



import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
							from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       str				,					_snake_case					:       str=13				,					_snake_case					:       Union[str, Any]=32				,					_snake_case					:       Tuple=3				,					_snake_case					:       List[str]=4				,					_snake_case					:       Optional[Any]=[10, 20, 30, 40]				,					_snake_case					:       List[str]=[2, 2, 3, 2]				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[Any]=True				,					_snake_case					:       Union[str, Any]=37				,					_snake_case					:       List[str]="gelu"				,					_snake_case					:       str=10				,					_snake_case					:       int=0.02				,					_snake_case					:       Optional[Any]=["stage2", "stage3", "stage4"]				,					_snake_case					:       Optional[int]=[2, 3, 4]				,					_snake_case					:       int=None				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    image_size
								A__       =    num_channels
								A__       =    num_stages
								A__       =    hidden_sizes
								A__       =    depths
								A__       =    is_training
								A__       =    use_labels
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    num_labels
								A__       =    initializer_range
								A__       =    out_features
								A__       =    out_indices
								A__       =    scope



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								A__       =    None
								if self.use_labels:
											A__       =    ids_tensor([self.batch_size]				,					self.num_labels					)

								A__       =    self.get_config()
								return config, pixel_values, labels



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return ConvNextConfig(
								    num_channels=self.num_channels				,					hidden_sizes=self.hidden_sizes				,					depths=self.depths				,					num_stages=self.num_stages				,					hidden_act=self.hidden_act				,					is_decoder=_snake_case				,					initializer_range=self.initializer_range				,					out_features=self.out_features				,					out_indices=self.out_indices				,					num_labels=self.num_labels				,					)



					def     _a							(     self					:       Tuple				,					_snake_case					:       Any				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    ConvNextModel(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape				,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,					)



					def     _a							(     self					:       str				,					_snake_case					:       Optional[int]				,					_snake_case					:       int				,					_snake_case					:       List[str]					):






								"""simple docstring"""


								A__       =    ConvNextForImageClassification(_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case				,					labels=_snake_case					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_labels)					)



					def     _a							(     self					:       List[str]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       str				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    ConvNextBackbone(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps					)				,					len(config.out_features					)					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)				,					[self.batch_size, self.hidden_sizes[1], 4, 4]					)

								# verify channels
								self.parent.assertEqual(len(model.channels					)				,					len(config.out_features					)					)
								self.parent.assertListEqual(model.channels				,					config.hidden_sizes[1:]					)

								# verify backbone works with out_features=None
								A__       =    None
								A__       =    ConvNextBackbone(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps					)				,					1					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)				,					[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

								# verify channels
								self.parent.assertEqual(len(model.channels					)				,					1					)
								self.parent.assertListEqual(model.channels				,					[config.hidden_sizes[-1]]					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.prepare_config_and_inputs()
								A__     , A__     , A__       =    config_and_inputs
								A__       =    {'pixel_values': pixel_values}
								return config, inputs_dict







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    (
					    (
					        ConvNextModel,
					        ConvNextForImageClassification,
					        ConvNextBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					A__				:		Optional[Any]			       =    (
					    {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__				:		Dict			       =    True
					A__				:		Optional[int]			       =    False
					A__				:		str			       =    False
					A__				:		Tuple			       =    False
					A__				:		List[str]			       =    False



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ConvNextModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								return



					@unittest.skip(reason='ConvNext does not use inputs_embeds'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ConvNext does not support input and output embeddings'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ConvNext does not use feedforward chunking'					)
					def     _a							(     self					:       str					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											A__       =    inspect.signature(model.forward					)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											A__       =    [*signature.parameters.keys()]

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_snake_case					)



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case					:       int				,					_snake_case					:       int				,					_snake_case					:       Union[str, Any]					):
											A__       =    model_class(_snake_case					)
											model.to(_snake_case					)
											model.eval()

											with torch.no_grad():
														A__       =    model(**self._prepare_for_class(_snake_case				,					_snake_case					)					)

											A__       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											A__       =    self.model_tester.num_stages
											self.assertEqual(len(_snake_case					)				,					expected_num_stages + 1					)

											# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]					)				,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,					)

								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    True
											check_hidden_states_output(_snake_case				,					_snake_case				,					_snake_case					)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											A__       =    True

											check_hidden_states_output(_snake_case				,					_snake_case				,					_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					@slow
					def     _a							(     self					:       str					):






								"""simple docstring"""


								for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ConvNextModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Optional[Any]:
			A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image


@require_torch
@require_vision
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'					) if is_vision_available() else None



					@slow
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'					).to(_snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

								# forward pass
								with torch.no_grad():
											A__       =    model(**_snake_case					)

								# verify the logits
								A__       =    torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([-0.0260, -0.4739, 0.1911]					).to(_snake_case					)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]				,					_snake_case				,					atol=1E-4					)					)







@require_torch
class      __lowerCAmelCase     (		unittest.TestCase							,     UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Dict			       =    (ConvNextBackbone,) if is_torch_available() else ()
					A__				:		Tuple			       =    ConvNextConfig

					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    ConvNextModelTester(self					)









import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

							print('''Googling.....''')

							SCREAMING_SNAKE_CASE__       =      f'https://www.google.com/search?q={query}&num=100'

							SCREAMING_SNAKE_CASE__       =      requests.get(
							    url,
							    headers={'''User-Agent''': str(UserAgent().random)},
							)

							try:
														SCREAMING_SNAKE_CASE__       =      (
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''yuRUbf'''})
														    .find('''a''')
														    .get('''href''')
														)

							except AttributeError:
														SCREAMING_SNAKE_CASE__       =      parse_qs(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''kCrYT'''})
														    .find('''a''')
														    .get('''href''')
														)['''url'''][0]

							webbrowser.open(link)




