



"""simple docstring"""
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class 					a	:



  def __init__(						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Tuple=99       ,		lowerCAmelCase :					List[str]=13       ,		lowerCAmelCase :					str=7       ,		lowerCAmelCase :					str=9       ,		lowerCAmelCase :					Optional[int]=True       ,		lowerCAmelCase :					Union[str, Any]=True       ,		lowerCAmelCase :					Union[str, Any]=False       ,		lowerCAmelCase :					Optional[Any]=32       ,		lowerCAmelCase :					List[Any]=5       ,		lowerCAmelCase :					int=4       ,		lowerCAmelCase :					str=37       ,		lowerCAmelCase :					Union[str, Any]=8       ,		lowerCAmelCase :					List[Any]=0.1       ,		lowerCAmelCase :					Optional[Any]=0.0_0_2       ,		lowerCAmelCase :					Optional[int]=1       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					Dict=None       ,		lowerCAmelCase :					Dict=None       ,		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=parent
     SCREAMING_SNAKE_CASE_: Any     			=batch_size
     SCREAMING_SNAKE_CASE_: Tuple     			=encoder_seq_length
     SCREAMING_SNAKE_CASE_: str     			=decoder_seq_length
     # For common tests
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.decoder_seq_length
     SCREAMING_SNAKE_CASE_: List[Any]     			=is_training
     SCREAMING_SNAKE_CASE_: Tuple     			=use_attention_mask
     SCREAMING_SNAKE_CASE_: List[str]     			=use_labels
     SCREAMING_SNAKE_CASE_: Tuple     			=vocab_size
     SCREAMING_SNAKE_CASE_: Tuple     			=hidden_size
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=d_ff
     SCREAMING_SNAKE_CASE_: Optional[int]     			=relative_attention_num_buckets
     SCREAMING_SNAKE_CASE_: int     			=dropout_rate
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=initializer_factor
     SCREAMING_SNAKE_CASE_: List[str]     			=eos_token_id
     SCREAMING_SNAKE_CASE_: Tuple     			=pad_token_id
     SCREAMING_SNAKE_CASE_: List[Any]     			=decoder_start_token_id
     SCREAMING_SNAKE_CASE_: Optional[int]     			=None
     SCREAMING_SNAKE_CASE_: List[str]     			=decoder_layers



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


     '''simple docstring'''




     return TaConfig.from_pretrained("""google/umt5-base"""		)



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					Optional[Any]=None       ,		lowerCAmelCase :					int=None       ,		lowerCAmelCase :					str=None       ,		lowerCAmelCase :					List[str]=None       ,		lowerCAmelCase :					List[Any]=None       ,		)     ->					Any:


     '''simple docstring'''




     if attention_mask is None:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=input_ids.ne(config.pad_token_id		)
     if decoder_attention_mask is None:
        SCREAMING_SNAKE_CASE_: Dict     			=decoder_input_ids.ne(config.pad_token_id		)
     if head_mask is None:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.ones(config.num_hidden_layers       ,		config.num_attention_heads       ,		device=lowerCAmelCase		)
     if decoder_head_mask is None:
        SCREAMING_SNAKE_CASE_: str     			=torch.ones(config.num_decoder_layers       ,		config.num_attention_heads       ,		device=lowerCAmelCase		)
     if cross_attn_head_mask is None:
        SCREAMING_SNAKE_CASE_: Tuple     			=torch.ones(
            config.num_decoder_layers       ,		config.num_attention_heads       ,		device=lowerCAmelCase		)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=ids_tensor([self.batch_size, self.encoder_seq_length]       ,		self.vocab_size		)
     SCREAMING_SNAKE_CASE_: Any     			=ids_tensor([self.batch_size, self.decoder_seq_length]       ,		self.vocab_size		)

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for NllbMoe the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=input_ids.clamp(self.pad_token_id + 1		)
     SCREAMING_SNAKE_CASE_: str     			=decoder_input_ids.clamp(self.pad_token_id + 1		)

     SCREAMING_SNAKE_CASE_: Any     			=self.get_config()
     SCREAMING_SNAKE_CASE_: Dict     			=config.num_attention_heads
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.prepare_inputs_dict(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
     return config, input_dict



  def 	lowerCamelCase__       (						self :					Dict		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=self.prepare_config_and_inputs()
     return config, inputs_dict



