




'''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      A       :







											@staticmethod
											def   __lowerCAmelCase     (    *lowerCAmelCase_			:    str				,     **lowerCAmelCase_			:    Any   )     ->				Union[str, Any]:


																		"""simple docstring"""


																		pass

@is_pipeline_test
@require_vision
class      A       (						unittest.TestCase       ):







							@require_torch
							def   __lowerCAmelCase     (    self			:    str   )     ->				Dict:


														"""simple docstring"""


														_a      	=							pipeline(
														    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''				,     )
														_a      	=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
														_a      	=							image_classifier(lowerCAmelCase_				,     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(lowerCAmelCase_   )				,     [
														        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
														        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
														    ]				,     )

														_a      	=							image_classifier([image] * 5				,     candidate_labels=['''A''', '''B''', '''C''']				,     batch_size=2   )
														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														    ]				,     )







							@require_tf
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							pipeline(
														    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''				,     framework='''tf'''   )
														_a      	=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
														_a      	=							image_classifier(lowerCAmelCase_				,     candidate_labels=['''a''', '''b''', '''c''']   )

														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]				,     )

														_a      	=							image_classifier([image] * 5				,     candidate_labels=['''A''', '''B''', '''C''']				,     batch_size=2   )
														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														            {'''score''': 0.3_3_3, '''label''': ANY(lowerCAmelCase_   )},
														        ],
														    ]				,     )







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


														"""simple docstring"""


														_a      	=							pipeline(
														    task='''zero-shot-image-classification'''				,     model='''openai/clip-vit-base-patch32'''				,     )
														# This is an image of 2 cats with remotes and no planes
														_a      	=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
														_a      	=							image_classifier(lowerCAmelCase_				,     candidate_labels=['''cat''', '''plane''', '''remote''']   )

														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [
														        {'''score''': 0.5_1_1, '''label''': '''remote'''},
														        {'''score''': 0.4_8_5, '''label''': '''cat'''},
														        {'''score''': 0.0_0_4, '''label''': '''plane'''},
														    ]				,     )

														_a      	=							image_classifier([image] * 5				,     candidate_labels=['''cat''', '''plane''', '''remote''']				,     batch_size=2   )
														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [
														        [
														            {'''score''': 0.5_1_1, '''label''': '''remote'''},
														            {'''score''': 0.4_8_5, '''label''': '''cat'''},
														            {'''score''': 0.0_0_4, '''label''': '''plane'''},
														        ],
														    ]
														    * 5				,     )







							@slow
							@require_tf
							def   __lowerCAmelCase     (    self			:    str   )     ->				Dict:


														"""simple docstring"""


														_a      	=							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
														_a      	=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
														_a      	=							image_classifier(lowerCAmelCase_				,     candidate_labels=['''cat''', '''plane''', '''remote''']   )
														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [
														        {'''score''': 0.5_1_1, '''label''': '''remote'''},
														        {'''score''': 0.4_8_5, '''label''': '''cat'''},
														        {'''score''': 0.0_0_4, '''label''': '''plane'''},
														    ]				,     )

														_a      	=							image_classifier([image] * 5				,     candidate_labels=['''cat''', '''plane''', '''remote''']				,     batch_size=2   )
														self.assertEqual(
														    nested_simplify(lowerCAmelCase_   )				,     [
														        [
														            {'''score''': 0.5_1_1, '''label''': '''remote'''},
														            {'''score''': 0.4_8_5, '''label''': '''cat'''},
														            {'''score''': 0.0_0_4, '''label''': '''plane'''},
														        ],
														    ]
														    * 5				,     )







'''simple docstring'''


import requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

