



'''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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		List[Any]     	=					self.config_class(**self.inputs_dict				)
        self.parent.assertTrue(hasattr(a_    ,"""hidden_sizes"""				)				)
        self.parent.assertTrue(hasattr(a_    ,"""num_encoder_blocks"""				)				)







class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=13    ,a_=64    ,a_=3    ,a_=4    ,a_=[2, 2, 2, 2]    ,a_=[8, 4, 2, 1]    ,a_=[16, 32, 64, 128]    ,a_=[1, 4, 8, 16]    ,a_=False    ,a_=True    ,a_="gelu"    ,a_=0.1    ,a_=0.02    ,a_=3    ,a_=None    ,)						->  List[str]:
        _UpperCAmelCase    :		Dict     	=					parent
        _UpperCAmelCase    :		Dict     	=					batch_size
        _UpperCAmelCase    :		int     	=					image_size
        _UpperCAmelCase    :		List[str]     	=					num_channels
        _UpperCAmelCase    :		Optional[Any]     	=					num_encoder_blocks
        _UpperCAmelCase    :		Union[str, Any]     	=					sr_ratios
        _UpperCAmelCase    :		Tuple     	=					depths
        _UpperCAmelCase    :		Tuple     	=					hidden_sizes
        _UpperCAmelCase    :		Any     	=					downsampling_rates
        _UpperCAmelCase    :		List[str]     	=					is_training
        _UpperCAmelCase    :		Any     	=					use_labels
        _UpperCAmelCase    :		str     	=					hidden_act
        _UpperCAmelCase    :		List[Any]     	=					hidden_dropout_prob
        _UpperCAmelCase    :		Tuple     	=					initializer_range
        _UpperCAmelCase    :		List[Any]     	=					num_labels
        _UpperCAmelCase    :		Optional[Any]     	=					scope







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[str]     	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

        _UpperCAmelCase    :		Union[str, Any]     	=					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 _snake_case					(				self    ,a_    ,a_    ,a_				)						->  Tuple:
        _UpperCAmelCase    :		int     	=					PoolFormerModel(config=a_				)
        model.to(a_				)
        model.eval()
        _UpperCAmelCase    :		str     	=					model(a_				)
        _UpperCAmelCase    :		Optional[int]     	=					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 _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Union[str, Any]     	=					self.prepare_config_and_inputs()
        _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		List[str]     	=					config_and_inputs
        _UpperCAmelCase    :		List[str]     	=					{"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


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

 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		Dict     	=					PoolFormerModelTester(self				)
        _UpperCAmelCase    :		int     	=					PoolFormerConfigTester(self    ,config_class=a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        self.config_tester.run_common_tests()







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_				)







 @unittest.skip("""PoolFormer does not use inputs_embeds"""				)
 def _snake_case					(				self				)						->  Optional[int]:
        pass







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







 def _snake_case					(				self				)						->  Any:
        def check_hidden_states_output(a_    ,a_    ,a_				):
               _UpperCAmelCase    :		Optional[int]     	=					model_class(a_				)
               model.to(a_				)
               model.eval()

               with torch.no_grad():
                      _UpperCAmelCase    :		List[Any]     	=					model(**self._prepare_for_class(a_    ,a_				)				)

               _UpperCAmelCase    :		Union[str, Any]     	=					outputs.hidden_states

               _UpperCAmelCase    :		List[Any]     	=					self.model_tester.num_encoder_blocks
               self.assertEqual(len(a_				)    ,a_				)

               # 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    :		List[str]     	=					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(a_    ,a_    ,a_				)

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

               check_hidden_states_output(a_    ,a_    ,a_				)







 def _snake_case					(				self				)						->  Optional[int]:
        if not self.model_tester.is_training:
               return

        _UpperCAmelCase	,_UpperCAmelCase    :		Dict     	=					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(a_				):
                      continue
               _UpperCAmelCase    :		Tuple     	=					model_class(a_				)
               model.to(a_				)
               model.train()
               _UpperCAmelCase    :		List[str]     	=					self._prepare_for_class(a_    ,a_    ,return_labels=a_				)
               _UpperCAmelCase    :		Optional[int]     	=					model(**a_				).loss
               loss.backward()







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

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

               _UpperCAmelCase    :		Any     	=					["""pixel_values"""]
               self.assertListEqual(arg_names[:1]    ,a_				)







 @slow
 def _snake_case					(				self				)						->  Union[str, Any]:
        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase    :		int     	=					PoolFormerModel.from_pretrained(a_				)
               self.assertIsNotNone(a_				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       return image



@require_torch
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 @slow
 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		int     	=					PoolFormerImageProcessor()
        _UpperCAmelCase    :		int     	=					PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""				).to(a_				)

        _UpperCAmelCase    :		Dict     	=					image_processor(images=prepare_img()    ,return_tensors="""pt"""				).to(a_				)

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

        # verify the logits
        _UpperCAmelCase    :		List[str]     	=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape    ,a_				)

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









"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)

 _UpperCamelCase      :	NDArray[floataa]					      =  np.zeros((rows, 1)  ,	dtype=__lowerCAmelCase						)
 for row in reversed(range(__lowerCAmelCase						)						):
  _UpperCamelCase      :	List[str]					      =  0
  for col in range(row + 1  ,	__lowerCAmelCase						):
   total += coefficients[row, col] * x[col]

  _UpperCamelCase      :	Dict					      =  (vector[row] - total) / coefficients[row, row]

 return x






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)
 if rows != columns:
  return np.array(()  ,	dtype=__lowerCAmelCase						)

 # augmented matrix
 _UpperCamelCase      :	NDArray[floataa]					      =  np.concatenate((coefficients, vector)  ,	axis=1						)
 _UpperCamelCase      :	List[str]					      =  augmented_mat.astype("float64"						)

 # scale the matrix leaving it triangular
 for row in range(rows - 1						):
  _UpperCamelCase      :	Optional[Any]					      =  augmented_mat[row, row]
  for col in range(row + 1  ,	__lowerCAmelCase						):
   _UpperCamelCase      :	Union[str, Any]					      =  augmented_mat[col, row] / pivot
   augmented_mat[col, :] -= factor * augmented_mat[row, :]

 _UpperCamelCase      :	Any					      =  retroactive_resolution(
     augmented_mat[:, 0:columns]  ,	augmented_mat[:, columns : columns + 1]						)

 return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()



