
"""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	,    _a	,    _a=99	,    _a=13	,    _a=7	,    _a=9	,    _a=True	,    _a=True	,    _a=False	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a=8	,    _a=0.1	,    _a=0.0_0_2	,    _a=1	,    _a=0	,    _a=0	,    _a=None	,    _a=None	,    ):
          lowercase    :				Optional[Any]       		=							parent
          lowercase    :				Optional[Any]       		=							batch_size
          lowercase    :				List[Any]       		=							encoder_seq_length
          lowercase    :				Dict       		=							decoder_seq_length
          # For common tests
          lowercase    :				Tuple       		=							self.decoder_seq_length
          lowercase    :				Tuple       		=							is_training
          lowercase    :				Union[str, Any]       		=							use_attention_mask
          lowercase    :				Dict       		=							use_labels
          lowercase    :				str       		=							vocab_size
          lowercase    :				List[str]       		=							hidden_size
          lowercase    :				List[Any]       		=							num_hidden_layers
          lowercase    :				int       		=							num_attention_heads
          lowercase    :				int       		=							d_ff
          lowercase    :				Optional[Any]       		=							relative_attention_num_buckets
          lowercase    :				Optional[Any]       		=							dropout_rate
          lowercase    :				Tuple       		=							initializer_factor
          lowercase    :				Optional[int]       		=							eos_token_id
          lowercase    :				List[Any]       		=							pad_token_id
          lowercase    :				Union[str, Any]       		=							decoder_start_token_id
          lowercase    :				List[str]       		=							None
          lowercase    :				Tuple       		=							decoder_layers
   def 			__magic_name__			(			self				):
          return TaConfig.from_pretrained("google/umt5-base"				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    ):
          if attention_mask is None:
                 lowercase    :				int       		=							input_ids.ne(config.pad_token_id				)
          if decoder_attention_mask is None:
                 lowercase    :				str       		=							decoder_input_ids.ne(config.pad_token_id				)
          if head_mask is None:
                 lowercase    :				str       		=							torch.ones(config.num_hidden_layers	,    config.num_attention_heads	,    device=_a				)
          if decoder_head_mask is None:
                 lowercase    :				List[Any]       		=							torch.ones(config.num_decoder_layers	,    config.num_attention_heads	,    device=_a				)
          if cross_attn_head_mask is None:
                 lowercase    :				Union[str, Any]       		=							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 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							ids_tensor([self.batch_size, self.encoder_seq_length]	,    self.vocab_size				)
          lowercase    :				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
          lowercase    :				Optional[int]       		=							input_ids.clamp(self.pad_token_id + 1				)
          lowercase    :				int       		=							decoder_input_ids.clamp(self.pad_token_id + 1				)

          lowercase    :				List[Any]       		=							self.get_config()
          lowercase    :				List[Any]       		=							config.num_attention_heads
          lowercase    :				Any       		=							self.prepare_inputs_dict(_a	,    _a	,    _a				)
          return config, input_dict
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Any       		=							self.prepare_config_and_inputs()
          return config, inputs_dict
   def 			__magic_name__			(			self				):
          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 			__magic_name__			(			self				):
          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 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    ):
          lowercase    :				Dict       		=							UMTaModel(config=_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Union[str, Any]       		=							model(
              input_ids=_a	,    decoder_input_ids=_a	,    attention_mask=_a	,    decoder_attention_mask=_a	,    )
          lowercase    :				int       		=							model(input_ids=_a	,    decoder_input_ids=_a				)
          lowercase    :				Any       		=							result.last_hidden_state
          lowercase    :				Dict       		=							result.past_key_values
          lowercase    :				str       		=							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 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    ):
          lowercase    :				int       		=							UMTaModel(config=_a				).get_decoder().to(_a				).eval()
          # first forward pass
          lowercase    :				Optional[int]       		=							model(_a	,    use_cache=_a				)
          lowercase    :				str       		=							model(_a				)
          lowercase    :				Optional[Any]       		=							model(_a	,    use_cache=_a				)

