



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       _lowercase				(  _lowerCAmelCase      ):





	"""simple docstring"""




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.config_class(**self.inputs_dict						)
						self.parent.assertTrue(hasattr(_UpperCAmelCase						,			"hidden_sizes"						)						)
						self.parent.assertTrue(hasattr(_UpperCAmelCase						,			"num_encoder_blocks"						)						)




class       _lowercase				:





	"""simple docstring"""




	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						,			):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						):
						"""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.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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  PoolFormerModel(config=_UpperCAmelCase						)
						model.to(_UpperCAmelCase						)
						model.eval()
						a   	=  model(_UpperCAmelCase						)
						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 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




@require_torch
class       _lowercase				(  _lowerCAmelCase, _lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
	__A		      =				(
	    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A		      =				False
	__A		      =				False
	__A		      =				False
	__A		      =				False
	__A		      =				False




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  PoolFormerModelTester(self						)
						a   	=  PoolFormerConfigTester(self						,			config_class=_UpperCAmelCase						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	@unittest.skip("PoolFormer does not use inputs_embeds"						)
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						pass




	@unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"						)
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						pass




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						def check_hidden_states_output(lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  model_class(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.eval()

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

											a   	=  outputs.hidden_states

											a   	=  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,
											    ]						,			)

						a   	=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											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 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if not self.model_tester.is_training:
											return

						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(_UpperCAmelCase						):
																continue
											a   	=  model_class(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.train()
											a   	=  self._prepare_for_class(_UpperCAmelCase						,			_UpperCAmelCase						,			return_labels=_UpperCAmelCase						)
											a   	=  model(**_UpperCAmelCase						).loss
											loss.backward()




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						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.forward						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a   	=  [*signature.parameters.keys()]

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




	@slow
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a   	=  PoolFormerModel.from_pretrained(_UpperCAmelCase						)
											self.assertIsNotNone(_UpperCAmelCase						)






def        a( )						->			str:
					"""simple docstring"""





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







@require_torch
class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	@slow
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  PoolFormerImageProcessor()
						a   	=  PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12"						).to(_UpperCAmelCase						)

						a   	=  image_processor(images=prepare_img()						,			return_tensors="pt"						).to(_UpperCAmelCase						)

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

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

						a   	=  torch.tensor([-0.6113, 0.1685, -0.0492]						).to(_UpperCAmelCase						)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]						,			_UpperCAmelCase						,			atol=1E-4						)						)



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							)							)





