"""simple docstring"""


import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
 from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor






class        __UpperCAmelCase						(     __lowerCAmelCase     ):

    def 		_a     (				self				):
          lowerCamelCase__ =self.config_class(**self.inputs_dict				)
          self.parent.assertTrue(hasattr(_lowerCamelCase     ,			"hidden_sizes"				)				)
          self.parent.assertTrue(hasattr(_lowerCamelCase     ,			"num_encoder_blocks"				)				)






class        __UpperCAmelCase						:

    def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase=13     ,			_lowerCamelCase=64     ,			_lowerCamelCase=3     ,			_lowerCamelCase=4     ,			_lowerCamelCase=[2, 2, 2, 2]     ,			_lowerCamelCase=[8, 4, 2, 1]     ,			_lowerCamelCase=[16, 32, 64, 128]     ,			_lowerCamelCase=[1, 4, 8, 16]     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=3     ,			_lowerCamelCase=None     ,			):
          lowerCamelCase__ =parent
          lowerCamelCase__ =batch_size
          lowerCamelCase__ =image_size
          lowerCamelCase__ =num_channels
          lowerCamelCase__ =num_encoder_blocks
          lowerCamelCase__ =sr_ratios
          lowerCamelCase__ =depths
          lowerCamelCase__ =hidden_sizes
          lowerCamelCase__ =downsampling_rates
          lowerCamelCase__ =is_training
          lowerCamelCase__ =use_labels
          lowerCamelCase__ =hidden_act
          lowerCamelCase__ =hidden_dropout_prob
          lowerCamelCase__ =initializer_range
          lowerCamelCase__ =num_labels
          lowerCamelCase__ =scope

    def 		_a     (				self				):
          lowerCamelCase__ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          lowerCamelCase__ =PoolFormerConfig(
              image_size=self.image_size     ,			num_channels=self.num_channels     ,			num_encoder_blocks=self.num_encoder_blocks     ,			depths=self.depths     ,			hidden_sizes=self.hidden_sizes     ,			hidden_act=self.hidden_act     ,			hidden_dropout_prob=self.hidden_dropout_prob     ,			initializer_range=self.initializer_range     ,			)

          return config, pixel_values, labels

    def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
          lowerCamelCase__ =PoolFormerModel(config=_lowerCamelCase				)
          model.to(_lowerCamelCase				)
          model.eval()
          lowerCamelCase__ =model(_lowerCamelCase				)
          lowerCamelCase__ =lowerCamelCase__ =self.image_size // 32.0
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,			(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)






    def 		_a     (				self				):
          lowerCamelCase__ =self.prepare_config_and_inputs()
          lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ =config_and_inputs
          lowerCamelCase__ ={"pixel_values": pixel_values}
          return config, inputs_dict






@require_torch
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
    A__				:       Optional[int]  							=	(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
    A__				:       str  							=	(
        {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    A__				:       List[Any]  							=	False
    A__				:       Dict  							=	False
    A__				:       Optional[int]  							=	False
    A__				:       Optional[Any]  							=	False
    A__				:       Optional[Any]  							=	False

    def 		_a     (				self				):
          lowerCamelCase__ =PoolFormerModelTester(self				)
          lowerCamelCase__ =PoolFormerConfigTester(self     ,			config_class=_lowerCamelCase				)

    def 		_a     (				self				):
          self.config_tester.run_common_tests()

    def 		_a     (				self				):
          lowerCamelCase__ =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase				)

    @unittest.skip("PoolFormer does not use inputs_embeds"				)
    def 		_a     (				self				):
          pass

    @unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"				)
    def 		_a     (				self				):
          pass

    def 		_a     (				self				):
          def check_hidden_states_output(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
                lowerCamelCase__ =model_class(_lowerCamelCase				)
                model.to(_lowerCamelCase				)
                model.eval()

                with torch.no_grad():
                      lowerCamelCase__ =model(**self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase				)				)

                lowerCamelCase__ =outputs.hidden_states

                lowerCamelCase__ =self.model_tester.num_encoder_blocks
                self.assertEqual(len(_lowerCamelCase				)     ,			_lowerCamelCase				)

                # verify the first hidden states (first block)
                self.assertListEqual(
                    list(hidden_states[0].shape[-3:]				)     ,			[
                        self.model_tester.hidden_sizes[0],
                        self.model_tester.image_size // 4,
                        self.model_tester.image_size // 4,
                    ]     ,			)

          lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                lowerCamelCase__ =True
                check_hidden_states_output(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				)

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

                check_hidden_states_output(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				)

    def 		_a     (				self				):
          if not self.model_tester.is_training:
                return

          lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__ =True

          for model_class in self.all_model_classes:
                if model_class in get_values(_lowerCamelCase				):
                      continue
                lowerCamelCase__ =model_class(_lowerCamelCase				)
                model.to(_lowerCamelCase				)
                model.train()
                lowerCamelCase__ =self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase     ,			return_labels=_lowerCamelCase				)
                lowerCamelCase__ =model(**_lowerCamelCase				).loss
                loss.backward()

    def 		_a     (				self				):
          lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

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

                lowerCamelCase__ =["pixel_values"]
                self.assertListEqual(arg_names[:1]     ,			_lowerCamelCase				)






    @slow
    def 		_a     (				self				):
          for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__ =PoolFormerModel.from_pretrained(_lowerCamelCase				)
                self.assertIsNotNone(_lowerCamelCase				)





def       lowerCamelCase_			( )							-> List[Any]:




      '''simple docstring'''



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






@require_torch
class        __UpperCAmelCase						(     unittest.TestCase     ):

    @slow
    def 		_a     (				self				):
          lowerCamelCase__ =PoolFormerImageProcessor()
          lowerCamelCase__ =PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12"				).to(_lowerCamelCase				)

          lowerCamelCase__ =image_processor(images=prepare_img()     ,			return_tensors="pt"				).to(_lowerCamelCase				)

          # forward pass
          with torch.no_grad():
                lowerCamelCase__ =model(**_lowerCamelCase				)

          # verify the logits
          lowerCamelCase__ =torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape     ,			_lowerCamelCase				)

          lowerCamelCase__ =torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]				).to(_lowerCamelCase				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,			_lowerCamelCase     ,			atol=1E-4				)				)



"""simple docstring"""


def       lowerCamelCase_			( __lowerCAmelCase    )							-> bool:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						if len(__lowerCAmelCase    ) == 1:
												return True
						try:
												lowerCamelCase__ =series[1] / series[0]
												for index in range(len(__lowerCAmelCase    ) - 1    ):
																		if series[index + 1] / series[index] != common_ratio:
																								return False
						except ZeroDivisionError:
												return False
						return True





def       lowerCamelCase_			( __lowerCAmelCase    )							-> float:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						lowerCamelCase__ =1
						for value in series:
												answer *= value
						return pow(__lowerCAmelCase       ,   1 / len(__lowerCAmelCase    )    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


