






from __future__ import annotations

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







@total_ordering
class 				UpperCAmelCase_  (		__lowerCamelCase		):




     def __lt__(				self	,						_lowerCAmelCase		):
          return self[-1] < other[-1]




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





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			list:

     '''simple docstring'''
     UpperCAmelCase__      :  list[Stack]				      =			[]
     # sort into stacks
     for element in collection:
          UpperCAmelCase__      :  Union[str, Any]				      =			Stack([element]       )
          UpperCAmelCase__      :  Union[str, Any]				      =			bisect_left(__lowerCamelCase ,							__lowerCamelCase       )
          if i != len(__lowerCamelCase       ):
               stacks[i].append(__lowerCamelCase       )
          else:
               stacks.append(__lowerCamelCase       )

    # use a heap-based merge to merge stack efficiently
     UpperCAmelCase__      :  Optional[int]				      =			merge(*(reversed(__lowerCamelCase       ) for stack in stacks)       )
     return collection


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








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        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase		,    lowercase=99		,    lowercase=13		,    lowercase=7		,    lowercase=9		,    lowercase=True		,    lowercase=True		,    lowercase=False		,    lowercase=32		,    lowercase=5		,    lowercase=4		,    lowercase=37		,    lowercase=8		,    lowercase=0.1		,    lowercase=0.0_0_2		,    lowercase=1		,    lowercase=0		,    lowercase=0		,    lowercase=None		,    lowercase=None		,    )							->			Tuple:
					lowerCamelCase_         =     parent
					lowerCamelCase_         =     batch_size
					lowerCamelCase_         =     encoder_seq_length
					lowerCamelCase_         =     decoder_seq_length
					# For common tests
					lowerCamelCase_         =     self.decoder_seq_length
					lowerCamelCase_         =     is_training
					lowerCamelCase_         =     use_attention_mask
					lowerCamelCase_         =     use_labels
					lowerCamelCase_         =     vocab_size
					lowerCamelCase_         =     hidden_size
					lowerCamelCase_         =     num_hidden_layers
					lowerCamelCase_         =     num_attention_heads
					lowerCamelCase_         =     d_ff
					lowerCamelCase_         =     relative_attention_num_buckets
					lowerCamelCase_         =     dropout_rate
					lowerCamelCase_         =     initializer_factor
					lowerCamelCase_         =     eos_token_id
					lowerCamelCase_         =     pad_token_id
					lowerCamelCase_         =     decoder_start_token_id
					lowerCamelCase_         =     None
					lowerCamelCase_         =     decoder_layers



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return TaConfig.from_pretrained("google/umt5-base"					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase=None		,    lowercase=None		,    lowercase=None		,    lowercase=None		,    lowercase=None		,    )							->			str:
					if attention_mask is None:
									lowerCamelCase_         =     input_ids.ne(config.pad_token_id					)
					if decoder_attention_mask is None:
									lowerCamelCase_         =     decoder_input_ids.ne(config.pad_token_id					)
					if head_mask is None:
									lowerCamelCase_         =     torch.ones(config.num_hidden_layers		,    config.num_attention_heads		,    device=lowercase					)
					if decoder_head_mask is None:
									lowerCamelCase_         =     torch.ones(config.num_decoder_layers		,    config.num_attention_heads		,    device=lowercase					)
					if cross_attn_head_mask is None:
									lowerCamelCase_         =     torch.ones(
									    config.num_decoder_layers		,    config.num_attention_heads		,    device=lowercase					)
					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					)							->			Dict:
					lowerCamelCase_         =     ids_tensor([self.batch_size, self.encoder_seq_length]		,    self.vocab_size					)
					lowerCamelCase_         =     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
					lowerCamelCase_         =     input_ids.clamp(self.pad_token_id + 1					)
					lowerCamelCase_         =     decoder_input_ids.clamp(self.pad_token_id + 1					)

					lowerCamelCase_         =     self.get_config()
					lowerCamelCase_         =     config.num_attention_heads
					lowerCamelCase_         =     self.prepare_inputs_dict(lowercase		,    lowercase		,    lowercase					)
					return config, input_dict



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_      ,       lowerCamelCase_         =     self.prepare_config_and_inputs()
					return config, inputs_dict



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					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					)							->			Any:
					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		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    )							->			Optional[Any]:
					lowerCamelCase_         =     UMTaModel(config=lowercase					)
					model.to(lowercase					)
					model.eval()
					lowerCamelCase_         =     model(
					    input_ids=lowercase		,    decoder_input_ids=lowercase		,    attention_mask=lowercase		,    decoder_attention_mask=lowercase		,    )
					lowerCamelCase_         =     model(input_ids=lowercase		,    decoder_input_ids=lowercase					)
					lowerCamelCase_         =     result.last_hidden_state
					lowerCamelCase_         =     result.past_key_values
					lowerCamelCase_         =     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(lowercase					)		,    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		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    )							->			Optional[int]:
					lowerCamelCase_         =     UMTaModel(config=lowercase					).get_decoder().to(lowercase					).eval()
					# first forward pass
					lowerCamelCase_         =     model(lowercase		,    use_cache=lowercase					)
					lowerCamelCase_         =     model(lowercase					)
					lowerCamelCase_         =     model(lowercase		,    use_cache=lowercase					)

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

					lowerCamelCase_      ,       lowerCamelCase_         =     outputs.to_tuple()

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

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

					lowerCamelCase_         =     model(lowercase					)["last_hidden_state"]
					lowerCamelCase_         =     model(lowercase		,    past_key_values=lowercase					)["last_hidden_state"]

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

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    )							->			Tuple:
					lowerCamelCase_         =     UMTaModel(config=lowercase					).to(lowercase					).half().eval()
					lowerCamelCase_         =     model(**lowercase					)["last_hidden_state"]
					self.parent.assertFalse(torch.isnan(lowercase					).any().item()					)




@require_torch
class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			snake_case_     ,			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        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_         =     UMTaModelTester(self					)



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



	@unittest.skipIf(torch_device == "cpu"		,    "Cant do half precision"					)
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     ["encoder_attentions", "decoder_attentions", "cross_attentions"]
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					lowerCamelCase_         =     config_and_inputs[0]
					lowerCamelCase_         =     UMTaForConditionalGeneration(lowercase					).eval()
					model.to(lowercase					)

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

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

									lowerCamelCase_         =     model.generate(
									    config_and_inputs[1]["input_ids"]		,    num_beams=1		,    max_length=3		,    output_attentions=lowercase		,    return_dict_in_generate=lowercase		,    **lowercase		,    )
									# We check the state of decoder_attentions and cross_attentions just from the last step
									lowerCamelCase_         =     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					)							->			Tuple:
					pass




@require_torch
@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE	( 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					)							->			int:
					lowerCamelCase_         =     UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,    return_dict=lowercase					).to(lowercase					)
					lowerCamelCase_         =     AutoTokenizer.from_pretrained("google/umt5-small"		,    use_fast=lowercase		,    legacy=lowercase					)
					lowerCamelCase_         =     [
					    "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>.",
					]
					lowerCamelCase_         =     tokenizer(lowercase		,    return_tensors="pt"		,    padding=lowercase					).input_ids
					# fmt: off
					lowerCamelCase_         =     torch.tensor(
					    [
					        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
					    ]					)
					# fmt: on
					torch.testing.assert_allclose(lowercase		,    lowercase					)

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


