

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			,      lowerCamelCase_					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=load_tool('image-question-answering'    )
           self.tool.setup()
           SCREAMING_SNAKE_CASE										=load_tool('image-question-answering'      ,remote=snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
           SCREAMING_SNAKE_CASE										=self.tool(snake_case      ,'How many cats are sleeping on the couch?'    )
           self.assertEqual(snake_case      ,'2'    )
      def 	_lowerCAmelCase					(				self					:							Any    ):
           SCREAMING_SNAKE_CASE										=Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
           SCREAMING_SNAKE_CASE										=self.remote_tool(snake_case      ,'How many cats are sleeping on the couch?'    )
           self.assertEqual(snake_case      ,'2'    )
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
           SCREAMING_SNAKE_CASE										=self.tool(image=snake_case      ,question='How many cats are sleeping on the couch?'    )
           self.assertEqual(snake_case      ,'2'    )


      def 	_lowerCAmelCase					(				self					:							Any    ):
           SCREAMING_SNAKE_CASE										=Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
           SCREAMING_SNAKE_CASE										=self.remote_tool(image=snake_case      ,question='How many cats are sleeping on the couch?'    )
           self.assertEqual(snake_case      ,'2'    )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase								={
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=[
     "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMProphetNetDecoder",
     "XLMProphetNetEncoder",
     "XLMProphetNetForCausalLM",
     "XLMProphetNetForConditionalGeneration",
     "XLMProphetNetModel",
     "XLMProphetNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_prophetnet import (
      XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMProphetNetDecoder,
      XLMProphetNetEncoder,
      XLMProphetNetForCausalLM,
      XLMProphetNetForConditionalGeneration,
      XLMProphetNetModel,
      XLMProphetNetPreTrainedModel,
  )

else:
 import sys

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

