'''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       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Dict     =    BlenderbotSmallTokenizer
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        super().setUp()

        lowercase_					:       List[Any]     				=					['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
        lowercase_					:       Tuple     				=					dict(zip(_a       ,				range(len(_a				)				)				)				)

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

        lowercase_					:       int     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        lowercase_					:       Optional[Any]     				=					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(_a				) + '\n'				)
        with open(self.merges_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             fp.write('\n'.join(_a				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     List[Any]:
        kwargs.update(self.special_tokens_map				)
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        lowercase_					:       str     				=					'adapt act apte'
        lowercase_					:       Optional[Any]     				=					'adapt act apte'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					BlenderbotSmallTokenizer(self.vocab_file       ,				self.merges_file       ,				**self.special_tokens_map				)
        lowercase_					:       Any     				=					'adapt act apte'
        lowercase_					:       Union[str, Any]     				=					['adapt', 'act', 'ap@@', 'te']
        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize(_a				)
        self.assertListEqual(_a       ,				_a				)

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

        lowercase_					:       Tuple     				=					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       str     				=					BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'				)
        assert tok('sam'				).input_ids == [1_384]
        lowercase_					:       Any     				=					'I am a small frog.'
        lowercase_					:       Optional[Any]     				=					tok([src_text]       ,				padding=_a       ,				truncation=_a				)['input_ids']
        lowercase_					:       Dict     				=					tok.batch_decode(_a       ,				skip_special_tokens=_a       ,				clean_up_tokenization_spaces=_a				)[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."





   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Dict     				=					BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'				)
        lowercase_					:       Dict     				=					'I am a small frog .'
        lowercase_					:       Dict     				=					'.'
        lowercase_					:       List[str]     				=					tok(_a				)['input_ids']
        lowercase_					:       Optional[Any]     				=					tok(_a				)['input_ids']

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





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

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




