import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class       __snake_case (  unittest.TestCase       , lowerCamelCase__ ):
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=load_tool('''document-question-answering'''    )
       self.tool.setup()
       UpperCAmelCase     :			Tuple     	=load_tool('''document-question-answering'''		, remote=lowerCamelCase__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=load_dataset('''hf-internal-testing/example-documents'''		, split='''test'''    )
       UpperCAmelCase     :			Union[str, Any]     	=dataset[0]['image']

       UpperCAmelCase     :			Optional[Any]     	=self.tool(lowerCamelCase__		, '''When is the coffee break?'''    )
       self.assertEqual(lowerCamelCase__		, '''11-14 to 11:39 a.m.'''    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=load_dataset('''hf-internal-testing/example-documents'''		, split='''test'''    )
       UpperCAmelCase     :			Dict     	=dataset[0]['image']

       UpperCAmelCase     :			Optional[int]     	=self.remote_tool(lowerCamelCase__		, '''When is the coffee break?'''    )
       self.assertEqual(lowerCamelCase__		, '''11-14 to 11:39 a.m.'''    )
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=load_dataset('''hf-internal-testing/example-documents'''		, split='''test'''    )
       UpperCAmelCase     :			Optional[Any]     	=dataset[0]['image']

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






 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=load_dataset('''hf-internal-testing/example-documents'''		, split='''test'''    )
       UpperCAmelCase     :			Any     	=dataset[0]['image']

       UpperCAmelCase     :			List[str]     	=self.remote_tool(document=lowerCamelCase__		, question='''When is the coffee break?'''    )
       self.assertEqual(lowerCamelCase__		, '''11-14 to 11:39 a.m.'''    )








import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

