




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       LevitForImageClassification,
       LevitForImageClassificationWithTeacher,
       LevitModel,
   )
   from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.config_class(**self.inputs_dict		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'hidden_sizes'		)		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'num_attention_heads'		)		)





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						int=13							,							_lowerCAmelCase    :						Dict=64							,							_lowerCAmelCase    :						Optional[Any]=3							,							_lowerCAmelCase    :						Dict=3							,							_lowerCAmelCase    :						List[Any]=2							,							_lowerCAmelCase    :						Tuple=1							,							_lowerCAmelCase    :						str=16							,							_lowerCAmelCase    :						Dict=[128, 256, 384]							,							_lowerCAmelCase    :						List[str]=[4, 6, 8]							,							_lowerCAmelCase    :						List[Any]=[2, 3, 4]							,							_lowerCAmelCase    :						Optional[int]=[16, 16, 16]							,							_lowerCAmelCase    :						List[Any]=0							,							_lowerCAmelCase    :						Optional[int]=[2, 2, 2]							,							_lowerCAmelCase    :						Optional[Any]=[2, 2, 2]							,							_lowerCAmelCase    :						Optional[int]=0.02							,							_lowerCAmelCase    :						List[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						int=2							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     kernel_size
       SCREAMING_SNAKE_CASE_      		=     stride
       SCREAMING_SNAKE_CASE_      		=     padding
       SCREAMING_SNAKE_CASE_      		=     hidden_sizes
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     depths
       SCREAMING_SNAKE_CASE_      		=     key_dim
       SCREAMING_SNAKE_CASE_      		=     drop_path_rate
       SCREAMING_SNAKE_CASE_      		=     patch_size
       SCREAMING_SNAKE_CASE_      		=     attention_ratio
       SCREAMING_SNAKE_CASE_      		=     mlp_ratio
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     [
           ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     use_labels
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     initializer_range



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       SCREAMING_SNAKE_CASE_      		=     None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size]							,							self.num_labels		)

       SCREAMING_SNAKE_CASE_      		=     self.get_config()
       return config, pixel_values, labels



 def  lowerCAmelCase_    (				self    :						int		):
       return LevitConfig(
           image_size=self.image_size							,							num_channels=self.num_channels							,							kernel_size=self.kernel_size							,							stride=self.stride							,							padding=self.padding							,							patch_size=self.patch_size							,							hidden_sizes=self.hidden_sizes							,							num_attention_heads=self.num_attention_heads							,							depths=self.depths							,							key_dim=self.key_dim							,							drop_path_rate=self.drop_path_rate							,							mlp_ratio=self.mlp_ratio							,							attention_ratio=self.attention_ratio							,							initializer_range=self.initializer_range							,							down_ops=self.down_ops							,							)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     LevitModel(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     (self.image_size, self.image_size)
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     image_size[0], image_size[1]
       for _ in range(4		):
             SCREAMING_SNAKE_CASE_      		=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
             SCREAMING_SNAKE_CASE_      		=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
       self.parent.assertEqual(
           result.last_hidden_state.shape							,							(self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])							,							)



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     LevitForImageClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)		)




 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     config_and_inputs
       SCREAMING_SNAKE_CASE_      		=     {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (
     (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
     if is_torch_available()
     else ()
 )
 lowercase_							       =       (
     {
         "feature-extraction": LevitModel,
         "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     LevitModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase							,							has_text_modality=_lowerCAmelCase							,							hidden_size=37		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()



 def  lowerCAmelCase_    (				self    :						Dict		):
       return



 @unittest.skip(reason='Levit does not use inputs_embeds'		)
 def  lowerCAmelCase_    (				self    :						str		):
       pass



 @unittest.skip(reason='Levit does not support input and output embeddings'		)
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       pass



 @unittest.skip(reason='Levit does not output attentions'		)
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       pass



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_      		=     ['pixel_values']
             self.assertListEqual(arg_names[:1]							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       def check_hidden_states_output(_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						str		):
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE_      		=     model(**self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase		)		)

