






"""simple docstring"""



import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 			a	(  unittest.TestCase      ):







    @cached_property
    def      UpperCamelCase_ (     self			):
      return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)







    @cached_property
    def      UpperCamelCase_ (     self			):
      return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.tokenizer_ab
      lowercase				       =			' I am a small frog.'
      lowercase				       =			tok([src_text]				,						padding=_lowerCamelCase				,						truncation=_lowerCamelCase			)['input_ids']
      lowercase				       =			tok.batch_decode(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase				,						clean_up_tokenization_spaces=_lowerCamelCase			)[0]
      assert src_text == decoded







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.rust_tokenizer_ab
      lowercase				       =			' I am a small frog.'
      lowercase				       =			tok([src_text]				,						padding=_lowerCamelCase				,						truncation=_lowerCamelCase			)['input_ids']
      lowercase				       =			tok.batch_decode(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase				,						clean_up_tokenization_spaces=_lowerCamelCase			)[0]
      assert src_text == decoded







    def      UpperCamelCase_ (     self			):
      assert self.tokenizer_ab.add_prefix_space
      assert self.tokenizer_ab([' Sam', 'Sam']			).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]







    def      UpperCamelCase_ (     self			):
      assert self.rust_tokenizer_ab.add_prefix_space
      assert self.rust_tokenizer_ab([' Sam', 'Sam']			).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]









'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase					:						str					     =							{
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							['ErnieMTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Union[str, Any]					     =							[
				    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ErnieMForMultipleChoice',
				    'ErnieMForQuestionAnswering',
				    'ErnieMForSequenceClassification',
				    'ErnieMForTokenClassification',
				    'ErnieMModel',
				    'ErnieMPreTrainedModel',
				    'ErnieMForInformationExtraction',
				]


if TYPE_CHECKING:
				from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ernie_m import ErnieMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ernie_m import (
								    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ErnieMForInformationExtraction,
								    ErnieMForMultipleChoice,
								    ErnieMForQuestionAnswering,
								    ErnieMForSequenceClassification,
								    ErnieMForTokenClassification,
								    ErnieMModel,
								    ErnieMPreTrainedModel,
								)


else:
				import sys

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

