
"""simple docstring"""




import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
   import torch

   from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer

class 		a__						:
   def __init__(			self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=False	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=16	,    _a=2	,    _a=0.0_2	,    _a=3	,    _a=4	,    _a=None	,    ):
          lowercase    :				Optional[Any]       		=							parent
          lowercase    :				int       		=							batch_size
          lowercase    :				str       		=							seq_length
          lowercase    :				Any       		=							is_training
          lowercase    :				Dict       		=							use_input_mask
          lowercase    :				List[str]       		=							use_token_type_ids
          lowercase    :				List[Any]       		=							use_labels
          lowercase    :				Any       		=							vocab_size
          lowercase    :				Optional[Any]       		=							hidden_size
          lowercase    :				Union[str, Any]       		=							num_hidden_layers
          lowercase    :				List[str]       		=							num_attention_heads
          lowercase    :				Optional[Any]       		=							intermediate_size
          lowercase    :				Tuple       		=							hidden_act
          lowercase    :				Any       		=							hidden_dropout_prob
          lowercase    :				Tuple       		=							attention_probs_dropout_prob
          lowercase    :				List[str]       		=							max_position_embeddings
          lowercase    :				Optional[Any]       		=							type_vocab_size
          lowercase    :				Optional[Any]       		=							type_sequence_label_size
          lowercase    :				List[Any]       		=							initializer_range
          lowercase    :				str       		=							num_labels
          lowercase    :				str       		=							num_choices
          lowercase    :				Union[str, Any]       		=							scope
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size				)

          lowercase    :				Union[str, Any]       		=							None
          if self.use_input_mask:
                 lowercase    :				int       		=							random_attention_mask([self.batch_size, self.seq_length]				)

          lowercase    :				Union[str, Any]       		=							None
          if self.use_token_type_ids:
                 lowercase    :				List[Any]       		=							ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size				)

          lowercase    :				Optional[int]       		=							None
          lowercase    :				Dict       		=							None
          lowercase    :				Union[str, Any]       		=							None
          if self.use_labels:
                 lowercase    :				Optional[int]       		=							ids_tensor([self.batch_size]	,    self.type_sequence_label_size				)
                 lowercase    :				str       		=							ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels				)
                 lowercase    :				Tuple       		=							ids_tensor([self.batch_size]	,    self.num_choices				)

          lowercase    :				Dict       		=							self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 			__magic_name__			(			self				):
          return LlamaConfig(
              vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=_a	,    initializer_range=self.initializer_range	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a				):
          lowercase    :				List[Any]       		=							LlamaModel(config=_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				int       		=							model(_a	,    attention_mask=_a				)
          lowercase    :				Union[str, Any]       		=							model(_a				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    ):
          lowercase    :				Optional[int]       		=							True
          lowercase    :				Tuple       		=							LlamaModel(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				int       		=							model(
              _a	,    attention_mask=_a	,    encoder_hidden_states=_a	,    encoder_attention_mask=_a	,    )
          lowercase    :				Union[str, Any]       		=							model(
              _a	,    attention_mask=_a	,    encoder_hidden_states=_a	,    )
          lowercase    :				Union[str, Any]       		=							model(_a	,    attention_mask=_a				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    ):
          lowercase    :				str       		=							LlamaForCausalLM(config=_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				List[str]       		=							model(_a	,    attention_mask=_a	,    labels=_a				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    ):
          lowercase    :				Union[str, Any]       		=							True
          lowercase    :				List[Any]       		=							True
          lowercase    :				List[Any]       		=							LlamaForCausalLM(config=_a				)
          model.to(_a				)
          model.eval()

          # first forward pass
          lowercase    :				Any       		=							model(
              _a	,    attention_mask=_a	,    encoder_hidden_states=_a	,    encoder_attention_mask=_a	,    use_cache=_a	,    )
          lowercase    :				str       		=							outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          lowercase    :				Optional[Any]       		=							ids_tensor((self.batch_size, 3)	,    config.vocab_size				)
          lowercase    :				int       		=							ids_tensor((self.batch_size, 3)	,    vocab_size=2				)

