






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=[1_0, 2_0, 3_0, 4_0]       ,					__lowerCAmelCase=[1, 1, 2, 1]       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase="relu"       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=None       ,					):
								"""simple docstring"""
								__magic_name__		:Tuple        =      parent
								__magic_name__		:Optional[int]        =      batch_size
								__magic_name__		:str        =      image_size
								__magic_name__		:List[str]        =      num_channels
								__magic_name__		:Union[str, Any]        =      embeddings_size
								__magic_name__		:List[Any]        =      hidden_sizes
								__magic_name__		:List[Any]        =      depths
								__magic_name__		:Any        =      is_training
								__magic_name__		:Tuple        =      use_labels
								__magic_name__		:str        =      hidden_act
								__magic_name__		:List[str]        =      num_labels
								__magic_name__		:Optional[int]        =      scope
								__magic_name__		:List[Any]        =      len(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

								__magic_name__		:List[str]        =      self.get_config()

								return config, pixel_values, labels







				def 			A	(						self     ):
								"""simple docstring"""
								return ResNetConfig(
								    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       ,					image_size=self.image_size       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      TFResNetModel(config=__lowerCAmelCase     )
								__magic_name__		:Optional[int]        =      model(__lowerCAmelCase     )
								# 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 // 3_2, self.image_size // 3_2)       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.num_labels
								__magic_name__		:Tuple        =      TFResNetForImageClassification(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      model(__lowerCAmelCase       ,					labels=__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:List[str]        =      config_and_inputs
								__magic_name__		:Dict        =      {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_tf
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
				a__					       =  (
				    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      TFResNetModelTester(self     )
								__magic_name__		:int        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""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     ):
								"""simple docstring"""
								return







				@unittest.skip(reason='''ResNet does not use inputs_embeds'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				@unittest.skip(reason='''ResNet does not support input and output embeddings'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Optional[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:Optional[int]        =      model_class(__lowerCAmelCase     )
												__magic_name__		:str        =      inspect.signature(model.call     )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												__magic_name__		:str        =      [*signature.parameters.keys()]

												__magic_name__		:Any        =      ['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								def check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Tuple        =      model_class(__lowerCAmelCase     )
												__magic_name__		:int        =      model(**self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

												__magic_name__		:Union[str, Any]        =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												__magic_name__		:Dict        =      self.model_tester.num_stages
												self.assertEqual(len(__lowerCAmelCase     )       ,					expected_num_stages + 1     )

												# ResNet'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]       ,					)

								__magic_name__     ,				__magic_name__		:Tuple        =      self.model_tester.prepare_config_and_inputs_for_common()
								__magic_name__		:int        =      ['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																__magic_name__		:Tuple        =      layer_type
																__magic_name__		:Union[str, Any]        =      True
																check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__magic_name__		:Any        =      True

																check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:Tuple        =      TFResNetModel.from_pretrained(__lowerCAmelCase     )
												self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







				"""simple docstring"""






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

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







				@cached_property
				def 			A	(						self     ):
								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
								    if is_vision_available()
								    else None
								)







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

								__magic_name__		:List[str]        =      self.default_image_processor
								__magic_name__		:Optional[Any]        =      prepare_img()
								__magic_name__		:List[str]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''tf'''     )

								# forward pass
								__magic_name__		:Tuple        =      model(**__lowerCAmelCase     )

								# verify the logits
								__magic_name__		:str        =      tf.TensorShape((1, 1_0_0_0)     )
								self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

								__magic_name__		:str        =      tf.constant([-11.1069, -9.7877, -8.3777]     )

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














from __future__ import annotations

from math import pi, sqrt

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative''' )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative''' )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






