
"""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 		UpperCAmelCase_	:
        """simple docstring"""




        @staticmethod
        def   a      ( *a_       : Tuple     ,   **a_       : Optional[Any] )->					Optional[Any]:



             """simple docstring"""

             pass



@is_pipeline_test
@require_vision
class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 UpperCamelCase_						:     List[str]				   =    MODEL_FOR_VISION_2_SEQ_MAPPING
 UpperCamelCase_						:     str				   =    TF_MODEL_FOR_VISION_2_SEQ_MAPPING




 def   a      ( self       : Optional[Any]     ,   a_       : List[Any]     ,   a_       : Any     ,   a_       : Optional[int] )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  pipeline("""image-to-text"""     ,   model=a_     ,   tokenizer=a_     ,   image_processor=a_ )
      UpperCAmelCase_						:    Any    					=  [
          Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
          """./tests/fixtures/tests_samples/COCO/000000039769.png""",
      ]
      return pipe, examples




 def   a      ( self       : str     ,   a_       : Any     ,   a_       : Optional[Any] )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  pipe(a_ )
      self.assertEqual(
          a_     ,   [
              [{"""generated_text""": ANY(a_ )}],
              [{"""generated_text""": ANY(a_ )}],
          ]     ,   )




 @require_tf
 def   a      ( self       : int )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  pipeline("""image-to-text"""     ,   model="""hf-internal-testing/tiny-random-vit-gpt2"""     ,   framework="""tf""" )
      UpperCAmelCase_						:    Union[str, Any]    					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

      UpperCAmelCase_						:    Union[str, Any]    					=  pipe(a_ )
      self.assertEqual(
          a_     ,   [
              {
                  """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
              },
          ]     ,   )

      UpperCAmelCase_						:    List[Any]    					=  pipe([image, image] )
      self.assertEqual(
          a_     ,   [
              [
                  {
                      """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
                  }
              ],
              [
                  {
                      """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
                  }
              ],
          ]     ,   )

      UpperCAmelCase_						:    Union[str, Any]    					=  pipe(a_     ,   max_new_tokens=1 )
      self.assertEqual(
          a_     ,   [{"""generated_text""": """growth"""}]     ,   )




 @require_torch
 def   a      ( self       : List[str] )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  pipeline("""image-to-text"""     ,   model="""hf-internal-testing/tiny-random-vit-gpt2""" )
      UpperCAmelCase_						:    List[str]    					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

      UpperCAmelCase_						:    Any    					=  pipe(a_ )
      self.assertEqual(
          a_     ,   [
              {
                  """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
              },
          ]     ,   )

      UpperCAmelCase_						:    Optional[Any]    					=  pipe([image, image] )
      self.assertEqual(
          a_     ,   [
              [
                  {
                      """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
                  }
              ],
              [
                  {
                      """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
                  }
              ],
          ]     ,   )




 @require_torch
 def   a      ( self       : Tuple )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  pipeline("""image-to-text"""     ,   model="""hf-internal-testing/tiny-random-BlipForConditionalGeneration""" )
      UpperCAmelCase_						:    Union[str, Any]    					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""
      UpperCAmelCase_						:    Union[str, Any]    					=  """a photo of"""

      UpperCAmelCase_						:    Any    					=  pipe(a_     ,   prompt=a_ )
      self.assertTrue(outputs[0]["""generated_text"""].startswith(a_ ) )




 @slow
 @require_torch
 def   a      ( self       : str )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  pipeline("""image-to-text"""     ,   model="""ydshieh/vit-gpt2-coco-en""" )
      UpperCAmelCase_						:    Any    					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

      UpperCAmelCase_						:    Optional[int]    					=  pipe(a_ )
      self.assertEqual(a_     ,   [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}] )

      UpperCAmelCase_						:    int    					=  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       : List[str] )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  pipeline("""image-to-text"""     ,   model="""Salesforce/blip-image-captioning-base""" )
      UpperCAmelCase_						:    Optional[int]    					=  """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
      UpperCAmelCase_						:    Any    					=  Image.open(requests.get(a_     ,   stream=a_ ).raw )

