


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				):
					'''simple docstring'''






					A__     =					BlenderbotSmallTokenizer
					A__     =					False

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Dict:




							"""simple docstring"""


							super().setUp()

							lowercase__	     =						["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
							lowercase__	     =						dict(zip(_UpperCAmelCase ,   range(len(_UpperCAmelCase						)						)						)						)

							lowercase__	     =						["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
							lowercase__	     =						{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

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

					def     lowerCamelCase__	(self       :		Optional[int] ,   **_UpperCAmelCase       :		str						)		->  Tuple:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Union[str, Any]						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						"""adapt act apte"""
							lowercase__	     =						"""adapt act apte"""
							return input_text, output_text

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						BlenderbotSmallTokenizer(self.vocab_file ,   self.merges_file ,   **self.special_tokens_map						)
							lowercase__	     =						"""adapt act apte"""
							lowercase__	     =						["""adapt""", """act""", """ap@@""", """te"""]
							lowercase__	     =						tokenizer.tokenize(_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

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

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

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""						)
							assert tok("""sam"""						).input_ids == [1384]
							lowercase__	     =						"""I am a small frog."""
							lowercase__	     =						tok([src_text] ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						tok.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."


					def     lowerCamelCase__	(self       :		List[Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""						)
							lowercase__	     =						"""I am a small frog ."""
							lowercase__	     =						"""."""
							lowercase__	     =						tok(_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						tok(_UpperCAmelCase						)["""input_ids"""]

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








"""simple docstring"""


from typing import TYPE_CHECKING

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


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__magic_name__       :					Union[str, Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



