self.assertEqual(tokenizer.convert_tokens_to_string(lowerCamelCase  ),						lowerCamelCase  )




					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	List[Any]				=self.get_tokenizer()
								lowerCamelCase__	:	Optional[int]				=len(lowerCamelCase  )

								tokenizer.add_tokens(['''new1''', '''new2''']  )
								tokenizer.move_added_token('''new1''',						1  )

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(lowerCamelCase  ),						original_len + 2  )
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode('''new1'''  ),						[1]  )
								self.assertEqual(tokenizer.decode([1]  ),						'''new1'''  )









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

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




