





from __future__ import annotations





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    list[int]:







			'''simple docstring'''

			if not list_of_ints:
						return []

			if min(UpperCamelCase__			) < 0:
						raise ValueError('''All numbers must be positive'''			)

			UpperCAmelCase       		=    max(len(bin(UpperCamelCase__			)[2:]			) for x in list_of_ints			)
			return _msd_radix_sort(UpperCamelCase__      ,      UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    list[int]:







			'''simple docstring'''

			if bit_position == 0 or len(UpperCamelCase__			) in [0, 1]:
						return list_of_ints

			UpperCAmelCase       		=    []
			UpperCAmelCase       		=    []
			# Split numbers based on bit at bit_position from the right
			for number in list_of_ints:
						if (number >> (bit_position - 1)) & 1:
									# number has a one at bit bit_position
									ones.append(UpperCamelCase__			)
						else:
									# number has a zero at bit bit_position
									zeros.append(UpperCamelCase__			)

    # recursively split both lists further
			UpperCAmelCase       		=    _msd_radix_sort(UpperCamelCase__      ,      bit_position - 1			)
			UpperCAmelCase       		=    _msd_radix_sort(UpperCamelCase__      ,      bit_position - 1			)

			# recombine lists
			UpperCAmelCase       		=    zeros
			res.extend(UpperCamelCase__			)

			return res





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    len(UpperCamelCase__			)
			if not list_of_ints or length == 1:
						return

			if min(UpperCamelCase__			) < 0:
						raise ValueError('''All numbers must be positive'''			)

			UpperCAmelCase       		=    max(len(bin(UpperCamelCase__			)[2:]			) for x in list_of_ints			)
			_msd_radix_sort_inplace(UpperCamelCase__      ,      UpperCamelCase__      ,      0      ,      UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			if bit_position == 0 or end_index - begin_index <= 1:
						return

			bit_position -= 1

			UpperCAmelCase       		=    begin_index
			UpperCAmelCase       		=    end_index - 1
			while i <= j:
						UpperCAmelCase       		=    False
						if not (list_of_ints[i] >> bit_position) & 1:
									# found zero at the beginning
									i += 1
									UpperCAmelCase       		=    True
						if (list_of_ints[j] >> bit_position) & 1:
									# found one at the end
									j -= 1
									UpperCAmelCase       		=    True

						if changed:
									continue

						UpperCAmelCase		,      UpperCAmelCase       		=    list_of_ints[j], list_of_ints[i]
						j -= 1
						if j != i:
									i += 1

			_msd_radix_sort_inplace(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
			_msd_radix_sort_inplace(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()










import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''model_details''': {
				        '''Organization''': '''testing''',
				        '''Model date''': '''today''',
				        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
				        '''Architecture''': '''Convolutional Neural Network.''',
				    },
				    '''metrics''': '''BLEU and ROUGE-1''',
				    '''evaluation_data''': {
				        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
				        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
				    },
				    '''training_data''': {
				        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
				        '''Preprocessing''': (
				            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
				            ''' https://arxiv.org/pdf/1810.03993.pdf'''
				        ),
				    },
				    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 7_6},
				}
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				self.assertTrue(hasattr(_A							, '''model_details'''		)		)
				self.assertTrue(hasattr(_A							, '''intended_use'''		)		)
				self.assertTrue(hasattr(_A							, '''factors'''		)		)
				self.assertTrue(hasattr(_A							, '''metrics'''		)		)
				self.assertTrue(hasattr(_A							, '''evaluation_data'''		)		)
				self.assertTrue(hasattr(_A							, '''training_data'''		)		)
				self.assertTrue(hasattr(_A							, '''quantitative_analyses'''		)		)
				self.assertTrue(hasattr(_A							, '''ethical_considerations'''		)		)
				self.assertTrue(hasattr(_A							, '''caveats_and_recommendations'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				UpperCAmelCase       		=    json.loads(modelcard.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.assertEqual(obj[key]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase       		=    os.path.join(_A							, '''modelcard.json'''		)
							model_card_first.to_json_file(_A		)
							UpperCAmelCase       		=    ModelCard.from_json_file(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model_card_first.save_pretrained(_A		)
							UpperCAmelCase       		=    ModelCard.from_pretrained(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)



