

def  _snake_case		(snake_case_					:     list  )							->		bool:
  if not isinstance(snake_case_    ,       snake_case_  ):
    raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'  )
  if len(snake_case_  ) == 0:
    raise ValueError('Input list must be a non empty list'  )
  if len(snake_case_  ) == 1 and series[0] != 0:
    return True
  __UpperCAmelCase	:  Optional[Any]		=     []
  __UpperCAmelCase	:  Optional[int]		=     len(snake_case_  )
  for i in range(0    ,       snake_case_  ):
    if series[i] == 0:
      raise ValueError('Input series cannot have 0 as an element'  )
    rec_series.append(1 / series[i]  )
  __UpperCAmelCase	:  List[str]		=     rec_series[1] - rec_series[0]
  for index in range(2    ,       snake_case_  ):
    if rec_series[index] - rec_series[index - 1] != common_diff:
      return False
  return True





def  _snake_case		(snake_case_					:     list  )							->		float:
  if not isinstance(snake_case_    ,       snake_case_  ):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'  )
  if len(snake_case_  ) == 0:
    raise ValueError('Input list must be a non empty list'  )
  __UpperCAmelCase	:  Optional[int]		=     0
  for val in series:
    answer += 1 / val
  return len(snake_case_  ) / answer


if __name__ == "__main__":
      import doctest

      doctest.testmod()








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_ (			_lowerCAmelCase):




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     self.config_class(**self.inputs_dict							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'hidden_sizes'							)							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'num_encoder_blocks'							)							)



class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=64  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=[2, 2, 2, 2]  ,  _UpperCAmelCase=[8, 4, 2, 1]  ,  _UpperCAmelCase=[16, 32, 64, 128]  ,  _UpperCAmelCase=[1, 4, 8, 16]  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=None  ,  ):
   __UpperCAmelCase	:  Optional[int]		=     parent
   __UpperCAmelCase	:  str		=     batch_size
   __UpperCAmelCase	:  Optional[int]		=     image_size
   __UpperCAmelCase	:  Dict		=     num_channels
   __UpperCAmelCase	:  int		=     num_encoder_blocks
   __UpperCAmelCase	:  str		=     sr_ratios
   __UpperCAmelCase	:  List[Any]		=     depths
   __UpperCAmelCase	:  Optional[int]		=     hidden_sizes
   __UpperCAmelCase	:  List[str]		=     downsampling_rates
   __UpperCAmelCase	:  Dict		=     is_training
   __UpperCAmelCase	:  Optional[int]		=     use_labels
   __UpperCAmelCase	:  Optional[Any]		=     hidden_act
   __UpperCAmelCase	:  int		=     hidden_dropout_prob
   __UpperCAmelCase	:  Tuple		=     initializer_range
   __UpperCAmelCase	:  str		=     num_labels
   __UpperCAmelCase	:  Union[str, Any]		=     scope




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

   __UpperCAmelCase	:  Tuple		=     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  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     PoolFormerModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  int		=     model(_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.image_size // 3_2.0
   self.parent.assertEqual(
       result.last_hidden_state.shape  ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.prepare_config_and_inputs()
   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     config_and_inputs
   __UpperCAmelCase	:  str		=     {'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase									=	(
     {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     PoolFormerModelTester(self							)
   __UpperCAmelCase	:  List[str]		=     PoolFormerConfigTester(self  ,  config_class=_UpperCAmelCase							)




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




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 @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(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.eval()

     with torch.no_grad():
       __UpperCAmelCase	:  Dict		=     model(**self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)							)

     __UpperCAmelCase	:  Tuple		=     outputs.hidden_states

     __UpperCAmelCase	:  Tuple		=     self.model_tester.num_encoder_blocks
     self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

     # 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,
         ]  ,  )

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  Optional[int]		=     True
     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

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

     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)




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

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  Dict		=     True

   for model_class in self.all_model_classes:
     if model_class in get_values(_UpperCAmelCase							):
       continue
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.train()
     __UpperCAmelCase	:  Any		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[Any]		=     model(**_UpperCAmelCase							).loss
     loss.backward()




 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()

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

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




 @slow
 def     a       (			self							):
   for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase	:  Dict		=     PoolFormerModel.from_pretrained(_UpperCAmelCase							)
     self.assertIsNotNone(_UpperCAmelCase							)





def  _snake_case		()							->		str:
  __UpperCAmelCase	:  Union[str, Any]		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image







@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     PoolFormerImageProcessor()
   __UpperCAmelCase	:  Tuple		=     PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'							).to(_UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     image_processor(images=prepare_img()  ,  return_tensors='pt'							).to(_UpperCAmelCase							)

   # forward pass
   with torch.no_grad():
     __UpperCAmelCase	:  Tuple		=     model(**_UpperCAmelCase							)

   # verify the logits
   __UpperCAmelCase	:  Optional[Any]		=     torch.Size((1, 1_000)							)
   self.assertEqual(outputs.logits.shape  ,  _UpperCAmelCase							)

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





