




'''simple docstring'''





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    :






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=[10, 20, 30, 40] ,_SCREAMING_SNAKE_CASE=[1, 1, 2, 1] ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE="relu" ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=None ,)							->      Tuple:
          _snake_case							=				parent
          _snake_case							=				batch_size
          _snake_case							=				image_size
          _snake_case							=				num_channels
          _snake_case							=				embeddings_size
          _snake_case							=				hidden_sizes
          _snake_case							=				depths
          _snake_case							=				is_training
          _snake_case							=				use_labels
          _snake_case							=				hidden_act
          _snake_case							=				num_labels
          _snake_case							=				scope
          _snake_case							=				len(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

          _snake_case							=				self.get_config()

          return config, pixel_values, labels






     def     _lowercase     (			self   )							->      List[str]:
          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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Any:
          _snake_case							=				TFResNetModel(config=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          _snake_case							=				self.num_labels
          _snake_case							=				TFResNetForImageClassification(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,labels=_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels)   )






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				self.prepare_config_and_inputs()
          _snake_case							, _snake_case							, _snake_case							=				config_and_inputs
          _snake_case							=				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_tf
class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Tuple       =						(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE_							:   int       =						(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_							:   Dict       =						False
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						False
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						False
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						False
     SCREAMING_SNAKE_CASE_							:   int       =						False






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				TFResNetModelTester(self   )
          _snake_case							=				ConfigTester(self ,config_class=_SCREAMING_SNAKE_CASE ,has_text_modality=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[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     _lowercase     (			self   )							->      int:
          return






     @unittest.skip(reason="ResNet does not use inputs_embeds"   )
     def     _lowercase     (			self   )							->      List[str]:
          pass






     @unittest.skip(reason="ResNet does not support input and output embeddings"   )
     def     _lowercase     (			self   )							->      Dict:
          pass






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

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

               _snake_case							=				["pixel_values"]
               self.assertListEqual(arg_names[:1] ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Tuple:
          def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )

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

               _snake_case							=				self.model_tester.num_stages
               self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,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] ,)

          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case							=				["basic", "bottleneck"]
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _snake_case							=				layer_type
                    _snake_case							=				True
                    check_hidden_states_output(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

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

                    check_hidden_states_output(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      Dict:
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case							=				TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def      __a     (       )						->			str:

     """simple docstring"""




     _snake_case							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
     return image





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






     @cached_property
     def     _lowercase     (			self   )							->      List[Any]:
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
              if is_vision_available()
              else None
          )






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

          _snake_case							=				self.default_image_processor
          _snake_case							=				prepare_img()
          _snake_case							=				image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="tf"   )

          # forward pass
          _snake_case							=				model(**_SCREAMING_SNAKE_CASE   )

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

          _snake_case							=				tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]   )

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








'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None







UpperCamelCase_						:      Any       =						namedtuple('''CoinsDistribResult''', '''moves excess''')





def      __a     (       _UpperCamelCase:   TreeNode | None   )						->			int:

     """simple docstring"""




     if root is None:
          return 0

     # Validation
     def count_nodes(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

     def count_coins(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_coins(node.left   ) + count_coins(node.right   ) + node.data

     if count_nodes(_UpperCamelCase   ) != count_coins(_UpperCamelCase   ):
          raise ValueError("The nodes number should be same as the number of coins"   )

     # Main calculation
     def get_distrib(_UpperCamelCase:   TreeNode | None   ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0 ,						1   )

          _snake_case							, _snake_case							=				get_distrib(node.left   )
          _snake_case							, _snake_case							=				get_distrib(node.right   )

          _snake_case							=				1 - left_distrib_excess
          _snake_case							=				1 - right_distrib_excess

          _snake_case							=				(
              left_distrib_moves
              + right_distrib_moves
              + abs(_UpperCamelCase   )
              + abs(_UpperCamelCase   )
          )
          _snake_case							=				node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_UpperCamelCase ,						_UpperCamelCase   )

     return get_distrib(_UpperCamelCase   )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


