



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 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					str		      =		BlenderbotSmallTokenizer
       UpperCamelCase__						:					Optional[int]		      =		False



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           super().setUp()

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

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

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



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       '''adapt act apte'''
           __a		 =       '''adapt act apte'''
           return input_text, output_text



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       BlenderbotSmallTokenizer(self.vocab_file  ,	self.merges_file  ,	**self.special_tokens_map)
           __a		 =       '''adapt act apte'''
           __a		 =       ['''adapt''', '''act''', '''ap@@''', '''te''']
           __a		 =       tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

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

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



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
           assert tok('''sam''').input_ids == [1_384]
           __a		 =       '''I am a small frog.'''
           __a		 =       tok([src_text]  ,	padding=__SCREAMING_SNAKE_CASE  ,	truncation=__SCREAMING_SNAKE_CASE)['''input_ids''']
           __a		 =       tok.batch_decode(__SCREAMING_SNAKE_CASE  ,	skip_special_tokens=__SCREAMING_SNAKE_CASE  ,	clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE)[0]
           assert src_text != decoded  # I wish it did!
           assert decoded == "i am a small frog ."





       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
           __a		 =       '''I am a small frog .'''
           __a		 =       '''.'''
           __a		 =       tok(__SCREAMING_SNAKE_CASE)['''input_ids''']
           __a		 =       tok(__SCREAMING_SNAKE_CASE)['''input_ids''']

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










from typing import TYPE_CHECKING

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


__snake_case		:Union[str, Any]   	=  {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:List[str]   	=  ['''ViTFeatureExtractor''']
 __snake_case		:Optional[Any]   	=  ['''ViTImageProcessor''']

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

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

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''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

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





