






import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:



 class 							_UpperCamelCase			:


       @staticmethod
       def  __UpperCAmelCase		(	*__UpperCamelCase					,						**__UpperCamelCase	)->	Optional[int]:
            pass



@is_pipeline_test
@require_torch
@require_vision
class 							_UpperCamelCase			(unittest.TestCase			):
      snake_case_  =							MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Any:
           __lowerCAmelCase								=					pipeline("visual-question-answering"					,						model="hf-internal-testing/tiny-vilt-random-vqa"	)
           __lowerCAmelCase								=					[
               {
                   "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	),
                   "question": "How many cats are there?",
               },
               {
                   "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                   "question": "How many cats are there?",
               },
           ]
           return vqa_pipeline, examples


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					vqa_pipeline(__UpperCamelCase					,						top_k=1	)
           self.assertEqual(
               __UpperCamelCase					,						[
                   [{"score": ANY(__UpperCamelCase	), "answer": ANY(__UpperCamelCase	)}],
                   [{"score": ANY(__UpperCamelCase	), "answer": ANY(__UpperCamelCase	)}],
               ]					,						)


      @require_torch
      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					pipeline("visual-question-answering"					,						model="hf-internal-testing/tiny-vilt-random-vqa"	)
           __lowerCAmelCase								=					"./tests/fixtures/tests_samples/COCO/000000039769.png"
           __lowerCAmelCase								=					"How many cats are there?"

           __lowerCAmelCase								=					vqa_pipeline(image=__UpperCamelCase					,						question="How many cats are there?"					,						top_k=2	)
           self.assertEqual(
               __UpperCamelCase					,						[{"score": ANY(__UpperCamelCase	), "answer": ANY(__UpperCamelCase	)}, {"score": ANY(__UpperCamelCase	), "answer": ANY(__UpperCamelCase	)}]	)

           __lowerCAmelCase								=					vqa_pipeline({"image": image, "question": question}					,						top_k=2	)
           self.assertEqual(
               __UpperCamelCase					,						[{"score": ANY(__UpperCamelCase	), "answer": ANY(__UpperCamelCase	)}, {"score": ANY(__UpperCamelCase	), "answer": ANY(__UpperCamelCase	)}]	)


      @slow
      @require_torch
      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					pipeline("visual-question-answering"					,						model="dandelin/vilt-b32-finetuned-vqa"	)
           __lowerCAmelCase								=					"./tests/fixtures/tests_samples/COCO/000000039769.png"
           __lowerCAmelCase								=					"How many cats are there?"

           __lowerCAmelCase								=					vqa_pipeline(image=__UpperCamelCase					,						question=__UpperCamelCase					,						top_k=2	)
           self.assertEqual(
               nested_simplify(__UpperCamelCase					,						decimals=4	)					,						[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]	)

           __lowerCAmelCase								=					vqa_pipeline({"image": image, "question": question}					,						top_k=2	)
           self.assertEqual(
               nested_simplify(__UpperCamelCase					,						decimals=4	)					,						[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]	)

           __lowerCAmelCase								=					vqa_pipeline(
               [{"image": image, "question": question}, {"image": image, "question": question}]					,						top_k=2	)
           self.assertEqual(
               nested_simplify(__UpperCamelCase					,						decimals=4	)					,						[[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2					,						)


      @require_tf
      @unittest.skip("Visual question answering not implemented in TF"	)
      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           pass














import os
import sys
import unittest


lowerCamelCase			:					int         =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:					Any         =	os.path.join(git_repo_path, '''src''', '''diffusers''')



class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					find_backend("    if not is_torch_available():"	)
           self.assertEqual(__UpperCamelCase					,						"torch"	)

           # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
           # self.assertEqual(backend_with_underscore, "tensorflow_text")

           __lowerCAmelCase								=					find_backend("    if not (is_torch_available() and is_transformers_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers"	)

           # double_backend_with_underscore = find_backend(
           #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
           # )
           # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

           __lowerCAmelCase								=					find_backend(
               "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers_and_onnx"	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("torch"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("flax_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers_and_onnx"					,						__UpperCamelCase	)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("UNet2DModel"					,						objects["torch"]	)
           self.assertIn("FlaxUNet2DConditionModel"					,						objects["flax"]	)
           self.assertIn("StableDiffusionPipeline"					,						objects["torch_and_transformers"]	)
           self.assertIn("FlaxStableDiffusionPipeline"					,						objects["flax_and_transformers"]	)
           self.assertIn("LMSDiscreteScheduler"					,						objects["torch_and_scipy"]	)
           self.assertIn("OnnxStableDiffusionPipeline"					,						objects["torch_and_transformers_and_onnx"]	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					create_dummy_object("CONSTANT"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						"\nCONSTANT = None\n"	)

           __lowerCAmelCase								=					create_dummy_object("function"					,						"'torch'"	)
           self.assertEqual(
               __UpperCamelCase					,						"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"	)

           __lowerCAmelCase								=					"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
           __lowerCAmelCase								=					create_dummy_object("FakeClass"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
           __lowerCAmelCase								=					create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}	)
           self.assertEqual(dummy_files["torch"]					,						__UpperCamelCase	)