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

          lowercase    :				List[Any]       		=							model(
              _a	,    attention_mask=_a	,    encoder_hidden_states=_a	,    encoder_attention_mask=_a	,    output_hidden_states=_a	,    )["hidden_states"][0]
          lowercase    :				Union[str, Any]       		=							model(
              _a	,    attention_mask=_a	,    encoder_hidden_states=_a	,    encoder_attention_mask=_a	,    past_key_values=_a	,    output_hidden_states=_a	,    )["hidden_states"][0]

          # select random slice
          lowercase    :				List[Any]       		=							ids_tensor((1,)	,    output_from_past.shape[-1]				).item()
          lowercase    :				Tuple       		=							output_from_no_past[:, -3:, random_slice_idx].detach()
          lowercase    :				List[str]       		=							output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

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

   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.prepare_config_and_inputs()
          (
              (
              lowercase
          )		,    (
              lowercase
          )		,    (
              lowercase
          )		,    (
              lowercase
          )		,    (
              lowercase
          )		,    (
              lowercase
          )		,    (
              lowercase
          )		,    
          )    :				List[Any]       		=							config_and_inputs
          lowercase    :				int       		=							{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict


@require_torch
class 		a__						(      a_,			a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   __lowerCAmelCase            =				(LlamaForCausalLM,) if is_torch_available() else ()
   __lowerCAmelCase            =				(
       {
           """feature-extraction""": LlamaModel,
           """text-classification""": LlamaForSequenceClassification,
           """text-generation""": LlamaForCausalLM,
           """zero-shot""": LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							LlamaModelTester(self				)
          lowercase    :				List[str]       		=							ConfigTester(self	,    config_class=_a	,    hidden_size=37				)
   def 			__magic_name__			(			self				):
          self.config_tester.run_common_tests()
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowercase    :				str       		=							type
                 self.model_tester.create_and_check_model(*_a				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				List[str]       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				List[Any]       		=							3
          lowercase    :				Dict       		=							input_dict["input_ids"]
          lowercase    :				List[str]       		=							input_ids.ne(1				).to(_a				)
          lowercase    :				List[str]       		=							ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size				)
          lowercase    :				str       		=							LlamaForSequenceClassification(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Union[str, Any]       		=							model(_a	,    attention_mask=_a	,    labels=_a				)
          self.assertEqual(result.logits.shape	,    (self.model_tester.batch_size, self.model_tester.num_labels)				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Optional[int]       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				Union[str, Any]       		=							3
          lowercase    :				Union[str, Any]       		=							"single_label_classification"
          lowercase    :				str       		=							input_dict["input_ids"]
          lowercase    :				Dict       		=							input_ids.ne(1				).to(_a				)
          lowercase    :				str       		=							ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size				)
          lowercase    :				int       		=							LlamaForSequenceClassification(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Dict       		=							model(_a	,    attention_mask=_a	,    labels=_a				)
          self.assertEqual(result.logits.shape	,    (self.model_tester.batch_size, self.model_tester.num_labels)				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				List[str]       		=							3
          lowercase    :				Union[str, Any]       		=							"multi_label_classification"
          lowercase    :				Union[str, Any]       		=							input_dict["input_ids"]
          lowercase    :				List[str]       		=							input_ids.ne(1				).to(_a				)
          lowercase    :				Dict       		=							ids_tensor(
              [self.model_tester.batch_size, config.num_labels]	,    self.model_tester.type_sequence_label_size				).to(torch.float				)
          lowercase    :				Union[str, Any]       		=							LlamaForSequenceClassification(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				int       		=							model(_a	,    attention_mask=_a	,    labels=_a				)
          self.assertEqual(result.logits.shape	,    (self.model_tester.batch_size, self.model_tester.num_labels)				)
   @unittest.skip("LLaMA buffers include complex numbers, which breaks this test"				)
   def 			__magic_name__			(			self				):
          pass

