


"""simple docstring"""







import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel


class 				SCREAMING_SNAKE_CASE__    :
							"""simple docstring"""


							def __init__(						self		,					snake_case__		,					snake_case__=13		,					snake_case__=7		,					snake_case__=True		,					snake_case__=True		,					snake_case__=False		,					snake_case__=True		,					snake_case__=99		,					snake_case__=32		,					snake_case__=5		,					snake_case__=4		,					snake_case__=37		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=512		,					snake_case__=16		,					snake_case__=2		,					snake_case__=0.02		,					snake_case__=3		,					snake_case__=4		,					snake_case__=None		,					):






									"""simple docstring"""





									lowerCAmelCase					:					str								=							parent
									lowerCAmelCase					:					Dict								=							batch_size
									lowerCAmelCase					:					Optional[int]								=							seq_length
									lowerCAmelCase					:					int								=							is_training
									lowerCAmelCase					:					str								=							use_input_mask
									lowerCAmelCase					:					List[str]								=							use_token_type_ids
									lowerCAmelCase					:					Optional[int]								=							use_labels
									lowerCAmelCase					:					Tuple								=							vocab_size
									lowerCAmelCase					:					int								=							hidden_size
									lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
									lowerCAmelCase					:					Tuple								=							num_attention_heads
									lowerCAmelCase					:					List[Any]								=							intermediate_size
									lowerCAmelCase					:					Union[str, Any]								=							hidden_act
									lowerCAmelCase					:					List[str]								=							hidden_dropout_prob
									lowerCAmelCase					:					Union[str, Any]								=							attention_probs_dropout_prob
									lowerCAmelCase					:					int								=							max_position_embeddings
									lowerCAmelCase					:					int								=							type_vocab_size
									lowerCAmelCase					:					Any								=							type_sequence_label_size
									lowerCAmelCase					:					str								=							initializer_range
									lowerCAmelCase					:					Optional[Any]								=							num_labels
									lowerCAmelCase					:					List[str]								=							num_choices
									lowerCAmelCase					:					int								=							scope

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size						)

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

									lowerCAmelCase					:					int								=							None
									if self.use_token_type_ids:
											lowerCAmelCase					:					int								=							ids_tensor([self.batch_size, self.seq_length]		,					self.type_vocab_size						)

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

									lowerCAmelCase					:					Any								=							self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def 	lowercase__							(						self						):






									"""simple docstring"""





									return OpenLlamaConfig(
									    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=lowercase_		,					initializer_range=self.initializer_range		,					use_stable_embedding=lowercase_		,					)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							OpenLlamaModel(config=lowercase_						)
									model.to(lowercase_						)
									model.eval()
									lowerCAmelCase					:					Tuple								=							model(lowercase_		,					attention_mask=lowercase_						)
									lowerCAmelCase					:					int								=							model(lowercase_						)
									self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					):






									"""simple docstring"""





									lowerCAmelCase					:					List[Any]								=							True
									lowerCAmelCase					:					Optional[int]								=							OpenLlamaModel(lowercase_						)
									model.to(lowercase_						)
									model.eval()
									lowerCAmelCase					:					Optional[Any]								=							model(
									    lowercase_		,					attention_mask=lowercase_		,					encoder_hidden_states=lowercase_		,					encoder_attention_mask=lowercase_		,					)
									lowerCAmelCase					:					Optional[int]								=							model(
									    lowercase_		,					attention_mask=lowercase_		,					encoder_hidden_states=lowercase_		,					)
									lowerCAmelCase					:					Tuple								=							model(lowercase_		,					attention_mask=lowercase_						)
									self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					):






									"""simple docstring"""





									lowerCAmelCase					:					str								=							OpenLlamaForCausalLM(config=lowercase_						)
									model.to(lowercase_						)
									model.eval()
									lowerCAmelCase					:					List[Any]								=							model(lowercase_		,					attention_mask=lowercase_		,					labels=lowercase_						)
									self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					):






									"""simple docstring"""





									lowerCAmelCase					:					List[Any]								=							True
									lowerCAmelCase					:					Dict								=							True
									lowerCAmelCase					:					Optional[int]								=							OpenLlamaForCausalLM(config=lowercase_						)
									model.to(lowercase_						)
									model.eval()

									# first forward pass
									lowerCAmelCase					:					int								=							model(
									    lowercase_		,					attention_mask=lowercase_		,					encoder_hidden_states=lowercase_		,					encoder_attention_mask=lowercase_		,					use_cache=lowercase_		,					)
									lowerCAmelCase					:					int								=							outputs.past_key_values

									# create hypothetical multiple next token and extent to next_input_ids
									lowerCAmelCase					:					Dict								=							ids_tensor((self.batch_size, 3)		,					config.vocab_size						)
									lowerCAmelCase					:					Tuple								=							ids_tensor((self.batch_size, 3)		,					vocab_size=2						)

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

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

									# select random slice
									lowerCAmelCase					:					Tuple								=							ids_tensor((1,)		,					output_from_past.shape[-1]						).item()
									lowerCAmelCase					:					int								=							output_from_no_past[:, -3:, random_slice_idx].detach()
									lowerCAmelCase					:					Optional[int]								=							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(lowercase_		,					lowercase_		,					atol=1e-3						)						)