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

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

          # create hypothetical next token and extent to next_input_ids
          lowercase    :				List[str]       		=							ids_tensor((self.batch_size, 1)	,    config.vocab_size				)

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

          lowercase    :				List[Any]       		=							model(_a				)["last_hidden_state"]
          lowercase    :				Optional[int]       		=							model(_a	,    past_key_values=_a				)["last_hidden_state"]

          # select random slice
          lowercase    :				List[Any]       		=							ids_tensor((1,)	,    output_from_past.shape[-1]				).item()
          lowercase    :				List[Any]       		=							output_from_no_past[:, -1, random_slice_idx].detach()
          lowercase    :				Any       		=							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 			__magic_name__			(			self	,    _a	,    _a	,    ):
          lowercase    :				Any       		=							UMTaModel(config=_a				).to(_a				).half().eval()
          lowercase    :				Optional[int]       		=							model(**_a				)["last_hidden_state"]
          self.parent.assertFalse(torch.isnan(_a				).any().item()				)


@require_torch
class 		a__						(      a_,			a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(
       (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
   )
   __lowerCAmelCase            =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
   __lowerCAmelCase            =				(
       {
           """conversational""": UMTaForConditionalGeneration,
           """feature-extraction""": UMTaModel,
           """summarization""": UMTaForConditionalGeneration,
           """text2text-generation""": UMTaForConditionalGeneration,
           """translation""": UMTaForConditionalGeneration,
           """question-answering""": UMTaForQuestionAnswering,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase            =				True
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				True
   __lowerCAmelCase            =				True
   # The small UMT5 model needs higher percentages for CPU/MP tests
   __lowerCAmelCase            =				[0.8, 0.9]
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							UMTaModelTester(self				)
   @unittest.skip("Test has a segmentation fault on torch 1.8.0"				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs()
          lowercase    :				Any       		=							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 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							["encoder_attentions", "decoder_attentions", "cross_attentions"]
          lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs()
          lowercase    :				List[str]       		=							config_and_inputs[0]
          lowercase    :				Optional[Any]       		=							UMTaForConditionalGeneration(_a				).eval()
          model.to(_a				)

          lowercase    :				Optional[Any]       		=							{
              "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()				):
                 lowercase    :				List[Any]       		=							{name: mask}
                 # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                 if name == "head_mask":
                        lowercase    :				Union[str, Any]       		=							torch.ones(
                            config.num_decoder_layers	,    config.num_heads	,    device=_a				)

                 lowercase    :				Union[str, Any]       		=							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
                 lowercase    :				Optional[Any]       		=							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 			__magic_name__			(			self				):
          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 			__magic_name__			(			self				):
          lowercase    :				str       		=							UMTaForConditionalGeneration.from_pretrained("google/umt5-small"	,    return_dict=_a				).to(_a				)
          lowercase    :				int       		=							AutoTokenizer.from_pretrained("google/umt5-small"	,    use_fast=_a	,    legacy=_a				)
          lowercase    :				int       		=							[
              "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>.",
          ]
          lowercase    :				Optional[Any]       		=							tokenizer(_a	,    return_tensors="pt"	,    padding=_a				).input_ids
          # fmt: off
          lowercase    :				List[Any]       		=							torch.tensor(
              [
                  [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
              ]				)
          # fmt: on
          torch.testing.assert_allclose(_a	,    _a				)

          lowercase    :				List[str]       		=							model.generate(input_ids.to(_a				)				)
          lowercase    :				str       		=							[
              "<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>",
          ]
          lowercase    :				List[Any]       		=							tokenizer.batch_decode(_a				)
          self.assertEqual(_a	,    _a				)




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


if __name__ == "__main__":
   _A			:       Optional[int]   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Optional[Any]   		=       [int(item) for item in user_input.split(""",""")]
   print(patience_sort(unsorted))


