






"""simple docstring"""
class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			:





	def __init__( self   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					int     	=   [
				    [],
				    [],
				    [],
				]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int		)		->     None:
				try:
							if len(self.queues[priority]		) >= 100:
										raise OverflowError("Maximum queue size is 100"		)
							self.queues[priority].append(lowerCAmelCase_		)
				except IndexError:
							raise ValueError("Valid priorities are 0, 1, and 2"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				for queue in self.queues:
							if queue:
										return queue.pop(0		)
				raise UnderFlowError("All queues are empty"		)





	def __str__( self   :					int		)		->     str:
				return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues		)		)







class      UpperCamelCase_			:





	def __init__( self   :					int		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Tuple     	=   []





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int		)		->     None:
				if len(self.queue		) == 100:
							raise OverFlowError("Maximum queue size is 100"		)
				self.queue.append(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				if not self.queue:
							raise UnderFlowError("The queue is empty"		)
				else:
							UpperCAmelCase_	:					Union[str, Any]     	=   min(self.queue		)
							self.queue.remove(lowerCAmelCase_		)
							return data





	def __str__( self   :					Dict		)		->     str:
				return str(self.queue		)





def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   FixedPriorityQueue()
			fpq.enqueue(0      ,10		)
			fpq.enqueue(1      ,70		)
			fpq.enqueue(0      ,1_00		)
			fpq.enqueue(2      ,1		)
			fpq.enqueue(2      ,5		)
			fpq.enqueue(1      ,7		)
			fpq.enqueue(2      ,4		)
			fpq.enqueue(1      ,64		)
			fpq.enqueue(0      ,1_28		)
			print(A__		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(A__		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)

def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   ElementPriorityQueue()
			epq.enqueue(10		)
			epq.enqueue(70		)
			epq.enqueue(1_00		)
			epq.enqueue(1		)
			epq.enqueue(5		)
			epq.enqueue(7		)
			epq.enqueue(4		)
			epq.enqueue(64		)
			epq.enqueue(1_28		)
			print(A__		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(A__		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)


if __name__ == "__main__":
			fixed_priority_queue()
			element_priority_queue()







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 AutoFeatureExtractor, WavaVecaFeatureExtractor
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_feature_extraction import CustomFeatureExtractor  # noqa E402


__snake_case   							=						get_tests_dir('''fixtures''')



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			mock.Mock()
            __UpperCamelCase =			500
            __UpperCamelCase =			{}
            __UpperCamelCase =			HTTPError
            __UpperCamelCase =			{}

            # Download this model to make sure it's in the cache.
            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
            # 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:
                 __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'					)



@is_staging_test
class 	__lowerCamelCase			(unittest.TestCase						):







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			TOKEN
            HfFolder.save_token(A_					)







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




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

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

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







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

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

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

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('valid_org/test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

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

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

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            CustomFeatureExtractor.register_for_auto_class()
            __UpperCamelCase =			CustomFeatureExtractor.from_pretrained(A_					)

            feature_extractor.push_to_hub('test-dynamic-feature-extractor',use_auth_token=self._token					)

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map,{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'},)

            __UpperCamelCase =			AutoFeatureExtractor.from_pretrained(
                F'''{USER}/test-dynamic-feature-extractor''',trust_remote_code=A_					)
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__,'CustomFeatureExtractor'					)


