





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_		(			__a						,					unittest.TestCase):
		lowerCamelCase    :Optional[int]						=     BlenderbotSmallTokenizer
		lowerCamelCase    :Union[str, Any]						=     False







		def 			__lowercase   (		self   )				->   List[str]:
				super().setUp()

				lowerCamelCase   : Any								=['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
				lowerCamelCase   : Any								=dict(zip(snake_case__			,     range(len(snake_case__   )   )   )   )

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

				lowerCamelCase   : Tuple								=os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''vocab_file''']   )
				lowerCamelCase   : List[str]								=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(snake_case__   ) + '''\n'''   )
				with open(self.merges_file			,     '''w'''			,     encoding='''utf-8'''   ) as fp:
						fp.write('''\n'''.join(snake_case__   )   )







		def 			__lowercase   (		self			,     **__lowercase   )				->   Optional[Any]:
				kwargs.update(self.special_tokens_map   )
				return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,     **snake_case__   )







		def 			__lowercase   (		self			,     __lowercase   )				->   List[Any]:
				lowerCamelCase   : Optional[int]								='''adapt act apte'''
				lowerCamelCase   : Any								='''adapt act apte'''
				return input_text, output_text







		def 			__lowercase   (		self   )				->   Tuple:
				lowerCamelCase   : List[Any]								=BlenderbotSmallTokenizer(self.vocab_file			,     self.merges_file			,     **self.special_tokens_map   )
				lowerCamelCase   : List[Any]								='''adapt act apte'''
				lowerCamelCase   : Optional[Any]								=['''adapt''', '''act''', '''ap@@''', '''te''']
				lowerCamelCase   : Any								=tokenizer.tokenize(snake_case__   )
				self.assertListEqual(snake_case__			,     snake_case__   )

				lowerCamelCase   : List[Any]								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

				lowerCamelCase   : Any								=[0, 1, 2, 3, 4, 5]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__   )			,     snake_case__   )







		def 			__lowercase   (		self   )				->   Optional[int]:
				lowerCamelCase   : List[str]								=BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
				assert tok('''sam'''   ).input_ids == [1_3_8_4]
				lowerCamelCase   : int								='''I am a small frog.'''
				lowerCamelCase   : Optional[Any]								=tok([src_text]			,     padding=snake_case__			,     truncation=snake_case__   )['''input_ids''']
				lowerCamelCase   : Optional[int]								=tok.batch_decode(snake_case__			,     skip_special_tokens=snake_case__			,     clean_up_tokenization_spaces=snake_case__   )[0]
				assert src_text != decoded  # I wish it did!
				assert decoded == "i am a small frog ."







		def 			__lowercase   (		self   )				->   Tuple:
				lowerCamelCase   : Optional[Any]								=BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
				lowerCamelCase   : str								='''I am a small frog .'''
				lowerCamelCase   : Any								='''.'''
				lowerCamelCase   : Union[str, Any]								=tok(snake_case__   )['''input_ids''']
				lowerCamelCase   : Union[str, Any]								=tok(snake_case__   )['''input_ids''']

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








from typing import TYPE_CHECKING

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


snake_case_							=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

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

