
def    a      ( a	)     ->str:




			'''simple docstring'''


			if collection == []:
						return []

			# get some information about the collection
			SCREAMING_SNAKE_CASE     						= len(a	)
			SCREAMING_SNAKE_CASE     						= max(a	)
			SCREAMING_SNAKE_CASE     						= min(a	)

			# create the counting array
			SCREAMING_SNAKE_CASE     						= coll_max + 1 - coll_min
			SCREAMING_SNAKE_CASE     						= [0] * counting_arr_length

			# count how much a number appears in the collection
			for number in collection:
						counting_arr[number - coll_min] += 1

			# sum each position with it's predecessors. now, counting_arr[i] tells
			# us how many elements <= i has in the collection
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= counting_arr[i] + counting_arr[i - 1]

			# create the output collection
			SCREAMING_SNAKE_CASE     						= [0] * coll_len

			# place the elements in the output, respecting the original order (stable
			# sort) from end to begin, updating counting_arr
			for i in reversed(range(0		,						a	)	):
						SCREAMING_SNAKE_CASE     						= collection[i]
						counting_arr[collection[i] - coll_min] -= 1

			return ordered


def    a      ( a	)     ->Optional[int]:




			'''simple docstring'''


			return "".join([chr(a	) for i in counting_sort([ord(a	) for c in string]	)]	)


if __name__ == "__main__":
							# Test string sort
							assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

							__lowerCAmelCase					   =       input('Enter numbers separated by a comma:\n').strip()
							__lowerCAmelCase					   =       [int(item) for item in user_input.split(',')]
							print(counting_sort(unsorted))

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)