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   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						self.config_class(**self.inputs_dict							)
            self.parent.assertTrue(hasattr(lowerCAmelCase__					,   'hidden_sizes'							)							)
            self.parent.assertTrue(hasattr(lowerCAmelCase__					,   'num_encoder_blocks'							)							)





class   lowerCAmelCase    :



     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__=0.02					,   lowerCAmelCase__=3					,   lowerCAmelCase__=None					,   ):
            _A=						parent
            _A=						batch_size
            _A=						image_size
            _A=						num_channels
            _A=						num_encoder_blocks
            _A=						sr_ratios
            _A=						depths
            _A=						hidden_sizes
            _A=						downsampling_rates
            _A=						is_training
            _A=						use_labels
            _A=						hidden_act
            _A=						hidden_dropout_prob
            _A=						initializer_range
            _A=						num_labels
            _A=						scope



     def       a__	(       self							):
            _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.image_size, self.image_size]					,   self.num_labels							)

            _A=						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__							):
            _A=						PoolFormerModel(config=lowerCAmelCase__							)
            model.to(lowerCAmelCase__							)
            model.eval()
            _A=						model(lowerCAmelCase__							)
            _A=						_A=						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							):
            _A=						self.prepare_config_and_inputs()
            _A,      _A,      _A=						config_and_inputs
            _A=						{'pixel_values': pixel_values}
            return config, inputs_dict





@require_torch
class   lowerCAmelCase    (       _a      ,		_a      ,		unittest.TestCase      ):
     _SCREAMING_SNAKE_CASE  :  Dict		=(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE  :  Optional[int]		=(
         {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE  :  List[str]		=False
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		=False
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		=False
     _SCREAMING_SNAKE_CASE  :  Any		=False
     _SCREAMING_SNAKE_CASE  :  str		=False



     def       a__	(       self							):
            _A=						PoolFormerModelTester(self							)
            _A=						PoolFormerConfigTester(self					,   config_class=lowerCAmelCase__							)



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



     def       a__	(       self							):
            _A=						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__							):
                   _A=						model_class(lowerCAmelCase__							)
                   model.to(lowerCAmelCase__							)
                   model.eval()

                   with torch.no_grad():
                          _A=						model(**self._prepare_for_class(lowerCAmelCase__					,   lowerCAmelCase__							)							)

                   _A=						outputs.hidden_states

                   _A=						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,
                       ]					,   )

            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A=						True
                   check_hidden_states_output(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							)

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

                   check_hidden_states_output(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							)



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

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

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



     def       a__	(       self							):
            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()

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

                   _A=						['pixel_values']
                   self.assertListEqual(arg_names[:1]					,   lowerCAmelCase__							)





     @slow
     def       a__	(       self							):
            for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						PoolFormerModel.from_pretrained(lowerCAmelCase__							)
                   self.assertIsNotNone(lowerCAmelCase__							)


def 				UpperCamelCase					(     )		->    Tuple:




       '''simple docstring'''






       _A=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
       return image





@require_torch
class   lowerCAmelCase    (       unittest.TestCase      ):



     @slow
     def       a__	(       self							):
            _A=						PoolFormerImageProcessor()
            _A=						PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'							).to(lowerCAmelCase__							)

            _A=						image_processor(images=prepare_img()					,   return_tensors='pt'							).to(lowerCAmelCase__							)

            # forward pass
            with torch.no_grad():
                   _A=						model(**lowerCAmelCase__							)

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

            _A=						torch.tensor([-0.6113, 0.1685, -0.0492]							).to(lowerCAmelCase__							)
            self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   lowerCAmelCase__					,   atol=1E-4							)							)
from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


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