



"""simple docstring"""
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 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  str											=    self.config_class(**self.inputs_dict		)
         self.parent.assertTrue(hasattr(A       ,	'''hidden_sizes'''		)		)
         self.parent.assertTrue(hasattr(A       ,	'''num_attention_heads'''		)		)



class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A=1_3       ,	A=6_4       ,	A=3       ,	A=3       ,	A=2       ,	A=1       ,	A=1_6       ,	A=[1_2_8, 2_5_6, 3_8_4]       ,	A=[4, 6, 8]       ,	A=[2, 3, 4]       ,	A=[1_6, 1_6, 1_6]       ,	A=0       ,	A=[2, 2, 2]       ,	A=[2, 2, 2]       ,	A=0.02       ,	A=True       ,	A=True       ,	A=2       ,	)							->					Union[str, Any]:
         _UpperCAmelCase							:  str											=    parent
         _UpperCAmelCase							:  Optional[int]											=    batch_size
         _UpperCAmelCase							:  int											=    image_size
         _UpperCAmelCase							:  List[str]											=    num_channels
         _UpperCAmelCase							:  Optional[int]											=    kernel_size
         _UpperCAmelCase							:  str											=    stride
         _UpperCAmelCase							:  Union[str, Any]											=    padding
         _UpperCAmelCase							:  Any											=    hidden_sizes
         _UpperCAmelCase							:  Tuple											=    num_attention_heads
         _UpperCAmelCase							:  Any											=    depths
         _UpperCAmelCase							:  Optional[Any]											=    key_dim
         _UpperCAmelCase							:  Any											=    drop_path_rate
         _UpperCAmelCase							:  Optional[int]											=    patch_size
         _UpperCAmelCase							:  Optional[int]											=    attention_ratio
         _UpperCAmelCase							:  Any											=    mlp_ratio
         _UpperCAmelCase							:  Optional[Any]											=    initializer_range
         _UpperCAmelCase							:  Dict											=    [
             ['''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],
         ]
         _UpperCAmelCase							:  List[str]											=    is_training
         _UpperCAmelCase							:  Dict											=    use_labels
         _UpperCAmelCase							:  Tuple											=    num_labels
         _UpperCAmelCase							:  Optional[Any]											=    initializer_range





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  Optional[Any]											=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

         _UpperCAmelCase							:  Optional[int]											=    self.get_config()
         return config, pixel_values, labels





     def  __lowerCAmelCase	(					self		)							->					Dict:
         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       ,	A       ,	A       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  List[Any]											=    LevitModel(config=A		)
         model.to(A		)
         model.eval()
         _UpperCAmelCase							:  Union[str, Any]											=    model(A		)
         _UpperCAmelCase							:  Any											=    (self.image_size, self.image_size)
         _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    image_size[0], image_size[1]
         for _ in range(4		):
             _UpperCAmelCase							:  int											=    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
             _UpperCAmelCase							:  int											=    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       ,	A       ,	A       ,	A		)							->					Dict:
         _UpperCAmelCase							:  List[Any]											=    self.num_labels
         _UpperCAmelCase							:  List[str]											=    LevitForImageClassification(A		)
         model.to(A		)
         model.eval()
         _UpperCAmelCase							:  Optional[int]											=    model(A       ,	labels=A		)
         self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Optional[int]											=    self.prepare_config_and_inputs()
         _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    config_and_inputs
         _UpperCAmelCase							:  List[Any]											=    {'''pixel_values''': pixel_values}
         return config, inputs_dict



@require_torch
class 							_UpperCAmelCase							( a		,a		,unittest.TestCase      ):





     '''simple docstring'''



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

     a__									=False
     a__									=False
     a__									=False
     a__									=False
     a__									=False





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[int]											=    LevitModelTester(self		)
         _UpperCAmelCase							:  List[str]											=    ConfigTester(self       ,	config_class=A       ,	has_text_modality=A       ,	hidden_size=3_7		)





     def  __lowerCAmelCase	(					self		)							->					int:
         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		)							->					List[Any]:
         return





     @unittest.skip(reason='''Levit does not use inputs_embeds'''		)
     def  __lowerCAmelCase	(					self		)							->					Any:
         pass





     @unittest.skip(reason='''Levit does not support input and output embeddings'''		)
     def  __lowerCAmelCase	(					self		)							->					int:
         pass





     @unittest.skip(reason='''Levit does not output attentions'''		)
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    self.model_tester.prepare_config_and_inputs_for_common()

