






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 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Tuple ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						List[str]=3 ,					__UpperCAmelCase      :						Dict=32 ,					__UpperCAmelCase      :						int=3 ,					__UpperCAmelCase      :						Optional[Any]=10 ,					__UpperCAmelCase      :						str=[10, 20, 30, 40] ,					__UpperCAmelCase      :						Optional[int]=[1, 1, 2, 1] ,					__UpperCAmelCase      :						Optional[Any]=True ,					__UpperCAmelCase      :						Any=True ,					__UpperCAmelCase      :						int="relu" ,					__UpperCAmelCase      :						Dict=3 ,					__UpperCAmelCase      :						str=None ,					)    ->List[Any]:




       """simple docstring"""


       a		  =      parent
       a		  =      batch_size
       a		  =      image_size
       a		  =      num_channels
       a		  =      embeddings_size
       a		  =      hidden_sizes
       a		  =      depths
       a		  =      is_training
       a		  =      use_labels
       a		  =      hidden_act
       a		  =      num_labels
       a		  =      scope
       a		  =      len(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Tuple:




       """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       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->str:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Any  )    ->List[Any]:




       """simple docstring"""


       a		  =      TFRegNetModel(config=__UpperCAmelCase  )
       a		  =      model(__UpperCAmelCase ,					training=__UpperCAmelCase  )
       # 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       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						int  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      self.num_labels
       a		  =      TFRegNetForImageClassification(__UpperCAmelCase  )
       a		  =      model(__UpperCAmelCase ,					labels=__UpperCAmelCase ,					training=__UpperCAmelCase  )
       self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)  )







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Dict:




       """simple docstring"""


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






@require_tf
class 		lowercase_					(	lowercase ,  lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      __snake_case       =  (
          {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      __snake_case       =  False
      __snake_case       =  False
      __snake_case       =  False
      __snake_case       =  False
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      TFRegNetModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase ,					has_text_modality=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return







      @unittest.skip(reason='''RegNet does not use inputs_embeds'''  )
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						List[Any]  )    ->Any:




       """simple docstring"""


       super().test_keras_fit()







      @unittest.skip(reason='''RegNet does not support input and output embeddings'''  )
      def       __lowerCAmelCase					(      self      :						str  )    ->Any:




       """simple docstring"""


       pass







      def       __lowerCAmelCase					(      self      :						int  )    ->Union[str, 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(__UpperCAmelCase  )
        a		  =      inspect.signature(model.call  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a		  =      [*signature.parameters.keys()]

        a		  =      ['''pixel_values''']
        self.assertListEqual(arg_names[:1] ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						str  )    ->str:




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       def check_hidden_states_output(__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Union[str, Any]  ):
        a		  =      model_class(__UpperCAmelCase  )
        a		  =      model(**self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase  ) ,					training=__UpperCAmelCase  )

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

        a		  =      self.model_tester.num_stages
        self.assertEqual(len(__UpperCAmelCase  ) ,					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] ,					)

       a  ,     a		  =      self.model_tester.prepare_config_and_inputs_for_common()
       a		  =      ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
        for layer_type in layers_type:
         a		  =      layer_type
         a		  =      True
         check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )

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

         check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						int  )    ->List[str]:




       """simple docstring"""


       a  ,     a		  =      self.model_tester.prepare_config_and_inputs_for_common()

       def check_equivalence(__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						List[str]={}  ):
        a		  =      model(__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					**__UpperCAmelCase  )
        a		  =      model(__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					**__UpperCAmelCase  ).to_tuple()

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

         recursive_check(__UpperCAmelCase ,					__UpperCAmelCase  )

       for model_class in self.all_model_classes:
        a		  =      model_class(__UpperCAmelCase  )

        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase  )
        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase  )
        check_equivalence(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )

        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase ,					return_labels=__UpperCAmelCase  )
        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase ,					return_labels=__UpperCAmelCase  )
        check_equivalence(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )

        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase  )
        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase  )
        check_equivalence(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					{'''output_hidden_states''': True}  )

        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase ,					return_labels=__UpperCAmelCase  )
        a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase ,					return_labels=__UpperCAmelCase  )
        check_equivalence(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					{'''output_hidden_states''': True}  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->List[str]:




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase  )







      @slow
      def       __lowerCAmelCase					(      self      :						Any  )    ->Dict:




       """simple docstring"""


       for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		  =      TFRegNetModel.from_pretrained(__UpperCAmelCase  )
        self.assertIsNotNone(__UpperCAmelCase  )
def 						_a    (      )							->			Tuple:
 a		  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
 return image






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






      '''simple docstring'''




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




       """simple docstring"""


       return (
           AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
           if is_vision_available()
           else None
       )







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




       """simple docstring"""


       a		  =      TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

       a		  =      self.default_image_processor
       a		  =      prepare_img()
       a		  =      image_processor(images=__UpperCAmelCase ,					return_tensors='''tf'''  )

       # forward pass
       a		  =      model(**__UpperCAmelCase ,					training=__UpperCAmelCase  )

       # verify the logits
       a		  =      tf.TensorShape((1, 1_000)  )
       self.assertEqual(outputs.logits.shape ,					__UpperCAmelCase  )

       a		  =      tf.constant([-0.4180, -1.5051, -3.4836]  )

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










def 						_a    (      a :int = 100 )							->			int:
 a		  =      n * (n + 1) * (2 * n + 1) / 6
 a		  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares )


if __name__ == "__main__":
     print(f"""{solution() = }""")


