




'''simple docstring'''





lowercase__			=	{
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

lowercase__			=	[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowercase__			=	[0 for x in range(len(graph))]
lowercase__			=	[]


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= 1
   while stack:
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= stack.pop()
      print(snake_case	,     clothes[current_clothing]   )
      order += 1


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[Any]       						= 1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(snake_case	,     snake_case	,     snake_case   )

   stack.append(snake_case   )


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   for v in range(len(snake_case   )   ):
      if not visited[v]:
         depth_first_search(snake_case	,     snake_case	,     snake_case   )


if __name__ == "__main__":
       topological_sort(graph, visited)
       print(stack)
       print_stack(stack, clothes)







'''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:
       lowercase__			=	False


if is_vision_available():
       from PIL import Image
else:






       class 						lowercase     :






             @staticmethod
             def    A__(*a__ ,  **a__					)       ->   Optional[Any]:
                pass






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






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






      def    A__(self ,  a__ ,  a__					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= pipe(a__					)
         self.assertEqual(
             a__ ,  [
                 [{'''generated_text''': ANY(a__					)}],
                 [{'''generated_text''': ANY(a__					)}],
             ] ,  )






      @require_tf
      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-vit-gpt2''' ,  framework='''tf'''					)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

         SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__					)
         self.assertEqual(
             a__ ,  [
                 {
                     '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                 },
             ] ,  )

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipe([image, image]					)
         self.assertEqual(
             a__ ,  [
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
             ] ,  )

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  max_new_tokens=1					)
         self.assertEqual(
             a__ ,  [{'''generated_text''': '''growth'''}] ,  )






      @require_torch
      def    A__(self					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-vit-gpt2'''					)
         SCREAMING_SNAKE_CASE__			:				Any       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipe(a__					)
         self.assertEqual(
             a__ ,  [
                 {
                     '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                 },
             ] ,  )

         SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image]					)
         self.assertEqual(
             a__ ,  [
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
             ] ,  )






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

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipe(a__ ,  prompt=a__					)
         self.assertTrue(outputs[0]['''generated_text'''].startswith(a__					)					)






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

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipe(a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]					)

         SCREAMING_SNAKE_CASE__			:				Tuple       						= 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    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__			:				Dict       						= pipeline('''image-to-text''' ,  model='''Salesforce/blip-image-captioning-base'''					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipe(a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a pink pokemon pokemon with a blue shirt and a blue shirt'''}]					)






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

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






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

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''a photography of'''

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  prompt=a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a photography of a volcano'''}]					)

         with self.assertRaises(a__					):
            SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image] ,  prompt=[prompt, prompt]					)






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

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''a photo of a'''

         SCREAMING_SNAKE_CASE__			:				str       						= 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__					):
            SCREAMING_SNAKE_CASE__			:				Dict       						= 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    A__(self					)       ->   Union[str, Any]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline('''image-to-text''' ,  model='''google/pix2struct-ai2d-base'''					)
         SCREAMING_SNAKE_CASE__			:				int       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
         SCREAMING_SNAKE_CASE__			:				Any       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

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

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  prompt=a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''ash cloud'''}]					)

         with self.assertRaises(a__					):
            SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image] ,  prompt=[prompt, prompt]					)






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

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

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= 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 '''}],
             ] ,  )

