




'''simple docstring'''






import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class    A						(					unittest.TestCase     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				def 			a_			(self     )      ->			Optional[int]:
							__UpperCamelCase :     str										=  load_tool("document-question-answering"     )
							self.tool.setup()
							__UpperCamelCase :     str										=  load_tool("document-question-answering"					,    remote=lowerCAmelCase__     )
				def 			a_			(self     )      ->			Optional[Any]:
							__UpperCamelCase :     Tuple										=  load_dataset("hf-internal-testing/example-documents"					,    split="test"     )
							__UpperCamelCase :     Dict										=  dataset[0]["image"]

							__UpperCamelCase :     Tuple										=  self.tool(lowerCAmelCase__					,    "When is the coffee break?"     )
							self.assertEqual(lowerCAmelCase__					,    "11-14 to 11:39 a.m."     )
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase :     str										=  load_dataset("hf-internal-testing/example-documents"					,    split="test"     )
							__UpperCamelCase :     Any										=  dataset[0]["image"]

							__UpperCamelCase :     Union[str, Any]										=  self.remote_tool(lowerCAmelCase__					,    "When is the coffee break?"     )
							self.assertEqual(lowerCAmelCase__					,    "11-14 to 11:39 a.m."     )
				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Union[str, Any]										=  load_dataset("hf-internal-testing/example-documents"					,    split="test"     )
							__UpperCamelCase :     Union[str, Any]										=  dataset[0]["image"]

							self.tool(document=lowerCAmelCase__					,    question="When is the coffee break?"     )





				def 			a_			(self     )      ->			Dict:
							__UpperCamelCase :     Any										=  load_dataset("hf-internal-testing/example-documents"					,    split="test"     )
							__UpperCamelCase :     Union[str, Any]										=  dataset[0]["image"]

							__UpperCamelCase :     Optional[int]										=  self.remote_tool(document=lowerCAmelCase__					,    question="When is the coffee break?"     )
							self.assertEqual(lowerCAmelCase__					,    "11-14 to 11:39 a.m."     )









"""simple docstring"""
from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE						 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



