

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, require_vision, 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, GLPNConfig, GLPNForDepthEstimation, GLPNModel
 from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor







class   __lowercase				(_UpperCAmelCase			):
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(A_				,							'''hidden_sizes''' ) )
       self.parent.assertTrue(hasattr(A_				,							'''num_attention_heads''' ) )
       self.parent.assertTrue(hasattr(A_				,							'''num_encoder_blocks''' ) )







class   __lowercase				:
 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_=[1, 2, 4, 8]				,							A_=True				,							A_=True				,							A_="gelu"				,							A_=0.1				,							A_=0.1				,							A_=0.02				,							A_=16				,							A_=3				,							A_=None				,							) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = parent
       __lowerCAmelCase :						List[Any] = batch_size
       __lowerCAmelCase :						List[Any] = image_size
       __lowerCAmelCase :						Optional[Any] = num_channels
       __lowerCAmelCase :						List[str] = num_encoder_blocks
       __lowerCAmelCase :						List[Any] = sr_ratios
       __lowerCAmelCase :						Tuple = depths
       __lowerCAmelCase :						Optional[Any] = hidden_sizes
       __lowerCAmelCase :						str = downsampling_rates
       __lowerCAmelCase :						int = num_attention_heads
       __lowerCAmelCase :						Dict = is_training
       __lowerCAmelCase :						Optional[int] = use_labels
       __lowerCAmelCase :						Dict = hidden_act
       __lowerCAmelCase :						Any = hidden_dropout_prob
       __lowerCAmelCase :						Tuple = attention_probs_dropout_prob
       __lowerCAmelCase :						Optional[int] = initializer_range
       __lowerCAmelCase :						str = decoder_hidden_size
       __lowerCAmelCase :						Any = num_labels
       __lowerCAmelCase :						Tuple = scope
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       __lowerCAmelCase :						List[Any] = None
       if self.use_labels:
             __lowerCAmelCase :						str = ids_tensor([self.batch_size, self.image_size, self.image_size]				,							self.num_labels )

       __lowerCAmelCase :						Tuple = self.get_config()
       return config, pixel_values, labels
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       return GLPNConfig(
           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				,							num_attention_heads=self.num_attention_heads				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							initializer_range=self.initializer_range				,							decoder_hidden_size=self.decoder_hidden_size				,							)
 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ ) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = GLPNModel(config=A_ )
       model.to(A_ )
       model.eval()
       __lowerCAmelCase :						Any = model(A_ )
       __lowerCAmelCase :						Any = self.image_size // (self.downsampling_rates[-1] * 2)
       self.parent.assertEqual(
           result.last_hidden_state.shape				,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.num_labels
       __lowerCAmelCase :						Dict = GLPNForDepthEstimation(A_ )
       model.to(A_ )
       model.eval()
       __lowerCAmelCase :						str = model(A_ )
       self.parent.assertEqual(result.predicted_depth.shape				,							(self.batch_size, self.image_size, self.image_size) )
       __lowerCAmelCase :						Any = model(A_				,							labels=A_ )
       self.parent.assertEqual(result.predicted_depth.shape				,							(self.batch_size, self.image_size, self.image_size) )





 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = self.prepare_config_and_inputs()
       __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Dict = config_and_inputs
       __lowerCAmelCase :						Union[str, Any] = {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class   __lowercase				(_UpperCAmelCase		,       _UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 _UpperCamelCase        =  (
     {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
 )

 _UpperCamelCase        =  False
 _UpperCamelCase        =  False
 _UpperCamelCase        =  False
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Any = GLPNModelTester(self )
       __lowerCAmelCase :						Any = GLPNConfigTester(self				,							config_class=A_ )
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       self.config_tester.run_common_tests()
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_ )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*A_ )
 @unittest.skip('''GLPN does not use inputs_embeds''' )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods''' )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase :						Tuple = self.model_tester.prepare_config_and_inputs_for_common()

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

             __lowerCAmelCase :						str = ['''pixel_values''']
             self.assertListEqual(arg_names[:1]				,							A_ )
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase :						List[str] = self.model_tester.prepare_config_and_inputs_for_common()
       __lowerCAmelCase :						str = True

       for model_class in self.all_model_classes:
             __lowerCAmelCase :						Any = True
             __lowerCAmelCase :						str = False
             __lowerCAmelCase :						List[Any] = True
             __lowerCAmelCase :						Dict = model_class(A_ )
             model.to(A_ )
             model.eval()
             with torch.no_grad():
                   __lowerCAmelCase :						Optional[int] = model(**self._prepare_for_class(A_				,							A_ ) )
             __lowerCAmelCase :						List[Any] = outputs.attentions