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


     '''simple docstring'''




     return TaConfig(
         vocab_size=166       ,		d_model=self.hidden_size       ,		d_ff=self.d_ff       ,		d_kv=self.hidden_size // self.num_attention_heads       ,		num_layers=self.num_hidden_layers       ,		num_decoder_layers=self.decoder_layers       ,		num_heads=self.num_attention_heads       ,		relative_attention_num_buckets=self.relative_attention_num_buckets       ,		dropout_rate=self.dropout_rate       ,		initializer_factor=self.initializer_factor       ,		eos_token_id=self.eos_token_id       ,		bos_token_id=self.pad_token_id       ,		pad_token_id=self.pad_token_id       ,		decoder_start_token_id=self.decoder_start_token_id       ,		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Optional[int]:


     '''simple docstring'''




     return TaConfig(
         vocab_size=self.vocab_size       ,		d_model=self.hidden_size       ,		d_ff=self.d_ff       ,		d_kv=self.hidden_size // self.num_attention_heads       ,		num_layers=self.num_hidden_layers       ,		num_decoder_layers=self.decoder_layers       ,		num_heads=self.num_attention_heads       ,		relative_attention_num_buckets=self.relative_attention_num_buckets       ,		dropout_rate=self.dropout_rate       ,		initializer_factor=self.initializer_factor       ,		eos_token_id=self.eos_token_id       ,		bos_token_id=self.pad_token_id       ,		pad_token_id=self.pad_token_id       ,		decoder_start_token_id=self.decoder_start_token_id       ,		)



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Dict       ,		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=UMTaModel(config=lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: List[str]     			=model(
         input_ids=lowerCAmelCase       ,		decoder_input_ids=lowerCAmelCase       ,		attention_mask=lowerCAmelCase       ,		decoder_attention_mask=lowerCAmelCase       ,		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(input_ids=lowerCAmelCase       ,		decoder_input_ids=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=result.last_hidden_state
     SCREAMING_SNAKE_CASE_: Any     			=result.past_key_values
     SCREAMING_SNAKE_CASE_: Dict     			=result.encoder_last_hidden_state

     self.parent.assertEqual(encoder_output.size()       ,		(self.batch_size, self.encoder_seq_length, self.hidden_size)		)
     self.parent.assertEqual(decoder_output.size()       ,		(self.batch_size, self.decoder_seq_length, self.hidden_size)		)
     # There should be `num_layers` key value embeddings stored in decoder_past
     self.parent.assertEqual(len(lowerCAmelCase		)       ,		config.num_layers		)
     # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
     self.parent.assertEqual(len(decoder_past[0]		)       ,		4		)



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Optional[int]       ,		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=UMTaModel(config=lowerCAmelCase		).get_decoder().to(lowerCAmelCase		).eval()
     # first forward pass
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(lowerCAmelCase       ,		use_cache=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=model(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=model(lowerCAmelCase       ,		use_cache=lowerCAmelCase		)

     self.parent.assertTrue(len(lowerCAmelCase		) == len(lowerCAmelCase		)		)
     self.parent.assertTrue(len(lowerCAmelCase		) == len(lowerCAmelCase		) + 1		)

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE_: int     			=ids_tensor((self.batch_size, 1)       ,		config.vocab_size		)

     # append to next input_ids and
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.cat([input_ids, next_tokens]       ,		dim=-1		)

     SCREAMING_SNAKE_CASE_: Any     			=model(lowerCAmelCase		)["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE_: List[Any]     			=model(lowerCAmelCase       ,		past_key_values=lowerCAmelCase		)["""last_hidden_state"""]

     # select random slice
     SCREAMING_SNAKE_CASE_: Tuple     			=ids_tensor((1,)       ,		output_from_past.shape[-1]		).item()
     SCREAMING_SNAKE_CASE_: int     			=output_from_no_past[:, -1, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE_: str     			=output_from_past[:, 0, random_slice_idx].detach()

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(lowerCAmelCase       ,		lowerCAmelCase       ,		atol=1E-3		)		)





  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Tuple       ,		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=UMTaModel(config=lowerCAmelCase		).to(lowerCAmelCase		).half().eval()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(**lowerCAmelCase		)["""last_hidden_state"""]
     self.parent.assertFalse(torch.isnan(lowerCAmelCase		).any().item()		)







