



'''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 							_lowerCAmelCase	(  _a							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		Dict						=					self.config_class(**self.inputs_dict  )
    self.parent.assertTrue(hasattr(lowerCAmelCase__					,    """hidden_sizes"""  )  )
    self.parent.assertTrue(hasattr(lowerCAmelCase__					,    """num_encoder_blocks"""  )  )


class 							_lowerCAmelCase	:


 """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					,    )    ->    Optional[int]:
    A_  :		Tuple						=					parent
    A_  :		List[str]						=					batch_size
    A_  :		str						=					image_size
    A_  :		Dict						=					num_channels
    A_  :		Tuple						=					num_encoder_blocks
    A_  :		Dict						=					sr_ratios
    A_  :		List[str]						=					depths
    A_  :		Any						=					hidden_sizes
    A_  :		Optional[int]						=					downsampling_rates
    A_  :		Union[str, Any]						=					is_training
    A_  :		int						=					use_labels
    A_  :		Union[str, Any]						=					hidden_act
    A_  :		Dict						=					hidden_dropout_prob
    A_  :		Optional[Any]						=					initializer_range
    A_  :		int						=					num_labels
    A_  :		Union[str, Any]						=					scope







 def       UpperCAmelCase_       (      self  )    ->    str:
    A_  :		List[Any]						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

    A_  :		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       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Dict:
    A_  :		Union[str, Any]						=					PoolFormerModel(config=lowerCAmelCase__  )
    model.to(lowerCAmelCase__  )
    model.eval()
    A_  :		Optional[Any]						=					model(lowerCAmelCase__  )
    A_  :		Union[str, Any]						=					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  )    ->    List[Any]:
    A_  :		List[str]						=					self.prepare_config_and_inputs()
    A_						,  A_						,  A_  :		Optional[Any]						=					config_and_inputs
    A_  :		str						=					{"""pixel_values""": pixel_values}
    return config, inputs_dict


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


 """simple docstring"""

 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       UpperCAmelCase_       (      self  )    ->    Any:
    A_  :		Union[str, Any]						=					PoolFormerModelTester(self  )
    A_  :		List[Any]						=					PoolFormerConfigTester(self					,    config_class=lowerCAmelCase__  )







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    self.config_tester.run_common_tests()







 def       UpperCAmelCase_       (      self  )    ->    str:
    A_  :		Optional[int]						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCAmelCase__  )







 @unittest.skip("""PoolFormer does not use inputs_embeds"""  )
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    pass







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







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    def check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		str						=					model_class(lowerCAmelCase__  )
       model.to(lowerCAmelCase__  )
       model.eval()

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

       A_  :		Optional[Any]						=					outputs.hidden_states

       A_  :		Tuple						=					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_  :		Any						=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A_  :		List[str]						=					True
       check_hidden_states_output(lowerCAmelCase__					,    lowerCAmelCase__					,    lowerCAmelCase__  )

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

       check_hidden_states_output(lowerCAmelCase__					,    lowerCAmelCase__					,    lowerCAmelCase__  )







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    if not self.model_tester.is_training:
       return

    A_						,  A_  :		int						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		str						=					True

    for model_class in self.all_model_classes:
       if model_class in get_values(lowerCAmelCase__  ):
          continue
       A_  :		List[Any]						=					model_class(lowerCAmelCase__  )
       model.to(lowerCAmelCase__  )
       model.train()
       A_  :		List[str]						=					self._prepare_for_class(lowerCAmelCase__					,    lowerCAmelCase__					,    return_labels=lowerCAmelCase__  )
       A_  :		List[Any]						=					model(**lowerCAmelCase__  ).loss
       loss.backward()







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		Tuple						=					self.model_tester.prepare_config_and_inputs_for_common()

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

       A_  :		Optional[Any]						=					["""pixel_values"""]
       self.assertListEqual(arg_names[:1]					,    lowerCAmelCase__  )







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A_  :		Dict						=					PoolFormerModel.from_pretrained(lowerCAmelCase__  )
       self.assertIsNotNone(lowerCAmelCase__  )



def     UpperCAmelCase     (    )   ->       Tuple:



   """simple docstring"""






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







@require_torch
class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 @slow
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[int]						=					PoolFormerImageProcessor()
    A_  :		Optional[Any]						=					PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""  ).to(lowerCAmelCase__  )

    A_  :		Any						=					image_processor(images=prepare_img()					,    return_tensors="""pt"""  ).to(lowerCAmelCase__  )

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

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

    A_  :		Union[str, Any]						=					torch.tensor([-0.6113, 0.1685, -0.0492]  ).to(lowerCAmelCase__  )
    self.assertTrue(torch.allclose(outputs.logits[0, :3]					,    lowerCAmelCase__					,    atol=1e-4  )  )

import heapq

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Dict:




       '''simple docstring'''






       _A=						[(0, start)]  # cost from start node,end node
       _A=						set()
       while heap:
              ((_A),      (_A))=						heapq.heappop(lowerCAmelCase_ )
              if u in visited:
                     continue
              visited.add(lowerCAmelCase_ )
              if u == end:
                     return cost
              for v, c in graph[u]:
                     if v in visited:
                            continue
                     _A=						cost + c
                     heapq.heappush(lowerCAmelCase_							,       (next_item, v) )
       return -1


UpperCAmelCase_    		=				{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

UpperCAmelCase_    		=				dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()