

'''simple docstring'''

def      __magic_name__							(			A       , A				)      ->		List[Any]:
	snake_case							=					1
	while b > 0:
		if b & 1:
			res *= a

		a *= a
		b >>= 1

	return res

def      __magic_name__							(			A       , A       , A				)      ->		Optional[Any]:
	snake_case							=					1
	while b > 0:
		if b & 1:
			snake_case							=					((res % c) * (a % c)) % c

		a *= a
		b >>= 1

	return res








'''simple docstring'''

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_     =      False


if is_vision_available():
						from PIL import Image
else:







						class lowerCamelCase   :

										@staticmethod
										def 					_lowerCamelCase    (       *lowercase_,						**lowercase_ )    ->							List[Any]:
											pass







@is_pipeline_test
@require_vision
class lowerCamelCase   (    unittest.TestCase				):
				snake_case_							     = MODEL_FOR_VISION_2_SEQ_MAPPING
				snake_case_							     = TF_MODEL_FOR_VISION_2_SEQ_MAPPING

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

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Tuple:
					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': ANY(lowercase_ )}],
					        [{'generated_text': ANY(lowercase_ )}],
					    ],						)

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

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

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

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

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

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

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

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

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

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model='ydshieh/vit-gpt2-coco-en' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

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

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [{'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 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					pipeline('image-to-text',						model='Salesforce/blip-image-captioning-base' )
					snake_case							=					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

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

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					pipeline('image-to-text',						model='microsoft/git-base-coco' )
					snake_case							=					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

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

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

					snake_case							=					'a photography of'

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

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					pipeline('image-to-text',						model='microsoft/git-base-coco' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'a photo of a'

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

					with self.assertRaises(lowercase_ ):
						snake_case							=					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 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					pipeline('image-to-text',						model='google/pix2struct-ai2d-base' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

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

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

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )






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

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

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [{'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 '}],
					    ],						)





