

'''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      a__						:


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





          """simple docstring"""





          pass


@is_pipeline_test
@require_torch_or_tf
@require_vision
class      a__						(							unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   str      =     image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"						)

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

      import datasets

      _lowercase    :   List[Any]      =     datasets.load_dataset("hf-internal-testing/fixtures_image_utils"    ,       "image"    ,       split="test"						)

      # Accepts URL + PIL.Image + lists
      _lowercase    :   Optional[Any]      =     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(
          _UpperCamelCase    ,       [
              [
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
              ],
              [
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
              ],
              [
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
              ],
              [
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
              ],
              [
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
                  {"score": ANY(_UpperCamelCase						), "label": ANY(_UpperCamelCase						)},
              ],
          ]    ,       )


   @require_torch
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     "hf-internal-testing/tiny-random-vit"
      _lowercase    :   str      =     pipeline("image-classification"    ,       model=_UpperCamelCase						)

      _lowercase    :   List[Any]      =     image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]    ,       )

      _lowercase    :   Optional[Any]      =     image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]    ,       top_k=2    ,       )
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]    ,       )


   @require_tf
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Tuple      =     "hf-internal-testing/tiny-random-vit"
      _lowercase    :   Union[str, Any]      =     pipeline("image-classification"    ,       model=_UpperCamelCase    ,       framework="tf"						)

      _lowercase    :   Union[str, Any]      =     image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]    ,       )

      _lowercase    :   Any      =     image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]    ,       top_k=2    ,       )
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     PreTrainedTokenizer()

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

      self.assertIs(image_classifier.tokenizer    ,       _UpperCamelCase						)






   @slow
   @require_torch
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     pipeline("image-classification"    ,       model="deepmind/vision-perceiver-conv"						)
      _lowercase    :   Dict      =     image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]    ,       )

      _lowercase    :   int      =     pipeline("image-classification"    ,       model="deepmind/vision-perceiver-fourier"						)
      _lowercase    :   Optional[int]      =     image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]    ,       )

      _lowercase    :   int      =     pipeline("image-classification"    ,       model="deepmind/vision-perceiver-learned"						)
      _lowercase    :   Optional[int]      =     image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
      self.assertEqual(
          nested_simplify(_UpperCamelCase    ,       decimals=4						)    ,       [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]    ,       )









'''simple docstring'''
from __future__ import annotations

from typing import Generic, TypeVar

_snake_case     						=						TypeVar('T')


class      a__						(							Generic[T]						):


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Any      =     data  # Assign data
      _lowercase    :   Node[T] | None      =     None  # Initialize next as null
      _lowercase    :   Node[T] | None      =     None  # Initialize prev as null




class      a__						(							Generic[T]						):


   def __init__(				self						):





      """simple docstring"""





      _lowercase    :   Node[T] | None      =     None


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if self.head is None:
         _lowercase    :   Optional[int]      =     Node(_UpperCamelCase						)
      else:
         _lowercase    :   str      =     Node(_UpperCamelCase						)
         _lowercase    :   Optional[Any]      =     new_node
         _lowercase    :   List[str]      =     self.head
         _lowercase    :   Tuple      =     None
         _lowercase    :   Tuple      =     new_node


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.head is None:
         return None
      else:
         assert self.head is not None
         _lowercase    :   str      =     self.head.data
         _lowercase    :   Optional[Any]      =     self.head.next
         if self.head is not None:
            _lowercase    :   Optional[int]      =     None
         return temp


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.head.data if self.head is not None else None


   def __len__(				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     self.head
      _lowercase    :   Tuple      =     0
      while temp is not None:
         count += 1
         _lowercase    :   Tuple      =     temp.next
      return count


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.head is None






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      print("stack elements are:"						)
      _lowercase    :   str      =     self.head
      while temp is not None:
         print(temp.data    ,       end="->"						)
         _lowercase    :   Any      =     temp.next


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