@require_torch
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				List[str]     						=					(
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  UpperCamelCase	:				Tuple     						=					(UMTaForConditionalGeneration,) if is_torch_available() else ()
  UpperCamelCase	:				Dict     						=					(
      {
          'conversational': UMTaForConditionalGeneration,
          'feature-extraction': UMTaModel,
          'summarization': UMTaForConditionalGeneration,
          'text2text-generation': UMTaForConditionalGeneration,
          'translation': UMTaForConditionalGeneration,
          'question-answering': UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  UpperCamelCase	:				Optional[int]     						=					True
  UpperCamelCase	:				str     						=					False
  UpperCamelCase	:				List[str]     						=					False
  UpperCamelCase	:				Optional[Any]     						=					True
  UpperCamelCase	:				str     						=					True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  UpperCamelCase	:				int     						=					[0.8, 0.9]



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=UMTaModelTester(self		)



  @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""		)
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE_: Dict     			=UMTaModel(config_and_inputs[0]		).to(lowerCAmelCase		)
     with tempfile.TemporaryDirectory() as tmpdirname:
        torch.onnx.export(
            lowerCAmelCase       ,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])       ,		f'''{tmpdirname}/t5_test.onnx'''       ,		export_params=lowerCAmelCase       ,		opset_version=9       ,		input_names=["""input_ids""", """decoder_input_ids"""]       ,		)



  @unittest.skipIf(torch_device == """cpu"""       ,		"""Cant do half precision"""		)
  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE_: List[str]     			=config_and_inputs[0]
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=UMTaForConditionalGeneration(lowerCAmelCase		).eval()
     model.to(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: int     			={
         """head_mask""": torch.zeros(config.num_layers       ,		config.num_heads       ,		device=lowerCAmelCase		),
         """decoder_head_mask""": torch.zeros(config.num_decoder_layers       ,		config.num_heads       ,		device=lowerCAmelCase		),
         """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers       ,		config.num_heads       ,		device=lowerCAmelCase		),
     }

     for attn_name, (name, mask) in zip(lowerCAmelCase       ,		head_masking.items()		):
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			={name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
           SCREAMING_SNAKE_CASE_: Dict     			=torch.ones(
               config.num_decoder_layers       ,		config.num_heads       ,		device=lowerCAmelCase		)

        SCREAMING_SNAKE_CASE_: int     			=model.generate(
            config_and_inputs[1]["""input_ids"""]       ,		num_beams=1       ,		max_length=3       ,		output_attentions=lowerCAmelCase       ,		return_dict_in_generate=lowerCAmelCase       ,		**lowerCAmelCase       ,		)
        # We check the state of decoder_attentions and cross_attentions just from the last step
        SCREAMING_SNAKE_CASE_: Tuple     			=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
        self.assertEqual(sum([w.sum().item() for w in attn_weights]		)       ,		0.0		)





  @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""		)
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 					a	(  unittest.TestCase       ):



  @slow
  @unittest.skip(
      """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""		)
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""       ,		return_dict=lowerCAmelCase		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=AutoTokenizer.from_pretrained("""google/umt5-small"""       ,		use_fast=lowerCAmelCase       ,		legacy=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[
         """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
         """No se como puedo <extra_id_0>.""",
         """This is the reason why we <extra_id_0> them.""",
         """The <extra_id_0> walks in <extra_id_1>, seats""",
         """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
     ]
     SCREAMING_SNAKE_CASE_: int     			=tokenizer(lowerCAmelCase       ,		return_tensors="""pt"""       ,		padding=lowerCAmelCase		).input_ids
     # fmt: off
     SCREAMING_SNAKE_CASE_: Any     			=torch.tensor(
         [
             [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
         ]		)
     # fmt: on
     torch.testing.assert_allclose(lowerCAmelCase       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=model.generate(input_ids.to(lowerCAmelCase		)		)
     SCREAMING_SNAKE_CASE_: Dict     			=[
         """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
         """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
         """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
         """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
         """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
     ]
     SCREAMING_SNAKE_CASE_: Optional[int]     			=tokenizer.batch_decode(lowerCAmelCase		)
     self.assertEqual(lowerCAmelCase       ,		lowerCAmelCase		)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      SCREAMING_SNAKE_CASE_: Tuple     			=True
      for i in range(0							,			len(lowercase ) - 1							,			2 ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: Tuple     			=False

      for i in range(1							,			len(lowercase ) - 1							,			2 ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: str     			=False
   return input_list


if __name__ == "__main__":
   print("""Enter list to be sorted""")
   _UpperCAmelCase     =			[int(x) for x in input().split()]
   # inputing elements of the list in one line
   _UpperCAmelCase     =			odd_even_sort(input_list)
   print("""The sorted list is""")
   print(sorted_list)