             SCREAMING_SNAKE_CASE_      		=     outputs.hidden_states

             SCREAMING_SNAKE_CASE_      		=     len(self.model_tester.depths		) + 1
             self.assertEqual(len(_lowerCAmelCase		)							,							_lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_      		=     (self.model_tester.image_size, self.model_tester.image_size)
             SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     image_size[0], image_size[1]
             for _ in range(4		):
                   SCREAMING_SNAKE_CASE_      		=     floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1		)
                   SCREAMING_SNAKE_CASE_      		=     floor(
                       (
                           (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1		)
             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]		)							,							[
                     height * width,
                     self.model_tester.hidden_sizes[0],
                 ]							,							)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_      		=     True
             check_hidden_states_output(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             SCREAMING_SNAKE_CASE_      		=     True

             check_hidden_states_output(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       pass



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]=False		):
       SCREAMING_SNAKE_CASE_      		=     super()._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase							,							return_labels=_lowerCAmelCase		)

       if return_labels:
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

       return inputs_dict



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     True

       for model_class in self.all_model_classes:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(_lowerCAmelCase		)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   continue
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.train()
             SCREAMING_SNAKE_CASE_      		=     self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase							,							return_labels=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		).loss
             loss.backward()



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_      		=     False
       SCREAMING_SNAKE_CASE_      		=     True

       for model_class in self.all_model_classes:
             if model_class in get_values(_lowerCAmelCase		) or not model_class.supports_gradient_checkpointing:
                   continue
             # LevitForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   continue
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             model.gradient_checkpointing_enable()
             model.to(_lowerCAmelCase		)
             model.train()
             SCREAMING_SNAKE_CASE_      		=     self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase							,							return_labels=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		).loss
             loss.backward()



 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_      		=     [
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(_lowerCAmelCase		),
                 ]
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   continue

             for problem_type in problem_types:
                   with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"		):
                         SCREAMING_SNAKE_CASE_      		=     problem_type['title']
                         SCREAMING_SNAKE_CASE_      		=     problem_type['num_labels']

                         SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
                         model.to(_lowerCAmelCase		)
                         model.train()

                         SCREAMING_SNAKE_CASE_      		=     self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase							,							return_labels=_lowerCAmelCase		)

                         if problem_type["num_labels"] > 1:
                               SCREAMING_SNAKE_CASE_      		=     inputs['labels'].unsqueeze(1		).repeat(1							,							problem_type['num_labels']		)

                         SCREAMING_SNAKE_CASE_      		=     inputs['labels'].to(problem_type['dtype']		)

                         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                         # they have the same size." which is a symptom something in wrong for the regression problem.
                         # See https://github.com/huggingface/transformers/issues/11780
                         with warnings.catch_warnings(record=_lowerCAmelCase		) as warning_list:
                               SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		).loss
                         for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message		):
                                     raise ValueError(
                                         F"Something is going wrong in the regression problem: intercepted {w.message}"		)

                         loss.backward()




 @slow
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_      		=     LevitModel.from_pretrained(_lowerCAmelCase		)
             self.assertIsNotNone(_lowerCAmelCase		)


def    UpperCAmelCase_				( )   ->					List[Any]:
      SCREAMING_SNAKE_CASE_      		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





@require_torch
@require_vision
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 @cached_property
 def  lowerCAmelCase_    (				self    :						Dict		):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)




 @slow
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
           _lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       # verify the logits
       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 1_000)		)
       self.assertEqual(outputs.logits.shape							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor([1.0448, -0.3745, -1.8317]		).to(_lowerCAmelCase		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,							_lowerCAmelCase							,							atol=1E-4		)		)





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       print(self.vertex		)
       for i in self.vertex:
             print(_lowerCAmelCase							,							' -> '							,							' -> '.join([str(_lowerCAmelCase		) for j in self.vertex[i]]		)		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase		)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_      		=     [to_vertex]



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_      		=     [False] * len(self.vertex		)

       # call the recursive helper function
       for i in range(len(self.vertex		)		):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						list		):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_      		=     True

       print(_lowerCAmelCase							,							end=' '		)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)


if __name__ == "__main__":
   lowerCamelCase__					:  List[Any]      							=				Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3