
from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE     :   Optional[int]							 =      {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

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

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

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    '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

					_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

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 			UpperCamelCase__		(     unittest.TestCase,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					A__	:						Dict								=				load_tool('''image-question-answering'''		)
					self.tool.setup()
					A__	:						List[Any]								=				load_tool('''image-question-answering'''					,							remote=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__	:						str								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						List[str]								=				self.tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						Dict								=				self.remote_tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Dict								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						int								=				self.tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						str								=				self.remote_tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)