






'''simple docstring'''







from typing import TYPE_CHECKING

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


_A      :     List[Any]       	={
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     List[str]       	=['''DebertaV2TokenizerFast''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Any       	=[
			    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFDebertaV2ForMaskedLM''',
			    '''TFDebertaV2ForQuestionAnswering''',
			    '''TFDebertaV2ForSequenceClassification''',
			    '''TFDebertaV2ForTokenClassification''',
			    '''TFDebertaV2Model''',
			    '''TFDebertaV2PreTrainedModel''',
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     int       	=[
			    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''DebertaV2ForMaskedLM''',
			    '''DebertaV2ForMultipleChoice''',
			    '''DebertaV2ForQuestionAnswering''',
			    '''DebertaV2ForSequenceClassification''',
			    '''DebertaV2ForTokenClassification''',
			    '''DebertaV2Model''',
			    '''DebertaV2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

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









'''simple docstring'''







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
			from PIL import Image

class _lowercase      (							unittest.TestCase							, _lowercase						):




							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      Optional[Any]			    =			load_tool("""image-captioning"""	)
														self.tool.setup()
														lowerCamelCase__   :      Optional[Any]			    =			load_tool("""image-captioning"""     ,				remote=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							int	):
														lowerCamelCase__   :      List[Any]			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      Optional[Any]			    =			self.tool(UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      Tuple			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      Dict			    =			self.remote_tool(UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)




							def 						lowerCamelCase_							(				self:							str	):
														lowerCamelCase__   :      Optional[int]			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      str			    =			self.tool(image=UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      Optional[int]			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      Optional[int]			    =			self.remote_tool(image=UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)

