


'''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     _lowerCamelCase       (     snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Dict     				=    BlenderbotSmallTokenizer
 __lowercase					:		Any     				=    False


 def 			snake_case__ (   self    ):

  """simple docstring"""







  super().setUp()

  __A	:		Optional[Any]	  =      ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
  __A	:		List[Any]	  =      dict(zip(__lowercase   ,   range(len(__lowercase    )    )    )    )

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

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


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      'adapt act apte'
  __A	:		List[Any]	  =      'adapt act apte'
  return input_text, output_text


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      BlenderbotSmallTokenizer(self.vocab_file   ,   self.merges_file   ,   **self.special_tokens_map    )
  __A	:		Any	  =      'adapt act apte'
  __A	:		Optional[int]	  =      ['adapt', 'act', 'ap@@', 'te']
  __A	:		Optional[int]	  =      tokenizer.tokenize(__lowercase    )
  self.assertListEqual(__lowercase   ,   __lowercase    )

  __A	:		Any	  =      [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

  __A	:		int	  =      [0, 1, 2, 3, 4, 5]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase    )   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







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







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'    )
  __A	:		List[Any]	  =      'I am a small frog .'
  __A	:		Tuple	  =      '.'
  __A	:		Any	  =      tok(__lowercase    )['input_ids']
  __A	:		str	  =      tok(__lowercase    )['input_ids']

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










'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






