






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     a    :



      """simple docstring"""


      def __init__(      self					:			List[str]		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			List[str]=3		,      __lowercase					:			Any=32		,      __lowercase					:			List[Any]=3		,      __lowercase					:			Tuple=10		,      __lowercase					:			List[Any]=[10, 20, 30, 40]		,      __lowercase					:			int=[1, 1, 2, 1]		,      __lowercase					:			Dict=True		,      __lowercase					:			Dict=True		,      __lowercase					:			List[str]="relu"		,      __lowercase					:			Optional[Any]=3		,      __lowercase					:			Optional[Any]=None		,      )							->      List[Any]:
        __UpperCAmelCase							:	List[Any]           =  parent
        __UpperCAmelCase							:	Optional[int]           =  batch_size
        __UpperCAmelCase							:	Dict           =  image_size
        __UpperCAmelCase							:	Tuple           =  num_channels
        __UpperCAmelCase							:	List[Any]           =  embeddings_size
        __UpperCAmelCase							:	Dict           =  hidden_sizes
        __UpperCAmelCase							:	Tuple           =  depths
        __UpperCAmelCase							:	Optional[Any]           =  is_training
        __UpperCAmelCase							:	str           =  use_labels
        __UpperCAmelCase							:	Any           =  hidden_act
        __UpperCAmelCase							:	Union[str, Any]           =  num_labels
        __UpperCAmelCase							:	Union[str, Any]           =  scope
        __UpperCAmelCase							:	List[Any]           =  len(__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Tuple:
        __UpperCAmelCase							:	List[Any]           =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

        __UpperCAmelCase							:	int           =  None
        if self.use_labels:
          __UpperCAmelCase							:	Optional[Any]           =  ids_tensor([self.batch_size]		,      self.num_labels					)

        __UpperCAmelCase							:	Optional[int]           =  self.get_config()

        return config, pixel_values, labels
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Union[str, Any]:
        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 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			List[Any]		,      __lowercase					:			List[Any]					)							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  TFResNetModel(config=__lowercase					)
        __UpperCAmelCase							:	List[str]           =  model(__lowercase					)
        # 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 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Any		,      __lowercase					:			Dict		,      __lowercase					:			int					)							->      str:
        __UpperCAmelCase							:	str           =  self.num_labels
        __UpperCAmelCase							:	str           =  TFResNetForImageClassification(__lowercase					)
        __UpperCAmelCase							:	int           =  model(__lowercase		,      labels=__lowercase					)
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)					)




      def 	UpperCAmelCase      (      self					:			Dict					)							->      Optional[int]:
        __UpperCAmelCase							:	List[Any]           =  self.prepare_config_and_inputs()
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Dict           =  config_and_inputs
        __UpperCAmelCase							:	Tuple           =  {"""pixel_values""": pixel_values}
        return config, inputs_dict


@require_tf
class     a    ( lowercase__							,       lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Dict							       =	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      a  : List[str]							       =	(
          {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      a  : Tuple							       =	False
      a  : Tuple							       =	False
      a  : Optional[int]							       =	False
      a  : Any							       =	False
      a  : Any							       =	False
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Any:
        __UpperCAmelCase							:	str           =  TFResNetModelTester(self					)
        __UpperCAmelCase							:	List[Any]           =  ConfigTester(self		,      config_class=__lowercase		,      has_text_modality=__lowercase					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Union[str, Any]:
        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 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[Any]:
        return
      @unittest.skip(reason="""ResNet does not use inputs_embeds"""					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      List[str]:
        pass
      @unittest.skip(reason="""ResNet does not support input and output embeddings"""					)
      def 	UpperCAmelCase      (      self					:			int					)							->      List[str]:
        pass
      def 	UpperCAmelCase      (      self					:			Any					)							->      Tuple:
        __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  self.model_tester.prepare_config_and_inputs_for_common()

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

          __UpperCAmelCase							:	int           =  ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	Dict           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Tuple:
        def check_hidden_states_output(__lowercase					:			Optional[Any]		,      __lowercase					:			int		,      __lowercase					:			Any					):
          __UpperCAmelCase							:	int           =  model_class(__lowercase					)
          __UpperCAmelCase							:	str           =  model(**self._prepare_for_class(__lowercase		,      __lowercase					)					)

          __UpperCAmelCase							:	Any           =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          __UpperCAmelCase							:	List[Any]           =  self.model_tester.num_stages
          self.assertEqual(len(__lowercase					)		,      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]		,      )

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

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

            check_hidden_states_output(__lowercase		,      __lowercase		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      int:
        __UpperCAmelCase							:	int           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase					)




      @slow
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Tuple:
        for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase							:	int           =  TFResNetModel.from_pretrained(__lowercase					)
          self.assertIsNotNone(__lowercase					)






def 				lowerCamelCase__      (    ):
  __UpperCAmelCase							:	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 	UpperCAmelCase      (      self					:			str					)							->      List[str]:
        return (
            AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
            if is_vision_available()
            else None
        )




      @slow
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      int:
        __UpperCAmelCase							:	int           =  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

        __UpperCAmelCase							:	Dict           =  self.default_image_processor
        __UpperCAmelCase							:	str           =  prepare_img()
        __UpperCAmelCase							:	Optional[Any]           =  image_processor(images=__lowercase		,      return_tensors="""tf"""					)

        # forward pass
        __UpperCAmelCase							:	Optional[Any]           =  model(**__lowercase					)

        # verify the logits
        __UpperCAmelCase							:	Optional[int]           =  tf.TensorShape((1, 1000)					)
        self.assertEqual(outputs.logits.shape		,      __lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  tf.constant([-11.1_069, -9.7_877, -8.3_777]					)

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










from __future__ import annotations

from math import pi






def 				lowerCamelCase__      (    __lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     float		):

  if (inductance, frequency, reactance).count(0		) != 1:
    raise ValueError("""One and only one argument must be 0"""		)
  if inductance < 0:
    raise ValueError("""Inductance cannot be negative"""		)
  if frequency < 0:
    raise ValueError("""Frequency cannot be negative"""		)
  if reactance < 0:
    raise ValueError("""Inductive reactance cannot be negative"""		)
  if inductance == 0:
    return {"inductance": reactance / (2 * pi * frequency)}
  elif frequency == 0:
    return {"frequency": reactance / (2 * pi * inductance)}
  elif reactance == 0:
    return {"reactance": 2 * pi * frequency * inductance}
  else:
    raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


