


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase:						List[str]   				=       get_tests_dir('fixtures')
_lowerCAmelCase:						List[Any]   				=       get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_lowerCAmelCase:						Optional[Any]   				=       get_tests_dir('fixtures/dummy-config.json')




class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =0





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h')
											self.assertIsInstance(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =AutoFeatureExtractor.from_pretrained(lowercase_)
											self.assertIsInstance(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											with tempfile.TemporaryDirectory() as tmpdirname:
																		a__      =WavaVecaConfig()

																		# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																		a__      =AutoFeatureExtractor.from_pretrained(lowercase_).to_dict()

																		config_dict.pop('feature_extractor_type')
																		a__      =WavaVecaFeatureExtractor(**lowercase_)

																		# save in new folder
																		model_config.save_pretrained(lowercase_)
																		config.save_pretrained(lowercase_)

																		a__      =AutoFeatureExtractor.from_pretrained(lowercase_)

																		# make sure private variable is not incorrectly saved
																		a__      =json.loads(config.to_json_string())
																		self.assertTrue('_processor_class' not in dict_as_saved)

											self.assertIsInstance(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =AutoFeatureExtractor.from_pretrained(lowercase_)
											self.assertIsInstance(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											with self.assertRaisesRegex(
											    lowercase_       ,  'bert-base is not a local folder and is not a valid model identifier'):
																		a__      =AutoFeatureExtractor.from_pretrained('bert-base')





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											with self.assertRaisesRegex(
											    lowercase_       ,  R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
																		a__      =AutoFeatureExtractor.from_pretrained(lowercase_       ,  revision='aaaaaa')





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											with self.assertRaisesRegex(
											    lowercase_       ,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'       ,  ):
																		a__      =AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model')





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# If remote code is not set, we will time out when asking whether to load the model.
											with self.assertRaises(lowercase_):
																		a__      =AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor')
											# If remote code is disabled, we can't load this config.
											with self.assertRaises(lowercase_):
																		a__      =AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       ,  trust_remote_code=lowercase_)

											a__      =AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor'       ,  trust_remote_code=lowercase_)
											self.assertEqual(feature_extractor.__class__.__name__       ,  'NewFeatureExtractor')

											# Test feature extractor can be reloaded.
											with tempfile.TemporaryDirectory() as tmp_dir:
																		feature_extractor.save_pretrained(lowercase_)
																		a__      =AutoFeatureExtractor.from_pretrained(lowercase_       ,  trust_remote_code=lowercase_)
											self.assertEqual(reloaded_feature_extractor.__class__.__name__       ,  'NewFeatureExtractor')





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											try:
																		AutoConfig.register('custom'       ,  lowercase_)
																		AutoFeatureExtractor.register(lowercase_       ,  lowercase_)
																		# Trying to register something existing in the Transformers library will raise an error
																		with self.assertRaises(lowercase_):
																									AutoFeatureExtractor.register(lowercase_       ,  lowercase_)

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		a__      =CustomFeatureExtractor.from_pretrained(lowercase_)
																		with tempfile.TemporaryDirectory() as tmp_dir:
																									feature_extractor.save_pretrained(lowercase_)
																									a__      =AutoFeatureExtractor.from_pretrained(lowercase_)
																									self.assertIsInstance(lowercase_       ,  lowercase_)

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





				def 						__UpperCamelCase    (				self)  -> List[Any]:




											class       lowercase_					(lowercase__   ):
															snake_case	    =True

											try:
																		AutoConfig.register('custom'       ,  lowercase_)
																		AutoFeatureExtractor.register(lowercase_       ,  lowercase_)
																		# If remote code is not set, the default is to use local
																		a__      =AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor')
																		self.assertEqual(feature_extractor.__class__.__name__       ,  'NewFeatureExtractor')
																		self.assertTrue(feature_extractor.is_local)

																		# If remote code is disabled, we load the local one.
																		a__      =AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       ,  trust_remote_code=lowercase_)
																		self.assertEqual(feature_extractor.__class__.__name__       ,  'NewFeatureExtractor')
																		self.assertTrue(feature_extractor.is_local)

																		# If remote is enabled, we load from the Hub
																		a__      =AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       ,  trust_remote_code=lowercase_)
																		self.assertEqual(feature_extractor.__class__.__name__       ,  'NewFeatureExtractor')
																		self.assertTrue(not hasattr(lowercase_       ,  'is_local'))

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





from __future__ import annotations

from typing import Any




class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> None:
											a__      =num_of_nodes
											a__      =[]
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:
											self.m_edges.append([u_node, v_node, weight])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									a__      =self.find_component(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

											elif component_size[u_node] >= component_size[v_node]:
																		a__      =self.find_component(lowercase_)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(lowercase_)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											a__      =self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									a__    ,     a__    ,     a__      =edge

																									a__      =self.m_component[u]
																									a__      =self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

																																a__      =self.m_component[u]
																																a__      =self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(lowercase_       ,  lowercase_       ,  lowercase_)
																																							print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		a__      =[-1] * self.m_num_of_nodes
											print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

