






"""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 						_a  (		unittest.TestCase							,	lowercase_       ):





   '''simple docstring'''






   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     load_tool("image-question-answering")
          self.tool.setup()
          lowercase__:							Dict         =     load_tool("image-question-answering"					, remote=UpperCAmelCase_)





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							List[Any]         =     Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
          lowercase__:							List[Any]         =     self.tool(UpperCAmelCase_					, "How many cats are sleeping on the couch?")
          self.assertEqual(UpperCAmelCase_					, "2")





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Any         =     Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
          lowercase__:							Tuple         =     self.remote_tool(UpperCAmelCase_					, "How many cats are sleeping on the couch?")
          self.assertEqual(UpperCAmelCase_					, "2")





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




          '''simple docstring'''




          lowercase__:							Optional[int]         =     Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
          lowercase__:							List[Any]         =     self.tool(image=UpperCAmelCase_					, question="How many cats are sleeping on the couch?")
          self.assertEqual(UpperCAmelCase_					, "2")





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							List[str]         =     Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png")
          lowercase__:							str         =     self.remote_tool(image=UpperCAmelCase_					, question="How many cats are sleeping on the couch?")
          self.assertEqual(UpperCAmelCase_					, "2")







from typing import TYPE_CHECKING

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


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