             __lowerCAmelCase :						str = sum(self.model_tester.depths )
             self.assertEqual(len(A_ )				,							A_ )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             __lowerCAmelCase :						str = True
             __lowerCAmelCase :						List[str] = model_class(A_ )
             model.to(A_ )
             model.eval()
             with torch.no_grad():
                   __lowerCAmelCase :						Optional[int] = model(**self._prepare_for_class(A_				,							A_ ) )
             __lowerCAmelCase :						List[Any] = outputs.attentions

             self.assertEqual(len(A_ )				,							A_ )

             # verify the first attentions (first block, first layer)
             __lowerCAmelCase :						Any = (self.model_tester.image_size // 4) ** 2
             __lowerCAmelCase :						Optional[int] = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(attentions[0].shape[-3:] )				,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]				,							)

             # verify the last attentions (last block, last layer)
             __lowerCAmelCase :						Tuple = (self.model_tester.image_size // 32) ** 2
             __lowerCAmelCase :						List[str] = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
             self.assertListEqual(
                 list(attentions[-1].shape[-3:] )				,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]				,							)
             __lowerCAmelCase :						int = len(A_ )

             # Check attention is always last and order is fine
             __lowerCAmelCase :						Optional[Any] = True
             __lowerCAmelCase :						Any = True
             __lowerCAmelCase :						str = model_class(A_ )
             model.to(A_ )
             model.eval()
             with torch.no_grad():
                   __lowerCAmelCase :						Tuple = model(**self._prepare_for_class(A_				,							A_ ) )

             self.assertEqual(out_len + 1				,							len(A_ ) )

             __lowerCAmelCase :						Dict = outputs.attentions

             self.assertEqual(len(A_ )				,							A_ )
             # verify the first attentions (first block, first layer)
             __lowerCAmelCase :						int = (self.model_tester.image_size // 4) ** 2
             __lowerCAmelCase :						Any = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:] )				,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]				,							)
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       def check_hidden_states_output(A_				,							A_				,							A_ ):
             __lowerCAmelCase :						Optional[int] = model_class(A_ )
             model.to(A_ )
             model.eval()

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

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

             __lowerCAmelCase :						Optional[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,
                 ]				,							)

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

       for model_class in self.all_model_classes:
             __lowerCAmelCase :						Dict = True
             check_hidden_states_output(A_				,							A_				,							A_ )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __lowerCAmelCase :						Union[str, Any] = True

             check_hidden_states_output(A_				,							A_				,							A_ )
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       if not self.model_tester.is_training:
             return

       __lowerCAmelCase,							__lowerCAmelCase :						Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
       __lowerCAmelCase :						Union[str, Any] = True

       for model_class in self.all_model_classes:
             if model_class in get_values(A_ ):
                   continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             if model_class.__name__ == "GLPNForDepthEstimation":
                   __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Dict = inputs_dict['''pixel_values'''].shape
                   __lowerCAmelCase :						Tuple = torch.zeros(
                       [self.model_tester.batch_size, height, width]				,							device=A_ ).long()
             __lowerCAmelCase :						List[str] = model_class(A_ )
             model.to(A_ )
             model.train()
             __lowerCAmelCase :						Dict = self._prepare_for_class(A_				,							A_				,							return_labels=A_ )
             __lowerCAmelCase :						int = model(**A_ ).loss
             loss.backward()





 @slow
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __lowerCAmelCase :						Optional[int] = GLPNModel.from_pretrained(A_ )
             self.assertIsNotNone(A_ )







def 		_lowercase			(					):
      __lowerCAmelCase :						Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
      return image







@require_torch
@require_vision
@slow
class   __lowercase				(unittest.TestCase			):
 @slow
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
       __lowerCAmelCase :						Optional[int] = GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A_ )

       __lowerCAmelCase :						int = prepare_img()
       __lowerCAmelCase :						Optional[Any] = image_processor(images=A_				,							return_tensors='''pt''' ).to(A_ )

       # forward pass
       with torch.no_grad():
             __lowerCAmelCase :						Optional[Any] = model(**A_ )

       # verify the predicted depth
       __lowerCAmelCase :						Any = torch.Size([1, 480, 640] )
       self.assertEqual(outputs.predicted_depth.shape				,							A_ )

       __lowerCAmelCase :						int = torch.tensor(
           [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] ).to(A_ )

       self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]				,							A_				,							atol=1e-4 ) )





import heapq







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = [(0, start)]  # cost from start node,end node
      __lowerCAmelCase :						Any = set()
      while heap:
            ((__lowerCAmelCase),							(__lowerCAmelCase)) :						str = heapq.heappop(lowercase__						)
            if u in visited:
                  continue
            visited.add(lowercase__						)
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  __lowerCAmelCase :						str = cost + c
                  heapq.heappush(lowercase__       ,     (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()


