


'''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 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=13      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase=99      ,      __lowercase=32      ,      __lowercase=5      ,      __lowercase=4      ,      __lowercase=37      ,      __lowercase="gelu"      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=16      ,      __lowercase=2      ,      __lowercase=0.0_2      ,      __lowercase=3      ,      __lowercase=4      ,      __lowercase=None      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size						)

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

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

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

  __A  :		List[Any]       =			self.get_config()

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


 def  snake_case__   (      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  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			OpenLlamaModel(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase						)
  __A  :		Dict       =			model(__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		Dict       =			OpenLlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[Any]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







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

  # first forward pass
  __A  :		List[str]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      use_cache=__lowercase      ,      )
  __A  :		Optional[Any]       =			outputs.past_key_values

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

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

  __A  :		Tuple       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]
  __A  :		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
  __A  :		Any       =			ids_tensor((1,)      ,      output_from_past.shape[-1]						).item()
  __A  :		Union[str, Any]       =			output_from_no_past[:, -3:, random_slice_idx].detach()
  __A  :		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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.prepare_config_and_inputs()
  (
      (
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,
  )  :		Optional[Any]       =			config_and_inputs
  __A  :		Optional[int]       =			{'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict


@require_torch
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		List[str]    							=						(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 __lowercase				:		List[Any]    							=						(OpenLlamaForCausalLM,) if is_torch_available() else ()
 __lowercase				:		Optional[int]    							=						(
     {
         '''feature-extraction''': OpenLlamaModel,
         '''text-classification''': OpenLlamaForSequenceClassification,
         '''text-generation''': OpenLlamaForCausalLM,
         '''zero-shot''': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase				:		Union[str, Any]    							=						False
 __lowercase				:		Union[str, Any]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			OpenLlamaModelTester(self						)
  __A  :		List[str]       =			ConfigTester(self      ,      config_class=__lowercase      ,      hidden_size=37						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   __A  :		List[str]       =			type
   self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		str       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		Any       =			3
  __A  :		List[str]       =			'single_label_classification'
  __A  :		Dict       =			input_dict['input_ids']
  __A  :		Dict       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		List[str]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		Union[str, Any]       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Dict       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		str       =			3
  __A  :		int       =			'multi_label_classification'
  __A  :		Union[str, Any]       =			input_dict['input_ids']
  __A  :		str       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Union[str, Any]       =			ids_tensor(
      [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size						).to(torch.float						)
  __A  :		int       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		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  snake_case__   (      self						):

  """simple docstring"""







  pass







 @parameterized.expand([('linear',), ('dynamic',)]						)
 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







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

  set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
  __A  :		List[Any]       =			{'type': scaling_type, 'factor': 1_0.0}
  __A  :		str       =			OpenLlamaModel(__lowercase						)
  scaled_model.to(__lowercase						)
  scaled_model.eval()
  __A  :		Dict       =			scaled_model(__lowercase						).last_hidden_state
  __A  :		List[str]       =			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'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

 # iteration for two-way merging
 __A  :		Dict       =			2
 while p <= len(UpperCamelCase__			):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0,				len(UpperCamelCase__			),				UpperCamelCase__			):
   __A  :		Union[str, Any]       =			i
   __A  :		Tuple       =			i + p - 1
   __A  :		Union[str, Any]       =			(low + high + 1) // 2
   __A  :		Tuple       =			merge(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase__			):
   __A  :		Optional[int]       =			i
   __A  :		str       =			merge(UpperCamelCase__,				0,				UpperCamelCase__,				len(UpperCamelCase__			) - 1			)
   break
  p *= 2

 return input_list


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