   @parameterized.expand([("linear",), ("dynamic",)]				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase		,    lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				Optional[int]       		=							ids_tensor([1, 10]	,    config.vocab_size				)
          lowercase    :				List[str]       		=							ids_tensor([1, int(config.max_position_embeddings * 1.5				)]	,    config.vocab_size				)

          set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
          lowercase    :				Optional[int]       		=							LlamaModel(_a				)
          original_model.to(_a				)
          original_model.eval()
          lowercase    :				Dict       		=							original_model(_a				).last_hidden_state
          lowercase    :				str       		=							original_model(_a				).last_hidden_state

          set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
          lowercase    :				Optional[int]       		=							{"type": scaling_type, "factor": 1_0.0}
          lowercase    :				List[str]       		=							LlamaModel(_a				)
          scaled_model.to(_a				)
          scaled_model.eval()
          lowercase    :				Union[str, Any]       		=							scaled_model(_a				).last_hidden_state
          lowercase    :				List[str]       		=							scaled_model(_a				).last_hidden_state

          # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
          # maximum sequence length, so the outputs for the short input should match.
          if scaling_type == "dynamic":
                 self.assertTrue(torch.allclose(_a	,    _a	,    atol=1E-5				)				)
          else:
                 self.assertFalse(torch.allclose(_a	,    _a	,    atol=1E-5				)				)

          # The output should be different for long inputs
          self.assertFalse(torch.allclose(_a	,    _a	,    atol=1E-5				)				)


@require_torch
class 		a__						(      unittest.TestCase						):
   @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
          lowercase    :				Optional[Any]       		=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"	,    device_map="auto"				)
          lowercase    :				int       		=							model(torch.tensor([input_ids]				)				)
          # Expected mean on dim = -1
          lowercase    :				Optional[int]       		=							torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]				)
          torch.testing.assert_close(out.mean(-1				)	,    _a	,    atol=1E-2	,    rtol=1E-2				)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          lowercase    :				Optional[Any]       		=							torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]				)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]	,    _a	,    atol=1E-5	,    rtol=1E-5				)
   @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
          lowercase    :				Optional[int]       		=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"	,    device_map="auto"				)
          lowercase    :				Any       		=							model(torch.tensor(_a				)				)
          # Expected mean on dim = -1
          lowercase    :				Optional[Any]       		=							torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]				)
          torch.testing.assert_close(out.mean(-1				)	,    _a	,    atol=1E-2	,    rtol=1E-2				)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          lowercase    :				Optional[int]       		=							torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]				)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]	,    _a	,    atol=1E-5	,    rtol=1E-5				)
   @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
          lowercase    :				int       		=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"	,    device_map="auto"				)
          lowercase    :				List[str]       		=							model(torch.tensor(_a				)				)
          # Expected mean on dim = -1
          lowercase    :				Optional[Any]       		=							torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]				)
          torch.testing.assert_close(out.mean(-1				)	,    _a	,    atol=1E-2	,    rtol=1E-2				)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          lowercase    :				Optional[Any]       		=							torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]				)
          # fmt: on
          torch.testing.assert_close(out.mean(-1				)	,    _a	,    atol=1E-2	,    rtol=1E-2				)
   @unittest.skip(
       "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
          lowercase    :				List[Any]       		=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"	,    device_map="auto"				)
          lowercase    :				Any       		=							model(torch.tensor(_a				)				)

          lowercase    :				Tuple       		=							torch.tensor(
              [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]	,    dtype=torch.floataa				)
          torch.testing.assert_close(out.mean(-1				)	,    _a	,    atol=1E-2	,    rtol=1E-2				)
          # fmt: off
          lowercase    :				Optional[int]       		=							torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]				)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]	,    _a	,    atol=1E-5	,    rtol=1E-5				)

   @unittest.skip("Model is curently gated"				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							"Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
          lowercase    :				Optional[Any]       		=							"Simply put, the theory of relativity states that "
          lowercase    :				Optional[Any]       		=							LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"				)
          lowercase    :				Optional[Any]       		=							tokenizer.encode(_a	,    return_tensors="pt"				)
          lowercase    :				Any       		=							LlamaForCausalLM.from_pretrained(
              "meta-llama/Llama-2-13b-chat-hf"	,    device_map="sequential"	,    use_safetensors=_a				)

          # greedy generation outputs
          lowercase    :				Dict       		=							model.generate(_a	,    max_new_tokens=64	,    top_p=_a	,    temperature=1	,    do_sample=_a				)
          lowercase    :				Optional[int]       		=							tokenizer.decode(generated_ids[0]	,    skip_special_tokens=_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))


