






"""simple docstring"""







import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class 					lowerCamelCase__					:






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







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 					lowerCamelCase__					(       unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    SCREAMING_SNAKE_CASE		=			TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






    def 				_UpperCamelCase							( self	,A	,A	,A					):
          UpperCAmelCase							=       ImageClassificationPipeline(model=A	,image_processor=A	,top_k=2					)
          UpperCAmelCase							=       [
              Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					),
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]
          return image_classifier, examples






    def 				_UpperCamelCase							( self	,A	,A					):
          UpperCAmelCase							=       image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)

          self.assertEqual(
              A	,[
                  {"""score""": ANY(A					), """label""": ANY(A					)},
                  {"""score""": ANY(A					), """label""": ANY(A					)},
              ]	,)

          import datasets

          UpperCAmelCase							=       datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""	,"""image"""	,split="""test"""					)

          # Accepts URL + PIL.Image + lists
          UpperCAmelCase							=       image_classifier(
              [
                  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					),
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  # RGBA
                  dataset[0]["""file"""],
                  # LA
                  dataset[1]["""file"""],
                  # L
                  dataset[2]["""file"""],
              ]					)
          self.assertEqual(
              A	,[
                  [
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                  ],
                  [
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                  ],
                  [
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                  ],
                  [
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                  ],
                  [
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                      {"""score""": ANY(A					), """label""": ANY(A					)},
                  ],
              ]	,)






    @require_torch
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """hf-internal-testing/tiny-random-vit"""
          UpperCAmelCase							=       pipeline("""image-classification"""	,model=A					)

          UpperCAmelCase							=       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]	,)

          UpperCAmelCase							=       image_classifier(
              [
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
              ]	,top_k=2	,)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
              ]	,)






    @require_tf
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """hf-internal-testing/tiny-random-vit"""
          UpperCAmelCase							=       pipeline("""image-classification"""	,model=A	,framework="""tf"""					)

          UpperCAmelCase							=       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]	,)

          UpperCAmelCase							=       image_classifier(
              [
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
              ]	,top_k=2	,)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
              ]	,)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       PreTrainedTokenizer()

          # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
          UpperCAmelCase							=       pipeline(
              """image-classification"""	,model="""hf-internal-testing/tiny-random-vit"""	,tokenizer=A					)

          self.assertIs(image_classifier.tokenizer	,A					)






    @slow
    @require_torch
    def 				_UpperCamelCase							( self					):
          # Perceiver is not tested by `run_pipeline_test` properly.
          # That is because the type of feature_extractor and model preprocessor need to be kept
          # in sync, which is not the case in the current design
          UpperCAmelCase							=       pipeline("""image-classification"""	,model="""deepmind/vision-perceiver-conv"""					)
          UpperCAmelCase							=       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[
                  {"""score""": 0.4385, """label""": """tabby, tabby cat"""},
                  {"""score""": 0.321, """label""": """tiger cat"""},
                  {"""score""": 0.0502, """label""": """Egyptian cat"""},
                  {"""score""": 0.0137, """label""": """crib, cot"""},
                  {"""score""": 0.007, """label""": """radiator"""},
              ]	,)

          UpperCAmelCase							=       pipeline("""image-classification"""	,model="""deepmind/vision-perceiver-fourier"""					)
          UpperCAmelCase							=       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[
                  {"""score""": 0.5658, """label""": """tabby, tabby cat"""},
                  {"""score""": 0.1309, """label""": """tiger cat"""},
                  {"""score""": 0.0722, """label""": """Egyptian cat"""},
                  {"""score""": 0.0707, """label""": """remote control, remote"""},
                  {"""score""": 0.0082, """label""": """computer keyboard, keypad"""},
              ]	,)

          UpperCAmelCase							=       pipeline("""image-classification"""	,model="""deepmind/vision-perceiver-learned"""					)
          UpperCAmelCase							=       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
          self.assertEqual(
              nested_simplify(A	,decimals=4					)	,[
                  {"""score""": 0.3022, """label""": """tabby, tabby cat"""},
                  {"""score""": 0.2362, """label""": """Egyptian cat"""},
                  {"""score""": 0.1856, """label""": """tiger cat"""},
                  {"""score""": 0.0324, """label""": """remote control, remote"""},
                  {"""score""": 0.0096, """label""": """quilt, comforter, comfort, puff"""},
              ]	,)











"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase     			=  TypeVar("""T""")







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self	,A					):
          UpperCAmelCase							=       data  # Assign data
          UpperCAmelCase							=       None  # Initialize next as null
          UpperCAmelCase							=       None  # Initialize prev as null







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self					):
          UpperCAmelCase							=       None






    def 				_UpperCamelCase							( self	,A					):
          if self.head is None:
                UpperCAmelCase							=       Node(A					)
          else:
                UpperCAmelCase							=       Node(A					)
                UpperCAmelCase							=       new_node
                UpperCAmelCase							=       self.head
                UpperCAmelCase							=       None
                UpperCAmelCase							=       new_node






    def 				_UpperCamelCase							( self					):
          if self.head is None:
                return None
          else:
                assert self.head is not None
                UpperCAmelCase							=       self.head.data
                UpperCAmelCase							=       self.head.next
                if self.head is not None:
                      UpperCAmelCase							=       None
                return temp






    def 				_UpperCamelCase							( self					):
          return self.head.data if self.head is not None else None






    def __len__( self					):
          UpperCAmelCase							=       self.head
          UpperCAmelCase							=       0
          while temp is not None:
                count += 1
                UpperCAmelCase							=       temp.next
          return count






    def 				_UpperCamelCase							( self					):
          return self.head is None






    def 				_UpperCamelCase							( self					):
          print("""stack elements are:"""					)
          UpperCAmelCase							=       self.head
          while temp is not None:
                print(temp.data	,end="""->"""					)
                UpperCAmelCase							=       temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  _UpperCamelCase     			=  Stack()

  # Insert 4 at the beginning. So stack becomes 4->None
  print("""Stack operations using Doubly LinkedList""")
  stack.push(4)

  # Insert 5 at the beginning. So stack becomes 4->5->None
  stack.push(5)

  # Insert 6 at the beginning. So stack becomes 4->5->6->None
  stack.push(6)

  # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
  stack.push(7)

  # Print the stack
  stack.print_stack()

  # Print the top element
  print("""\nTop element is """, stack.top())

  # Print the stack size
  print("""Size of the stack is """, len(stack))

  # pop the top element
  stack.pop()

  # pop the top element
  stack.pop()

  # two elements have now been popped off
  stack.print_stack()

  # Print True if the stack is empty else False
  print("""\nstack is empty:""", stack.is_empty())



