





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:
       lowercase     :			Any     						=	False


if is_vision_available():
       from PIL import Image
else:

       class 			__A:

            @staticmethod
            def 						_UpperCamelCase  (   *A,							**A   ):





                   """simple docstring"""

                   pass

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

     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model=A,							tokenizer=A,							image_processor=A   )
            _UpperCamelCase														=    [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   ),
                '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
            ]
            return pipe, examples

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    pipe(A   )
            self.assertEqual(
                A,							[
                    [{'''generated_text''': ANY(A   )}],
                    [{'''generated_text''': ANY(A   )}],
                ],							)

     @require_tf
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''hf-internal-testing/tiny-random-vit-gpt2''',							framework='''tf'''   )
            _UpperCamelCase														=    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

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

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

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''hf-internal-testing/tiny-random-vit-gpt2'''   )
            _UpperCamelCase														=    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

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

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''hf-internal-testing/tiny-random-BlipForConditionalGeneration'''   )
            _UpperCamelCase														=    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
            _UpperCamelCase														=    '''a photo of'''

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

     @slow
     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''ydshieh/vit-gpt2-coco-en'''   )
            _UpperCamelCase														=    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

            _UpperCamelCase														=    pipe([image, image]   )
            self.assertEqual(
                A,							[
                    [{'''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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''Salesforce/blip-image-captioning-base'''   )
            _UpperCamelCase														=    '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
            _UpperCamelCase														=    Image.open(requests.get(A,							stream=A   ).raw   )

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

     @slow
     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''microsoft/git-base-coco'''   )
            _UpperCamelCase														=    '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
            _UpperCamelCase														=    Image.open(requests.get(A,							stream=A   ).raw   )

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

     @slow
     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''Salesforce/blip-image-captioning-base'''   )
            _UpperCamelCase														=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
            _UpperCamelCase														=    Image.open(requests.get(A,							stream=A   ).raw   )

            _UpperCamelCase														=    '''a photography of'''

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

            with self.assertRaises(A   ):
                   _UpperCamelCase														=    pipe([image, image],							prompt=[prompt, prompt]   )

     @slow
     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''microsoft/git-base-coco'''   )
            _UpperCamelCase														=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
            _UpperCamelCase														=    Image.open(requests.get(A,							stream=A   ).raw   )

            _UpperCamelCase														=    '''a photo of a'''

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

            with self.assertRaises(A   ):
                   _UpperCamelCase														=    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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''google/pix2struct-ai2d-base'''   )
            _UpperCamelCase														=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
            _UpperCamelCase														=    Image.open(requests.get(A,							stream=A   ).raw   )

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

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

            with self.assertRaises(A   ):
                   _UpperCamelCase														=    pipe([image, image],							prompt=[prompt, prompt]   )



     @slow
     @require_tf
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    pipeline('''image-to-text''',							model='''ydshieh/vit-gpt2-coco-en''',							framework='''tf'''   )
            _UpperCamelCase														=    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

            _UpperCamelCase														=    pipe([image, image]   )
            self.assertEqual(
                A,							[
                    [{'''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 '''}],
                ],							)








'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: str	):
						_SCREAMING_SNAKE_CASE					=			len(_SCREAMING_SNAKE_CASE	)
						for i in range(length - 1	):
												_SCREAMING_SNAKE_CASE					=			False
												for j in range(length - 1 - i	):
																		if collection[j] > collection[j + 1]:
																								_SCREAMING_SNAKE_CASE					=			True
																								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			collection[j + 1], collection[j]
												if not swapped:
																		break  # Stop iteration if the collection is sorted.
						return collection


if __name__ == "__main__":
						import doctest
						import time

						doctest.testmod()

						_snake_case					: Union[str, Any]     			=					input("""Enter numbers separated by a comma:""").strip()
						_snake_case					: List[Any]     			=					[int(item) for item in user_input.split(""",""")]
						_snake_case					: Optional[int]     			=					time.process_time()
						print(*bubble_sort(unsorted), sep=""",""")
						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")