





"""simple docstring"""






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
  import jax

  from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 				__lowercase			(			unittest.TestCase							):







     '''simple docstring'''




     def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=13	,			_UpperCAmelCase=30	,			_UpperCAmelCase=2	,			_UpperCAmelCase=3	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=32	,			_UpperCAmelCase=5	,			_UpperCAmelCase=4	,			_UpperCAmelCase=37	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=10	,			_UpperCAmelCase=0.0_2	,			):
         __a :						List[Any]			       =  parent
         __a :						List[Any]			       =  batch_size
         __a :						Union[str, Any]			       =  image_size
         __a :						List[str]			       =  patch_size
         __a :						List[str]			       =  num_channels
         __a :						int			       =  is_training
         __a :						Optional[Any]			       =  use_labels
         __a :						Tuple			       =  hidden_size
         __a :						int			       =  num_hidden_layers
         __a :						Optional[int]			       =  num_attention_heads
         __a :						Optional[int]			       =  intermediate_size
         __a :						List[str]			       =  hidden_act
         __a :						int			       =  hidden_dropout_prob
         __a :						List[str]			       =  attention_probs_dropout_prob
         __a :						Union[str, Any]			       =  type_sequence_label_size
         __a :						Union[str, Any]			       =  initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __a :						Dict			       =  (image_size // patch_size) ** 2
         __a :						Dict			       =  num_patches + 1







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

         __a :						List[Any]			       =  ViTConfig(
             image_size=self.image_size	,			patch_size=self.patch_size	,			num_channels=self.num_channels	,			hidden_size=self.hidden_size	,			num_hidden_layers=self.num_hidden_layers	,			num_attention_heads=self.num_attention_heads	,			intermediate_size=self.intermediate_size	,			hidden_act=self.hidden_act	,			hidden_dropout_prob=self.hidden_dropout_prob	,			attention_probs_dropout_prob=self.attention_probs_dropout_prob	,			is_decoder=_UpperCAmelCase	,			initializer_range=self.initializer_range	,			)

         return config, pixel_values







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
         __a :						Optional[Any]			       =  FlaxViTModel(config=_UpperCAmelCase     )
         __a :						Any			       =  model(_UpperCAmelCase     )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __a :						List[str]			       =  (self.image_size, self.image_size)
         __a :						Any			       =  (self.patch_size, self.patch_size)
         __a :						str			       =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, num_patches + 1, self.hidden_size)     )







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
         __a :						Any			       =  self.type_sequence_label_size
         __a :						Dict			       =  FlaxViTForImageClassification(config=_UpperCAmelCase     )
         __a :						Any			       =  model(_UpperCAmelCase     )
         self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size)     )

         # test greyscale images
         __a :						Union[str, Any]			       =  1
         __a :						Any			       =  FlaxViTForImageClassification(_UpperCAmelCase     )

         __a :						List[str]			       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
         __a :						Optional[Any]			       =  model(_UpperCAmelCase     )







     def  _lowerCamelCase		(						self     ):
         __a :						List[str]			       =  self.prepare_config_and_inputs()
         (
             __a						
         ) :						Tuple			       =  config_and_inputs
         __a :						Any			       =  {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







     '''simple docstring'''




     __lowerCAmelCase  =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







     def  _lowerCamelCase		(						self     ):
         __a :						Tuple			       =  FlaxViTModelTester(self     )
         __a :						str			       =  ConfigTester(self	,			config_class=_UpperCAmelCase	,			has_text_modality=_UpperCAmelCase	,			hidden_size=37     )







     def  _lowerCamelCase		(						self     ):
         self.config_tester.run_common_tests()







     def  _lowerCamelCase		(						self     ):
         __a :						Optional[int]			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_UpperCAmelCase     )







     def  _lowerCamelCase		(						self     ):
         __a :						Optional[int]			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase     )







     def  _lowerCamelCase		(						self     ):
         __a :						List[Any]			       =  self.model_tester.prepare_config_and_inputs_for_common()

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

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







     def  _lowerCamelCase		(						self     ):
         __a :						Union[str, Any]			       =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                 __a :						Union[str, Any]			       =  self._prepare_for_class(_UpperCAmelCase	,			_UpperCAmelCase     )
                 __a :						Optional[int]			       =  model_class(_UpperCAmelCase     )

                 @jax.jit
                 def model_jitted(_UpperCAmelCase	,			**_UpperCAmelCase     ):
                     return model(pixel_values=_UpperCAmelCase	,			**_UpperCAmelCase     )

                 with self.subTest('''JIT Enabled'''     ):
                     __a :						Dict			       =  model_jitted(**_UpperCAmelCase     ).to_tuple()

                 with self.subTest('''JIT Disabled'''     ):
                     with jax.disable_jit():
                         __a :						Optional[int]			       =  model_jitted(**_UpperCAmelCase     ).to_tuple()

                 self.assertEqual(len(_UpperCAmelCase     )	,			len(_UpperCAmelCase     )     )
                 for jitted_output, output in zip(_UpperCAmelCase	,			_UpperCAmelCase     ):
                     self.assertEqual(jitted_output.shape	,			output.shape     )







     @slow
     def  _lowerCamelCase		(						self     ):
         for model_class_name in self.all_model_classes:
             __a :						str			       =  model_class_name.from_pretrained('''google/vit-base-patch16-224'''     )
             __a :						int			       =  model(np.ones((1, 3, 224, 224)     )     )
             self.assertIsNotNone(_UpperCAmelCase     )






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[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:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






def     __A      (			a_						:str = "p107_network.txt")		->		int:
				__a :						str			       =  os.path.abspath(os.path.dirname(a_))
				__a :						str			       =  os.path.join(a_   ,       a_)
				__a :						dict[EdgeT, int]			       =  {}
				__a :						list[str]
				__a :						int
				__a :						int

				with open(a_) as f:
								__a :						Any			       =  f.read().strip().split('''\n''')

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

				for edgea in range(1   ,       len(a_)):
								for edgea in range(a_):
												if adjaceny_matrix[edgea][edgea] != "-":
																__a :						int			       =  int(adjaceny_matrix[edgea][edgea])

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

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

				return initial_total - optimal_total


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