






import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class 				_UpperCAmelCase (	unittest.TestCase		,A__     ):

				"""simple docstring"""




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      load_tool('''document-question-answering'''  )
								self.tool.setup()
								lowercase__       			=      load_tool('''document-question-answering''',  remote=lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								lowercase__       			=      self.tool(lowerCamelCase,  '''When is the coffee break?'''  )
								self.assertEqual(lowerCamelCase,  '''11-14 to 11:39 a.m.'''  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								lowercase__       			=      self.remote_tool(lowerCamelCase,  '''When is the coffee break?'''  )
								self.assertEqual(lowerCamelCase,  '''11-14 to 11:39 a.m.'''  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								self.tool(document=lowerCamelCase,  question='''When is the coffee break?'''  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								lowercase__       			=      self.remote_tool(document=lowerCamelCase,  question='''When is the coffee break?'''  )
								self.assertEqual(lowerCamelCase,  '''11-14 to 11:39 a.m.'''  )












from typing import TYPE_CHECKING

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


A__     :      int             =			{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Tuple             =			['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

							A__     :      Dict             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




