






from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor


class 							__a							:





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=3 ,    UpperCamelCase__=32 ,    UpperCamelCase__=3 ,    UpperCamelCase__=10 ,    UpperCamelCase__=[10, 20, 30, 40] ,    UpperCamelCase__=[1, 1, 2, 1] ,    UpperCamelCase__=True ,    UpperCamelCase__=True ,    UpperCamelCase__="relu" ,    UpperCamelCase__=3 ,    UpperCamelCase__=None ,    ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   parent
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   batch_size
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   image_size
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   num_channels
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   embeddings_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_sizes
						SCREAMING_SNAKE_CASE_   :	List[str]													=   depths
						SCREAMING_SNAKE_CASE_   :	List[str]													=   is_training
						SCREAMING_SNAKE_CASE_   :	Any													=   use_labels
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	str													=   num_labels
						SCREAMING_SNAKE_CASE_   :	Tuple													=   scope
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   len(snake_case_       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   None
						if self.use_labels:
										SCREAMING_SNAKE_CASE_   :	Any													=   ids_tensor([self.batch_size] ,    self.num_labels       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_config()
						return config, pixel_values, labels


		def   __snake_case			(						self       ):
						return RegNetConfig(
						    num_channels=self.num_channels ,    embeddings_size=self.embeddings_size ,    hidden_sizes=self.hidden_sizes ,    depths=self.depths ,    hidden_act=self.hidden_act ,    num_labels=self.num_labels ,    )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   TFRegNetModel(config=snake_case_       )
						SCREAMING_SNAKE_CASE_   :	int													=   model(snake_case_ ,    training=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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.num_labels
						SCREAMING_SNAKE_CASE_   :	Tuple													=   TFRegNetForImageClassification(snake_case_       )
						SCREAMING_SNAKE_CASE_   :	str													=   model(snake_case_ ,    labels=snake_case_ ,    training=snake_case_       )
						self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE_   :	str													=   config_and_inputs
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {"pixel_values": pixel_values}
						return config, inputs_dict






@require_tf
class 							__a							(  _snake_case ,	_snake_case ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]				    =						(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
		UpperCAmelCase__      :				str				    =						(
		    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		UpperCAmelCase__      :				Optional[int]				    =						False
		UpperCAmelCase__      :				List[Any]				    =						False
		UpperCAmelCase__      :				str				    =						False
		UpperCAmelCase__      :				Optional[Any]				    =						False
		UpperCAmelCase__      :				Dict				    =						False


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   TFRegNetModelTester(self       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ConfigTester(self ,    config_class=snake_case_ ,    has_text_modality=snake_case_       )


		def   __snake_case			(						self       ):
						return


		@unittest.skip(reason='RegNet does not use inputs_embeds'       )
		def   __snake_case			(						self       ):
						pass


		@unittest.skipIf(
		    not is_tf_available() or len(tf.config.list_physical_devices('GPU'       )       ) == 0 ,    reason='TF does not support backprop for grouped convolutions on CPU.' ,    )
		@slow
		def   __snake_case			(						self       ):
						super().test_keras_fit()


		@unittest.skip(reason='RegNet does not support input and output embeddings'       )
		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   model_class(snake_case_       )
										SCREAMING_SNAKE_CASE_   :	Tuple													=   inspect.signature(model.call       )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [*signature.parameters.keys()]

										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ["pixel_values"]
										self.assertListEqual(arg_names[:1] ,    snake_case_       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case_       )


		def   __snake_case			(						self       ):
						def check_hidden_states_output(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model_class(snake_case_       )
										SCREAMING_SNAKE_CASE_   :	str													=   model(**self._prepare_for_class(snake_case_ ,    snake_case_       ) ,    training=snake_case_       )

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.model_tester.num_stages
										self.assertEqual(len(snake_case_       ) ,    expected_num_stages + 1       )