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

             _UpperCAmelCase							:  List[Any]											=    ['''pixel_values''']
             self.assertListEqual(arg_names[:1]       ,	A		)





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

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

             _UpperCAmelCase							:  Optional[int]											=    outputs.hidden_states

             _UpperCAmelCase							:  Any											=    len(self.model_tester.depths		) + 1
             self.assertEqual(len(A		)       ,	A		)

             _UpperCAmelCase							:  int											=    (self.model_tester.image_size, self.model_tester.image_size)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    image_size[0], image_size[1]
             for _ in range(4		):
                 _UpperCAmelCase							:  Optional[Any]											=    floor(
                     (
                         (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                         / self.model_tester.stride
                     )
                     + 1		)
                 _UpperCAmelCase							:  int											=    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],
                 ]       ,	)

         _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[int]											=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _UpperCAmelCase							:  Union[str, Any]											=    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		)





     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         pass





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A=False		)							->					Any:
         _UpperCAmelCase							:  Optional[int]											=    super()._prepare_for_class(A       ,	A       ,	return_labels=A		)

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

         return inputs_dict





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  int											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A		)





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  str											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A		)





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         if not self.model_tester.is_training:
             return

         _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							:  Union[str, Any]											=    True

         for model_class in self.all_model_classes:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(A		)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                 continue
             _UpperCAmelCase							:  str											=    model_class(A		)
             model.to(A		)
             model.train()
             _UpperCAmelCase							:  List[Any]											=    self._prepare_for_class(A       ,	A       ,	return_labels=A		)
             _UpperCAmelCase							:  Any											=    model(**A		).loss
             loss.backward()





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         _UpperCAmelCase							:  Dict											=    False
         _UpperCAmelCase							:  Optional[Any]											=    True

         for model_class in self.all_model_classes:
             if model_class in get_values(A		) or not model_class.supports_gradient_checkpointing:
                 continue
             # LevitForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                 continue
             _UpperCAmelCase							:  List[Any]											=    model_class(A		)
             model.gradient_checkpointing_enable()
             model.to(A		)
             model.train()
             _UpperCAmelCase							:  List[str]											=    self._prepare_for_class(A       ,	A       ,	return_labels=A		)
             _UpperCAmelCase							:  int											=    model(**A		).loss
             loss.backward()





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    self.model_tester.prepare_config_and_inputs_for_common()

         _UpperCAmelCase							:  Any											=    [
             {'''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(A		),
                 ]
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                 continue

             for problem_type in problem_types:
                 with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'		):
                     _UpperCAmelCase							:  Tuple											=    problem_type['''title''']
                     _UpperCAmelCase							:  Union[str, Any]											=    problem_type['''num_labels''']

                     _UpperCAmelCase							:  Union[str, Any]											=    model_class(A		)
                     model.to(A		)
                     model.train()

                     _UpperCAmelCase							:  List[Any]											=    self._prepare_for_class(A       ,	A       ,	return_labels=A		)

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

                     _UpperCAmelCase							:  Optional[int]											=    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=A		) as warning_list:
                         _UpperCAmelCase							:  Optional[int]											=    model(**A		).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		)							->					Any:
         for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _UpperCAmelCase							:  List[Any]											=    LevitModel.from_pretrained(A		)
             self.assertIsNotNone(A		)





def  lowerCamelCase_						():
    _UpperCAmelCase							:  int											=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image


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





     '''simple docstring'''



     @cached_property
     def  __lowerCAmelCase	(					self		)							->					Any:
         return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)





     @slow
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  List[Any]											=    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
             A		)

         _UpperCAmelCase							:  str											=    self.default_image_processor
         _UpperCAmelCase							:  Dict											=    prepare_img()
         _UpperCAmelCase							:  int											=    image_processor(images=A       ,	return_tensors='''pt'''		).to(A		)

         # forward pass
         with torch.no_grad():
             _UpperCAmelCase							:  List[str]											=    model(**A		)

         # verify the logits
         _UpperCAmelCase							:  Optional[int]											=    torch.Size((1, 1_0_0_0)		)
         self.assertEqual(outputs.logits.shape       ,	A		)

         _UpperCAmelCase							:  List[str]											=    torch.tensor([1.0_448, -0.3_745, -1.8_317]		).to(A		)

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







"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    max(self.edges.values()		) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


if __name__ == "__main__":
  print(f"{solution() = }")


