

"""simple docstring"""




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image
else:
				class      _UpperCAmelCase   :



					@staticmethod
					def        a  (  *_lowercase     :       Optional[int]			,    **_lowercase     :       List[str]						):
						pass
@is_pipeline_test
@require_vision
class      _UpperCAmelCase   (							unittest.TestCase       ):



	@require_torch
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= pipeline(
		    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,    )
		__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		__UpperCAmelCase      		= image_classifier(_lowercase			,    candidate_labels=['''a''', '''b''', '''c''']						)

		# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
		# python and torch versions.
		self.assertIn(
		    nested_simplify(_lowercase						)			,    [
		        [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
		        [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
		    ]			,    )

		__UpperCAmelCase      		= image_classifier([image] * 5			,    candidate_labels=['''A''', '''B''', '''C''']			,    batch_size=2						)
		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		    ]			,    )



	@require_tf
	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= pipeline(
		    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,    framework='''tf'''						)
		__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		__UpperCAmelCase      		= image_classifier(_lowercase			,    candidate_labels=['''a''', '''b''', '''c''']						)

		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]			,    )

		__UpperCAmelCase      		= image_classifier([image] * 5			,    candidate_labels=['''A''', '''B''', '''C''']			,    batch_size=2						)
		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		        [
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		            {'''score''': 0.333, '''label''': ANY(_lowercase						)},
		        ],
		    ]			,    )



	@slow
	@require_torch
	def        a  (  self     :       List[str]						):
		__UpperCAmelCase      		= pipeline(
		    task='''zero-shot-image-classification'''			,    model='''openai/clip-vit-base-patch32'''			,    )
		# This is an image of 2 cats with remotes and no planes
		__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		__UpperCAmelCase      		= image_classifier(_lowercase			,    candidate_labels=['''cat''', '''plane''', '''remote''']						)

		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        {'''score''': 0.511, '''label''': '''remote'''},
		        {'''score''': 0.485, '''label''': '''cat'''},
		        {'''score''': 0.004, '''label''': '''plane'''},
		    ]			,    )

		__UpperCAmelCase      		= image_classifier([image] * 5			,    candidate_labels=['''cat''', '''plane''', '''remote''']			,    batch_size=2						)
		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        [
		            {'''score''': 0.511, '''label''': '''remote'''},
		            {'''score''': 0.485, '''label''': '''cat'''},
		            {'''score''': 0.004, '''label''': '''plane'''},
		        ],
		    ]
		    * 5			,    )



	@slow
	@require_tf
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= pipeline(
		    task='''zero-shot-image-classification'''			,    model='''openai/clip-vit-base-patch32'''			,    framework='''tf'''						)
		# This is an image of 2 cats with remotes and no planes
		__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		__UpperCAmelCase      		= image_classifier(_lowercase			,    candidate_labels=['''cat''', '''plane''', '''remote''']						)
		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        {'''score''': 0.511, '''label''': '''remote'''},
		        {'''score''': 0.485, '''label''': '''cat'''},
		        {'''score''': 0.004, '''label''': '''plane'''},
		    ]			,    )

		__UpperCAmelCase      		= image_classifier([image] * 5			,    candidate_labels=['''cat''', '''plane''', '''remote''']			,    batch_size=2						)
		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        [
		            {'''score''': 0.511, '''label''': '''remote'''},
		            {'''score''': 0.485, '''label''': '''cat'''},
		            {'''score''': 0.004, '''label''': '''plane'''},
		        ],
		    ]
		    * 5			,    )








from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

snake_case__   :						Dict													=   TypeVar("""T""")


class     _a							(	Generic[T]     ):






  """simple docstring"""





  A_						       = 42  # Cache store of keys
  A_						       = 42  # References of the keys in cache
  A_						       = 10  # Maximum capacity of cache




  def __init__(  self		,		_UpperCAmelCase			)      ->			None:
     UpperCamelCase_						=  deque()
     UpperCamelCase_						=  set()
     if not n:
        UpperCamelCase_						=  sys.maxsize
     elif n < 0:
        raise ValueError('n should be an integer greater than 0.'			)
     else:
        UpperCamelCase_						=  n




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     if x not in self.key_reference:
        if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
           UpperCamelCase_						=  self.dq_store.pop()
           self.key_reference.remove(_UpperCAmelCase			)
     else:
        self.dq_store.remove(_UpperCAmelCase			)

     self.dq_store.appendleft(_UpperCAmelCase			)
     self.key_reference.add(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			None:
     for k in self.dq_store:
        print(_UpperCAmelCase			)




  def __repr__(  self			)      ->			str:
     return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}"""


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 snake_case__   :						LRUCache[str | int]													=   LRUCache(4)
 lru_cache.refer("""A""")
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer("""A""")
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