										# RegNet'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 // 2, self.model_tester.image_size // 2] ,    )

						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE_   :	str													=   ["basic", "bottleneck"]
						for model_class in self.all_model_classes:
										for layer_type in layers_type:
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   layer_type
														SCREAMING_SNAKE_CASE_   :	Tuple													=   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"]
														SCREAMING_SNAKE_CASE_   :	int													=   True

														check_hidden_states_output(snake_case_ ,    snake_case_ ,    snake_case_       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.model_tester.prepare_config_and_inputs_for_common()

						def check_equivalence(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__={}       ):
										SCREAMING_SNAKE_CASE_   :	List[str]													=   model(snake_case_ ,    return_dict=snake_case_ ,    **snake_case_       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model(snake_case_ ,    return_dict=snake_case_ ,    **snake_case_       ).to_tuple()

										def recursive_check(UpperCamelCase__ ,    UpperCamelCase__       ):
														if isinstance(snake_case_ ,    (List, Tuple)       ):
																		for tuple_iterable_value, dict_iterable_value in zip(snake_case_ ,    snake_case_       ):
																						recursive_check(snake_case_ ,    snake_case_       )
														elif tuple_object is None:
																		return
														else:
																		self.assertTrue(
																		    all(tf.equal(snake_case_ ,    snake_case_       )       ) ,    msg=(
																		        'Tuple and dict output are not equal. Difference:'
																		        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}'''
																		    ) ,    )

														recursive_check(snake_case_ ,    snake_case_       )

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   :	int													=   model_class(snake_case_       )

										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self._prepare_for_class(snake_case_ ,    snake_case_       )
										SCREAMING_SNAKE_CASE_   :	Dict													=   self._prepare_for_class(snake_case_ ,    snake_case_       )
										check_equivalence(snake_case_ ,    snake_case_ ,    snake_case_       )

										SCREAMING_SNAKE_CASE_   :	Tuple													=   self._prepare_for_class(snake_case_ ,    snake_case_ ,    return_labels=snake_case_       )
										SCREAMING_SNAKE_CASE_   :	str													=   self._prepare_for_class(snake_case_ ,    snake_case_ ,    return_labels=snake_case_       )
										check_equivalence(snake_case_ ,    snake_case_ ,    snake_case_       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   self._prepare_for_class(snake_case_ ,    snake_case_       )
										SCREAMING_SNAKE_CASE_   :	str													=   self._prepare_for_class(snake_case_ ,    snake_case_       )
										check_equivalence(snake_case_ ,    snake_case_ ,    snake_case_ ,    {'output_hidden_states': True}       )

										SCREAMING_SNAKE_CASE_   :	Tuple													=   self._prepare_for_class(snake_case_ ,    snake_case_ ,    return_labels=snake_case_       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   self._prepare_for_class(snake_case_ ,    snake_case_ ,    return_labels=snake_case_       )
										check_equivalence(snake_case_ ,    snake_case_ ,    snake_case_ ,    {'output_hidden_states': True}       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case_       )






		@slow
		def   __snake_case			(						self       ):
						for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   TFRegNetModel.from_pretrained(snake_case_       )
										self.assertIsNotNone(snake_case_       )






def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
				return image


@require_tf
@require_vision
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		@cached_property
		def   __snake_case			(						self       ):
						return (
						    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
						    if is_vision_available()
						    else None
						)






		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.default_image_processor
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   prepare_img()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   image_processor(images=snake_case_ ,    return_tensors='tf'       )

						# forward pass
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(**snake_case_ ,    training=snake_case_       )

						# verify the logits
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tf.TensorShape((1, 1000)       )
						self.assertEqual(outputs.logits.shape ,    snake_case_       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   tf.constant([-0.41_80, -1.50_51, -3.48_36]       )

						tf.debugging.assert_near(outputs.logits[0, :3] ,    snake_case_ ,    atol=1E-4       )







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A        =			input('Enter image url: ').strip()
				print(f"""Downloading image from {url} ...""")
				A        =			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A        =			soup.find('meta', {'property': 'og:image'})['content']
				A        =			requests.get(image_url).content
				A        =			f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(f"""Done. Image saved to disk as {file_name}.""")