




"""simple docstring"""

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_lowerCamelCase     :	Tuple         =				get_tests_dir('fixtures')

class 				lowercase					(			unittest.TestCase):







				def     a_     (							self      :    int				):



					"""simple docstring"""

					A_     : str		      =						mock.Mock()
					A_     : Tuple		      =						5_00
					A_     : str		      =						{}
					A_     : Union[str, Any]		      =						HTTPError
					A_     : Union[str, Any]		      =						{}

					# Download this model to make sure it's in the cache.
					A_     : List[Any]		      =						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
					# Under the mock environment we get a 500 error when trying to reach the model.
					with mock.patch('''requests.Session.request'''					,   return_value=A_				) as mock_head:
						A_     : int		      =						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
						# This check we did call the fake head request
						mock_head.assert_called()







				def     a_     (							self      :    int				):



					"""simple docstring"""

					A_     : Optional[int]		      =						ViTImageProcessor.from_pretrained(
					    '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''				)







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					with self.assertRaises(A_				):
						# config is in subfolder, the following should not work without specifying the subfolder
						A_     : List[str]		      =						AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''				)

					A_     : Any		      =						AutoImageProcessor.from_pretrained(
					    '''hf-internal-testing/stable-diffusion-all-variants'''					,   subfolder='''feature_extractor'''				)

					self.assertIsNotNone(A_				)

@is_staging_test
class 				lowercase					(			unittest.TestCase):







				@classmethod
				def     a_     (							cls      :    Any				):



					"""simple docstring"""

					A_     : Tuple		      =						TOKEN
					HfFolder.save_token(A_				)







				@classmethod
				def     a_     (							cls      :    List[Any]				):



					"""simple docstring"""

					try:
						delete_repo(token=cls._token					,   repo_id='''test-image-processor'''				)
					except HTTPError:
						pass

					try:
						delete_repo(token=cls._token					,   repo_id='''valid_org/test-image-processor-org'''				)
					except HTTPError:
						pass

					try:
						delete_repo(token=cls._token					,   repo_id='''test-dynamic-image-processor'''				)
					except HTTPError:
						pass







				def     a_     (							self      :    int				):



					"""simple docstring"""

					A_     : List[Any]		      =						ViTImageProcessor.from_pretrained(A_				)
					image_processor.push_to_hub('''test-image-processor'''					,   use_auth_token=self._token				)

					A_     : Union[str, Any]		      =						ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""				)
					for k, v in image_processor.__dict__.items():
						self.assertEqual(A_					,   getattr(A_					,   A_				)				)

					# Reset repo
					delete_repo(token=self._token					,   repo_id='''test-image-processor'''				)

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(
						    A_					,   repo_id='''test-image-processor'''					,   push_to_hub=A_					,   use_auth_token=self._token				)

					A_     : List[str]		      =						ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""				)
					for k, v in image_processor.__dict__.items():
						self.assertEqual(A_					,   getattr(A_					,   A_				)				)







				def     a_     (							self      :    Union[str, Any]				):



					"""simple docstring"""

					A_     : Optional[int]		      =						ViTImageProcessor.from_pretrained(A_				)
					image_processor.push_to_hub('''valid_org/test-image-processor'''					,   use_auth_token=self._token				)

					A_     : List[str]		      =						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''				)
					for k, v in image_processor.__dict__.items():
						self.assertEqual(A_					,   getattr(A_					,   A_				)				)

					# Reset repo
					delete_repo(token=self._token					,   repo_id='''valid_org/test-image-processor'''				)

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(
						    A_					,   repo_id='''valid_org/test-image-processor-org'''					,   push_to_hub=A_					,   use_auth_token=self._token				)

					A_     : str		      =						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''				)
					for k, v in image_processor.__dict__.items():
						self.assertEqual(A_					,   getattr(A_					,   A_				)				)







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					CustomImageProcessor.register_for_auto_class()
					A_     : Any		      =						CustomImageProcessor.from_pretrained(A_				)

					image_processor.push_to_hub('''test-dynamic-image-processor'''					,   use_auth_token=self._token				)

					# This has added the proper auto_map field to the config
					self.assertDictEqual(
					    image_processor.auto_map					,   {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}					,   )

					A_     : Any		      =						AutoImageProcessor.from_pretrained(
					    F"""{USER}/test-dynamic-image-processor"""					,   trust_remote_code=A_				)
					# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
					self.assertEqual(new_image_processor.__class__.__name__					,   '''CustomImageProcessor'''				)







from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCAmelCase					: Tuple      =       tuple[int, int]


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     vertices
				UpperCamelCase		=     {
				    (min(A_       ), max(A_       )): weight for edge, weight in edges.items()
				}



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				self.vertices.add(edge[0]       )
				self.vertices.add(edge[1]       )
				UpperCamelCase		=     weight



		def __UpperCamelCase ( self       )  ->					Graph:
				"""simple docstring"""
				UpperCamelCase		=     Graph({min(self.vertices       )}				,      {}       )
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42

				while len(subgraph.vertices       ) < len(self.vertices       ):
						UpperCamelCase		=     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		=     edge
												UpperCamelCase		=     weight

						subgraph.add_edge(A_				,      A_       )

				return subgraph






def       A       (					lowercase = "p107_network.txt" )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     os.path.abspath(os.path.dirname(lowercase ) )
		UpperCamelCase		=     os.path.join(lowercase    ,       lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     42
		UpperCamelCase		=     42
		UpperCamelCase		=     42

		with open(lowercase ) as f:
				UpperCamelCase		=     f.read().strip().split('\n' )

		UpperCamelCase		=     [line.split(',' ) for line in data]

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

		UpperCamelCase		=     Graph(set(range(len(lowercase ) ) )    ,       lowercase )

		UpperCamelCase		=     graph.prims_algorithm()

		UpperCamelCase		=     sum(graph.edges.values() )
		UpperCamelCase		=     sum(subgraph.edges.values() )

		return initial_total - optimal_total


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