





import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
       import jax

       from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
       from transformers.models.bert.modeling_flax_bert import FlaxBertModel
       from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxBertModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxRobertaModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''bert-base is not a local folder and is not a valid model identifier''' ):
               A__											=						FlaxAutoModel.from_pretrained('''bert-base''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
               A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase,revision='''aaaaaa''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''',):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )




 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(__lowerCamelCase,'''Use `from_pt=True` to load this model''' ):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )












def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[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 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[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 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


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

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