							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					List[Any]								=							self.prepare_config_and_inputs()
									(
									    lowerCAmelCase       
									)					:					Optional[Any]								=							config_and_inputs
									lowerCAmelCase					:					Optional[Any]								=							{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict


@require_torch
class 				SCREAMING_SNAKE_CASE__    (		lowercase__       ,   lowercase__       ,   lowercase__       ,   unittest.TestCase    ):
							"""simple docstring"""


							a  :    List[Any]       =(
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							a  :    Optional[int]       =(OpenLlamaForCausalLM,) if is_torch_available() else ()
							a  :    Any       =(
							    {
							        """feature-extraction""": OpenLlamaModel,
							        """text-classification""": OpenLlamaForSequenceClassification,
							        """text-generation""": OpenLlamaForCausalLM,
							        """zero-shot""": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							a  :    Tuple       =False
							a  :    List[Any]       =False

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					List[str]								=							OpenLlamaModelTester(self						)
									lowerCAmelCase					:					Union[str, Any]								=							ConfigTester(self		,					config_class=lowercase_		,					hidden_size=37						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									self.config_tester.run_common_tests()

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[Any]								=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Dict								=							self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											lowerCAmelCase					:					Optional[Any]								=							type
											self.model_tester.create_and_check_model(*lowercase_						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase					:					Optional[int]								=							3
									lowerCAmelCase					:					Optional[Any]								=							input_dict["input_ids"]
									lowerCAmelCase					:					Union[str, Any]								=							input_ids.ne(1						).to(lowercase_						)
									lowerCAmelCase					:					Union[str, Any]								=							ids_tensor([self.model_tester.batch_size]		,					self.model_tester.type_sequence_label_size						)
									lowerCAmelCase					:					str								=							OpenLlamaForSequenceClassification(lowercase_						)
									model.to(lowercase_						)
									model.eval()
									lowerCAmelCase					:					int								=							model(lowercase_		,					attention_mask=lowercase_		,					labels=lowercase_						)
									self.assertEqual(result.logits.shape		,					(self.model_tester.batch_size, self.model_tester.num_labels)						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					str								=							self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase					:					str								=							3
									lowerCAmelCase					:					str								=							"single_label_classification"
									lowerCAmelCase					:					Any								=							input_dict["input_ids"]
									lowerCAmelCase					:					int								=							input_ids.ne(1						).to(lowercase_						)
									lowerCAmelCase					:					Union[str, Any]								=							ids_tensor([self.model_tester.batch_size]		,					self.model_tester.type_sequence_label_size						)
									lowerCAmelCase					:					Optional[Any]								=							OpenLlamaForSequenceClassification(lowercase_						)
									model.to(lowercase_						)
									model.eval()
									lowerCAmelCase					:					List[Any]								=							model(lowercase_		,					attention_mask=lowercase_		,					labels=lowercase_						)
									self.assertEqual(result.logits.shape		,					(self.model_tester.batch_size, self.model_tester.num_labels)						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Any								=							self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase					:					int								=							3
									lowerCAmelCase					:					Union[str, Any]								=							"multi_label_classification"
									lowerCAmelCase					:					Dict								=							input_dict["input_ids"]
									lowerCAmelCase					:					Tuple								=							input_ids.ne(1						).to(lowercase_						)
									lowerCAmelCase					:					Tuple								=							ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]		,					self.model_tester.type_sequence_label_size						).to(torch.float						)
									lowerCAmelCase					:					List[str]								=							OpenLlamaForSequenceClassification(lowercase_						)
									model.to(lowercase_						)
									model.eval()
									lowerCAmelCase					:					List[Any]								=							model(lowercase_		,					attention_mask=lowercase_		,					labels=lowercase_						)
									self.assertEqual(result.logits.shape		,					(self.model_tester.batch_size, self.model_tester.num_labels)						)

							@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"						)
							def 	lowercase__							(						self						):






									"""simple docstring"""





									pass







							@parameterized.expand([("linear",), ("dynamic",)]						)
							def 	lowercase__							(						self		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[Any]								=							self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase					:					Dict								=							ids_tensor([1, 10]		,					config.vocab_size						)
									lowerCAmelCase					:					List[Any]								=							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
									lowerCAmelCase					:					Union[str, Any]								=							OpenLlamaModel(lowercase_						)
									original_model.to(lowercase_						)
									original_model.eval()
									lowerCAmelCase					:					Optional[int]								=							original_model(lowercase_						).last_hidden_state
									lowerCAmelCase					:					Optional[int]								=							original_model(lowercase_						).last_hidden_state

									set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
									lowerCAmelCase					:					Optional[int]								=							{"type": scaling_type, "factor": 10.0}
									lowerCAmelCase					:					List[Any]								=							OpenLlamaModel(lowercase_						)
									scaled_model.to(lowercase_						)
									scaled_model.eval()
									lowerCAmelCase					:					Tuple								=							scaled_model(lowercase_						).last_hidden_state
									lowerCAmelCase					:					Any								=							scaled_model(lowercase_						).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(lowercase_		,					lowercase_		,					atol=1e-5						)						)
									else:
											self.assertFalse(torch.allclose(lowercase_		,					lowercase_		,					atol=1e-5						)						)

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









"""simple docstring"""







def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							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]:
        lowerCAmelCase						,  lowerCAmelCase					:					Any								=							numbers[j], numbers[i]
  return numbers


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





