import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	lowerCamelCase_				:		Union[str, Any]       						=       False


if is_vision_available():
	from PIL import Image
else:


	class 						a__							:



				@staticmethod
				def __SCREAMING_SNAKE_CASE							(						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    List[Any]:
									pass


@is_pipeline_test
@require_vision
class 						a__							(   unittest.TestCase		):
			A__				:   str								=			MODEL_FOR_VISION_2_SEQ_MAPPING
			A__				:   List[Any]								=			TF_MODEL_FOR_VISION_2_SEQ_MAPPING



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[Any]:
								__a							=   pipeline('image-to-text'      ,						model=UpperCAmelCase      ,						tokenizer=UpperCAmelCase      ,						image_processor=UpperCAmelCase    )
								__a							=   [
								    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    ),
								    './tests/fixtures/tests_samples/COCO/000000039769.png',
								]
								return pipe, examples



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[Any]:
								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        [{'generated_text': ANY(UpperCAmelCase    )}],
								        [{'generated_text': ANY(UpperCAmelCase    )}],
								    ]      ,						)



			@require_tf
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   pipeline('image-to-text'      ,						model='hf-internal-testing/tiny-random-vit-gpt2'      ,						framework='tf'    )
								__a							=   './tests/fixtures/tests_samples/COCO/000000039769.png'

								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        {
								            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
								        },
								    ]      ,						)

								__a							=   pipe([image, image]    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        [
								            {
								                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
								            }
								        ],
								        [
								            {
								                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
								            }
								        ],
								    ]      ,						)

								__a							=   pipe(UpperCAmelCase      ,						max_new_tokens=1    )
								self.assertEqual(
								    UpperCAmelCase      ,						[{'generated_text': 'growth'}]      ,						)



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   pipeline('image-to-text'      ,						model='hf-internal-testing/tiny-random-vit-gpt2'    )
								__a							=   './tests/fixtures/tests_samples/COCO/000000039769.png'

								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        {
								            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
								        },
								    ]      ,						)

								__a							=   pipe([image, image]    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        [
								            {
								                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
								            }
								        ],
								        [
								            {
								                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
								            }
								        ],
								    ]      ,						)



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   pipeline('image-to-text'      ,						model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'    )
								__a							=   './tests/fixtures/tests_samples/COCO/000000039769.png'
								__a							=   'a photo of'

								__a							=   pipe(UpperCAmelCase      ,						prompt=UpperCAmelCase    )
								self.assertTrue(outputs[0]['generated_text'].startswith(UpperCAmelCase    )    )



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   pipeline('image-to-text'      ,						model='ydshieh/vit-gpt2-coco-en'    )
								__a							=   './tests/fixtures/tests_samples/COCO/000000039769.png'

								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]    )

								__a							=   pipe([image, image]    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
								        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
								    ]      ,						)



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   pipeline('image-to-text'      ,						model='Salesforce/blip-image-captioning-base'    )
								__a							=   'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
								__a							=   Image.open(requests.get(UpperCAmelCase      ,						stream=UpperCAmelCase    ).raw    )

								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]    )



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   pipeline('image-to-text'      ,						model='microsoft/git-base-coco'    )
								__a							=   'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
								__a							=   Image.open(requests.get(UpperCAmelCase      ,						stream=UpperCAmelCase    ).raw    )

								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'a cartoon of a purple character.'}]    )



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   pipeline('image-to-text'      ,						model='Salesforce/blip-image-captioning-base'    )
								__a							=   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
								__a							=   Image.open(requests.get(UpperCAmelCase      ,						stream=UpperCAmelCase    ).raw    )

								__a							=   'a photography of'

								__a							=   pipe(UpperCAmelCase      ,						prompt=UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'a photography of a volcano'}]    )

								with self.assertRaises(UpperCAmelCase    ):
													__a							=   pipe([image, image]      ,						prompt=[prompt, prompt]    )



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								__a							=   pipeline('image-to-text'      ,						model='microsoft/git-base-coco'    )
								__a							=   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
								__a							=   Image.open(requests.get(UpperCAmelCase      ,						stream=UpperCAmelCase    ).raw    )

								__a							=   'a photo of a'

								__a							=   pipe(UpperCAmelCase      ,						prompt=UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]    )

								with self.assertRaises(UpperCAmelCase    ):
													__a							=   pipe([image, image]      ,						prompt=[prompt, prompt]    )



			@unittest.skipIf(
			    not is_torch_greater_or_equal_than_1_11      ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'    )
			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   pipeline('image-to-text'      ,						model='google/pix2struct-ai2d-base'    )
								__a							=   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
								__a							=   Image.open(requests.get(UpperCAmelCase      ,						stream=UpperCAmelCase    ).raw    )

								__a							=   'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

								__a							=   pipe(UpperCAmelCase      ,						prompt=UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'ash cloud'}]    )

								with self.assertRaises(UpperCAmelCase    ):
													__a							=   pipe([image, image]      ,						prompt=[prompt, prompt]    )







			@slow
			@require_tf
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   pipeline('image-to-text'      ,						model='ydshieh/vit-gpt2-coco-en'      ,						framework='tf'    )
								__a							=   './tests/fixtures/tests_samples/COCO/000000039769.png'

								__a							=   pipe(UpperCAmelCase    )
								self.assertEqual(UpperCAmelCase      ,						[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]    )

								__a							=   pipe([image, image]    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
								        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
								    ]      ,						)





def 							lowerCAmelCase(	__lowerCamelCase = 10**6						):

					if n <= 0:
										raise ValueError('Please enter an integer greater than 0'						)

					__a							=   list(range(n + 1						)						)
					for number in range(2      ,				n + 1						):
										if phi[number] == number:
															phi[number] -= 1
															for multiple in range(number * 2      ,				n + 1      ,				__lowerCamelCase						):
																				__a							=   (phi[multiple] // number) * (number - 1)

					__a							=   1
					for number in range(1      ,				n + 1						):
										if (answer / phi[answer]) < (number / phi[number]):
															__a							=   number

					return answer


if __name__ == "__main__":
	print(solution())




