






'''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			,							_lowerCamelCase   ):


    '''simple docstring'''

    def  a__					(       self  :  Optional[Any]  )				->				Any:

     '''simple docstring'''




     lowerCamelCase__	=  load_tool("image-question-answering"  )
     self.tool.setup()
     lowerCamelCase__	=  load_tool("image-question-answering"					,  remote=__lowerCamelCase  )

    def  a__					(       self  :  Optional[Any]  )				->				str:

     '''simple docstring'''




     lowerCamelCase__	=  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
     lowerCamelCase__	=  self.tool(__lowerCamelCase					,  "How many cats are sleeping on the couch?"  )
     self.assertEqual(__lowerCamelCase					,  "2"  )

    def  a__					(       self  :  int  )				->				Dict:

     '''simple docstring'''




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

    def  a__					(       self  :  Dict  )				->				int:

     '''simple docstring'''




     lowerCamelCase__	=  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
     lowerCamelCase__	=  self.tool(image=__lowerCamelCase					,  question="How many cats are sleeping on the couch?"  )
     self.assertEqual(__lowerCamelCase					,  "2"  )




    def  a__					(       self  :  Tuple  )				->				int:

     '''simple docstring'''




     lowerCamelCase__	=  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
     lowerCamelCase__	=  self.remote_tool(image=__lowerCamelCase					,  question="How many cats are sleeping on the couch?"  )
     self.assertEqual(__lowerCamelCase					,  "2"  )









'''simple docstring'''





from typing import TYPE_CHECKING

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


__A				:				Union[str, Any]					=		{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""GPT2TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[int]					=		[
     """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPT2DoubleHeadsModel""",
     """GPT2ForQuestionAnswering""",
     """GPT2ForSequenceClassification""",
     """GPT2ForTokenClassification""",
     """GPT2LMHeadModel""",
     """GPT2Model""",
     """GPT2PreTrainedModel""",
     """load_tf_weights_in_gpt2""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[Any]					=		[
     """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFGPT2DoubleHeadsModel""",
     """TFGPT2ForSequenceClassification""",
     """TFGPT2LMHeadModel""",
     """TFGPT2MainLayer""",
     """TFGPT2Model""",
     """TFGPT2PreTrainedModel""",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Any					=		["""TFGPT2Tokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 __A				:				Tuple					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

