


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 							__UpperCamelCase			(    *_a   : str			,      **_a   : Tuple					)							->       Tuple:


																	"""simple docstring"""





																	pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class   A__							(						unittest.TestCase					):
						UpperCAmelCase     =  MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						UpperCAmelCase     =  TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




						def 							__UpperCamelCase			(    self   : Any			,      _a   : Optional[Any]			,      _a   : Optional[int]			,      _a   : List[Any]					)							->       Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ImageClassificationPipeline(model=_a			,      image_processor=_a			,      top_k=2					)
										_SCREAMING_SNAKE_CASE     					=[
										    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
										    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										]
										return image_classifier, examples




						def 							__UpperCamelCase			(    self   : Dict			,      _a   : Any			,      _a   : Any					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=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

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

										# Accepts URL + PIL.Image + lists
										_SCREAMING_SNAKE_CASE     					=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   : List[str]					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''hf-internal-testing/tiny-random-vit'''
										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model=_a					)

										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,      )

										_SCREAMING_SNAKE_CASE     					=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''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										    ]			,      )




						@require_tf
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''hf-internal-testing/tiny-random-vit'''
										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model=_a			,      framework='''tf'''					)

										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,      )

										_SCREAMING_SNAKE_CASE     					=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''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										    ]			,      )




						def 							__UpperCamelCase			(    self   : int					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=PreTrainedTokenizer()

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

										self.assertIs(image_classifier.tokenizer			,      _a					)




						@slow
						@require_torch
						def 							__UpperCamelCase			(    self   : Any					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model='''deepmind/vision-perceiver-conv'''					)
										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
										        {'''score''': 0.3_21, '''label''': '''tiger cat'''},
										        {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
										        {'''score''': 0.01_37, '''label''': '''crib, cot'''},
										        {'''score''': 0.0_07, '''label''': '''radiator'''},
										    ]			,      )

										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model='''deepmind/vision-perceiver-fourier'''					)
										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
										        {'''score''': 0.13_09, '''label''': '''tiger cat'''},
										        {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
										        {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
										        {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
										    ]			,      )

										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model='''deepmind/vision-perceiver-learned'''					)
										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
										        {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
										        {'''score''': 0.18_56, '''label''': '''tiger cat'''},
										        {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
										        {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
										    ]			,      )



from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=data
										_SCREAMING_SNAKE_CASE     					=None




						def __str__(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										return f"{self.data}"

class   A__							:




						def __init__(    self   : Optional[Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None




						def __iter__(    self   : List[str]					)							->       Iterator[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.front
										while node:
														yield node.data
														_SCREAMING_SNAKE_CASE     					=node.next




						def __len__(    self   : List[Any]					)							->       int:


										"""simple docstring"""





										return len(tuple(iter(self					)					)					)




						def __str__(    self   : int					)							->       str:


										"""simple docstring"""





										return " <- ".join(str(_a					) for item in self					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       bool:


										"""simple docstring"""





										return len(self					) == 0




						def 							__UpperCamelCase			(    self   : int			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=Node(_a					)
										if self.is_empty():
														_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=node
										else:
														assert isinstance(self.rear			,      _a					)
														_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=node




						def 							__UpperCamelCase			(    self   : Dict					)							->       Any:


										"""simple docstring"""





										if self.is_empty():
														raise IndexError('''dequeue from empty queue'''					)
										assert isinstance(self.front			,      _a					)
										_SCREAMING_SNAKE_CASE     					=self.front
										_SCREAMING_SNAKE_CASE     					=self.front.next
										if self.front is None:
														_SCREAMING_SNAKE_CASE     					=None
										return node.data




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=None


if __name__ == "__main__":
							from doctest import testmod

							testmod()