      UpperCAmelCase_						:    List[str]    					=  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       : Any )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  pipeline("""image-to-text"""     ,   model="""microsoft/git-base-coco""" )
      UpperCAmelCase_						:    Dict    					=  """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
      UpperCAmelCase_						:    Optional[int]    					=  Image.open(requests.get(a_     ,   stream=a_ ).raw )

      UpperCAmelCase_						:    str    					=  pipe(a_ )
      self.assertEqual(a_     ,   [{"""generated_text""": """a cartoon of a purple character."""}] )




 @slow
 @require_torch
 def   a      ( self       : Dict )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  pipeline("""image-to-text"""     ,   model="""Salesforce/blip-image-captioning-base""" )
      UpperCAmelCase_						:    Tuple    					=  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
      UpperCAmelCase_						:    Optional[Any]    					=  Image.open(requests.get(a_     ,   stream=a_ ).raw )

      UpperCAmelCase_						:    Optional[Any]    					=  """a photography of"""

      UpperCAmelCase_						:    Any    					=  pipe(a_     ,   prompt=a_ )
      self.assertEqual(a_     ,   [{"""generated_text""": """a photography of a volcano"""}] )

      with self.assertRaises(a_ ):
           UpperCAmelCase_						:    List[str]    					=  pipe([image, image]     ,   prompt=[prompt, prompt] )




 @slow
 @require_torch
 def   a      ( self       : Optional[Any] )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  pipeline("""image-to-text"""     ,   model="""microsoft/git-base-coco""" )
      UpperCAmelCase_						:    Optional[int]    					=  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
      UpperCAmelCase_						:    str    					=  Image.open(requests.get(a_     ,   stream=a_ ).raw )

      UpperCAmelCase_						:    List[Any]    					=  """a photo of a"""

      UpperCAmelCase_						:    Optional[Any]    					=  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_						:    Tuple    					=  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       : Any )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  pipeline("""image-to-text"""     ,   model="""google/pix2struct-ai2d-base""" )
      UpperCAmelCase_						:    Tuple    					=  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
      UpperCAmelCase_						:    Any    					=  Image.open(requests.get(a_     ,   stream=a_ ).raw )

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

      UpperCAmelCase_						:    Dict    					=  pipe(a_     ,   prompt=a_ )
      self.assertEqual(a_     ,   [{"""generated_text""": """ash cloud"""}] )

      with self.assertRaises(a_ ):
           UpperCAmelCase_						:    List[str]    					=  pipe([image, image]     ,   prompt=[prompt, prompt] )






 @slow
 @require_tf
 def   a      ( self       : Optional[int] )->					Optional[Any]:



      """simple docstring"""

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

      UpperCAmelCase_						:    Dict    					=  pipe(a_ )
      self.assertEqual(a_     ,   [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}] )

      UpperCAmelCase_						:    Optional[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 """}],
          ]     ,   )



"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : int = 0 )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  value
      UpperCAmelCase_						:    Node | None    					=  None
      UpperCAmelCase_						:    Node | None    					=  None




def 			A_			(					lowercase						, lowercase      )     ->							Node | None:


     """simple docstring"""







     if treea is None:
          return treea
     if treea is None:
          return treea

     UpperCAmelCase_						:    int    					=  treea.value + treea.value
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.left						, treea.left      )
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.right						, treea.right      )
     return treea




def 			A_			(					lowercase      )     ->							None:


     """simple docstring"""







     if root:
          print(root.value      )
          print_preorder(root.left      )
          print_preorder(root.right      )


if __name__ == "__main__":
       lowercase_						    =					Node(1)
       lowercase_						    =					Node(2)
       lowercase_						    =					Node(3)
       lowercase_						    =					Node(4)

       lowercase_						    =					Node(2)
       lowercase_						    =					Node(4)
       lowercase_						    =					Node(6)
       lowercase_						    =					Node(9)
       lowercase_						    =					Node(5)

       print("Tree1 is: ")
       print_preorder(treea)
       print("Tree2 is: ")
       print_preorder(treea)
       lowercase_						    =					merge_two_binary_trees(treea, treea)
       print("Merged Tree is: ")
       print_preorder(merged_tree)

