
"""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    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=99 ,				snake_case_=13 ,				snake_case_=7 ,				snake_case_=9 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=False ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_=8 ,				snake_case_=0.1 ,				snake_case_=0.002 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=0 ,				snake_case_=None ,				snake_case_=None ,				)		->       List[Any]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    encoder_seq_length
       UpperCamelCase__         =    decoder_seq_length
       # For common tests
       UpperCamelCase__         =    self.decoder_seq_length
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_attention_mask
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    d_ff
       UpperCamelCase__         =    relative_attention_num_buckets
       UpperCamelCase__         =    dropout_rate
       UpperCamelCase__         =    initializer_factor
       UpperCamelCase__         =    eos_token_id
       UpperCamelCase__         =    pad_token_id
       UpperCamelCase__         =    decoder_start_token_id
       UpperCamelCase__         =    None
       UpperCamelCase__         =    decoder_layers

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       return TaConfig.from_pretrained('google/umt5-base'      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				)		->       Optional[Any]:
       if attention_mask is None:
           UpperCamelCase__         =    input_ids.ne(config.pad_token_id      )
       if decoder_attention_mask is None:
           UpperCamelCase__         =    decoder_input_ids.ne(config.pad_token_id      )
       if head_mask is None:
           UpperCamelCase__         =    torch.ones(config.num_hidden_layers ,				config.num_attention_heads ,				device=__A      )
       if decoder_head_mask is None:
           UpperCamelCase__         =    torch.ones(config.num_decoder_layers ,				config.num_attention_heads ,				device=__A      )
       if cross_attn_head_mask is None:
           UpperCamelCase__         =    torch.ones(
               config.num_decoder_layers ,				config.num_attention_heads ,				device=__A      )
       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    ids_tensor([self.batch_size, self.encoder_seq_length] ,				self.vocab_size      )
       UpperCamelCase__         =    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
       UpperCamelCase__         =    input_ids.clamp(self.pad_token_id + 1      )
       UpperCamelCase__         =    decoder_input_ids.clamp(self.pad_token_id + 1      )

       UpperCamelCase__         =    self.get_config()
       UpperCamelCase__         =    config.num_attention_heads
       UpperCamelCase__         =    self.prepare_inputs_dict(__A ,				__A ,				__A      )
       return config, input_dict

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       return config, inputs_dict

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				)		->       Dict:
       UpperCamelCase__         =    UMTaModel(config=__A      )
       model.to(__A      )
       model.eval()
       UpperCamelCase__         =    model(
           input_ids=__A ,				decoder_input_ids=__A ,				attention_mask=__A ,				decoder_attention_mask=__A ,				)
       UpperCamelCase__         =    model(input_ids=__A ,				decoder_input_ids=__A      )
       UpperCamelCase__         =    result.last_hidden_state
       UpperCamelCase__         =    result.past_key_values
       UpperCamelCase__         =    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(__A      ) ,				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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				)		->       str:
       UpperCamelCase__         =    UMTaModel(config=__A      ).get_decoder().to(__A      ).eval()
       # first forward pass
       UpperCamelCase__         =    model(__A ,				use_cache=__A      )
       UpperCamelCase__         =    model(__A      )
       UpperCamelCase__         =    model(__A ,				use_cache=__A      )

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

       UpperCamelCase__         =    outputs.to_tuple()

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

       # append to next input_ids and
       UpperCamelCase__         =    torch.cat([input_ids, next_tokens] ,				dim=-1      )

       UpperCamelCase__         =    model(__A      )["last_hidden_state"]
       UpperCamelCase__         =    model(__A ,				past_key_values=__A      )["last_hidden_state"]

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

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







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				)		->       int:
       UpperCamelCase__         =    UMTaModel(config=__A      ).to(__A      ).half().eval()
       UpperCamelCase__         =    model(**__A      )["last_hidden_state"]
       self.parent.assertFalse(torch.isnan(__A      ).any().item()      )







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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    UMTaModelTester(self      )

   @unittest.skip('Test has a segmentation fault on torch 1.8.0'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       UpperCamelCase__         =    UMTaModel(config_and_inputs[0]      ).to(__A      )
       with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               __A ,				(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,				F'{tmpdirname}/t5_test.onnx' ,				export_params=__A ,				opset_version=9 ,				input_names=['input_ids', 'decoder_input_ids'] ,				)

   @unittest.skipIf(torch_device == 'cpu' ,				'Cant do half precision'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*__A      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    ["encoder_attentions", "decoder_attentions", "cross_attentions"]
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       UpperCamelCase__         =    config_and_inputs[0]
       UpperCamelCase__         =    UMTaForConditionalGeneration(__A      ).eval()
       model.to(__A      )

       UpperCamelCase__         =    {
           "head_mask": torch.zeros(config.num_layers ,				config.num_heads ,				device=__A      ),
           "decoder_head_mask": torch.zeros(config.num_decoder_layers ,				config.num_heads ,				device=__A      ),
           "cross_attn_head_mask": torch.zeros(config.num_decoder_layers ,				config.num_heads ,				device=__A      ),
       }

       for attn_name, (name, mask) in zip(__A ,				head_masking.items()      ):
           UpperCamelCase__         =    {name: mask}
           # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
           if name == "head_mask":
               UpperCamelCase__         =    torch.ones(
                   config.num_decoder_layers ,				config.num_heads ,				device=__A      )

           UpperCamelCase__         =    model.generate(
               config_and_inputs[1]['input_ids'] ,				num_beams=1 ,				max_length=3 ,				output_attentions=__A ,				return_dict_in_generate=__A ,				**__A ,				)
           # We check the state of decoder_attentions and cross_attentions just from the last step
           UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       pass







@require_torch
@require_sentencepiece
@require_tokenizers
class    __lowerCamelCase    (  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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    UMTaForConditionalGeneration.from_pretrained('google/umt5-small' ,				return_dict=__A      ).to(__A      )
       UpperCamelCase__         =    AutoTokenizer.from_pretrained('google/umt5-small' ,				use_fast=__A ,				legacy=__A      )
       UpperCamelCase__         =    [
           "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>.",
       ]
       UpperCamelCase__         =    tokenizer(__A ,				return_tensors='pt' ,				padding=__A      ).input_ids
       # fmt: off
       UpperCamelCase__         =    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(__A ,				__A      )

       UpperCamelCase__         =    model.generate(input_ids.to(__A      )      )
       UpperCamelCase__         =    [
           "<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>",
       ]
       UpperCamelCase__         =    tokenizer.batch_decode(__A      )
       self.assertEqual(__A ,				__A      )












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[int]:
    """simple docstring"""

    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
    for i in range(SCREAMING_SNAKE_CASE					):
        for j in range(i + 1 ,	SCREAMING_SNAKE_CASE					):
            if numbers[j] < numbers[i]:
                UpperCamelCase__       ,						UpperCamelCase__         =    numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
  A__					: Union[str, Any]=	input("""Enter numbers separated by a comma:\n""").strip()
  A__					: List[Any]=	[int(item) for item in user_input.split(""",""")]
  print(exchange_sort(unsorted))




