

"""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 					a						:



    """simple docstring"""



    def __init__(     self: int    ,						UpperCamelCase: str    ,						UpperCamelCase: List[str]=13    ,						UpperCamelCase: Optional[int]=7    ,						UpperCamelCase: Optional[int]=True    ,						UpperCamelCase: Optional[int]=True    ,						UpperCamelCase: Dict=False    ,						UpperCamelCase: Optional[Any]=True    ,						UpperCamelCase: Any=99    ,						UpperCamelCase: int=32    ,						UpperCamelCase: List[Any]=5    ,						UpperCamelCase: Optional[Any]=4    ,						UpperCamelCase: Dict=37    ,						UpperCamelCase: Tuple="gelu"    ,						UpperCamelCase: Optional[Any]=0.1    ,						UpperCamelCase: Any=0.1    ,						UpperCamelCase: Optional[Any]=5_12    ,						UpperCamelCase: str=16    ,						UpperCamelCase: Optional[int]=2    ,						UpperCamelCase: Optional[Any]=0.02    ,						UpperCamelCase: List[Any]=3    ,						UpperCamelCase: Any=4    ,						UpperCamelCase: int=None    ,						):






         """simple docstring"""


         A__										=  parent
         A__										=  batch_size
         A__										=  seq_length
         A__										=  is_training
         A__										=  use_input_mask
         A__										=  use_token_type_ids
         A__										=  use_labels
         A__										=  vocab_size
         A__										=  hidden_size
         A__										=  num_hidden_layers
         A__										=  num_attention_heads
         A__										=  intermediate_size
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  max_position_embeddings
         A__										=  type_vocab_size
         A__										=  type_sequence_label_size
         A__										=  initializer_range
         A__										=  num_labels
         A__										=  num_choices
         A__										=  scope

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size       )

         A__										=  None
         if self.use_input_mask:
              A__										=  random_attention_mask([self.batch_size, self.seq_length]       )

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

         A__										=  None
         A__										=  None
         A__										=  None
         if self.use_labels:
              A__										=  ids_tensor([self.batch_size]    ,						self.type_sequence_label_size       )
              A__										=  ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels       )
              A__										=  ids_tensor([self.batch_size]    ,						self.num_choices       )

         A__										=  self.get_config()

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

    def 				UpperCamelCase	(     self: Tuple       ):






         """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=UpperCamelCase    ,						initializer_range=self.initializer_range    ,						use_stable_embedding=UpperCamelCase    ,						)

    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: str    ,						UpperCamelCase: Dict    ,						UpperCamelCase: int    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: int    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         A__										=  OpenLlamaModel(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						attention_mask=UpperCamelCase       )
         A__										=  model(UpperCamelCase       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)       )

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: Dict    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: int    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict    ,						UpperCamelCase: Dict    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Optional[int]    ,						):






         """simple docstring"""


         A__										=  True
         A__										=  OpenLlamaModel(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(
             UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						)
         A__										=  model(
             UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						)
         A__										=  model(UpperCamelCase    ,						attention_mask=UpperCamelCase       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)       )

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: List[Any]    ,						):






         """simple docstring"""


         A__										=  OpenLlamaForCausalLM(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						labels=UpperCamelCase       )
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)       )

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: str    ,						):






         """simple docstring"""


         A__										=  True
         A__										=  True
         A__										=  OpenLlamaForCausalLM(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()

         # first forward pass
         A__										=  model(
             UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						use_cache=UpperCamelCase    ,						)
         A__										=  outputs.past_key_values

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

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

         A__										=  model(
             UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						output_hidden_states=UpperCamelCase    ,						)["""hidden_states"""][0]
         A__										=  model(
             UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						past_key_values=UpperCamelCase    ,						output_hidden_states=UpperCamelCase    ,						)["""hidden_states"""][0]

         # select random slice
         A__										=  ids_tensor((1,)    ,						output_from_past.shape[-1]       ).item()
         A__										=  output_from_no_past[:, -3:, random_slice_idx].detach()
         A__										=  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(UpperCamelCase    ,						UpperCamelCase    ,						atol=1e-3       )       )





    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  self.prepare_config_and_inputs()
         (
             (
             A__
         )			,				(
             A__
         )			,				(
             A__
         )			,				(
             A__
         )			,				(
             A__
         )			,				(
             A__
         )			,				(
             A__
         )			,				
         )										=  config_and_inputs
         A__										=  {"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict



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



    """simple docstring"""



    UpperCAmelCase						 =					(
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    UpperCAmelCase						 =					(OpenLlamaForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase						 =					(
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  OpenLlamaModelTester(self       )
         A__										=  ConfigTester(self    ,						config_class=UpperCamelCase    ,						hidden_size=37       )

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         self.config_tester.run_common_tests()

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase       )

    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              A__										=  type
              self.model_tester.create_and_check_model(*UpperCamelCase       )

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  3
         A__										=  input_dict["""input_ids"""]
         A__										=  input_ids.ne(1       ).to(UpperCamelCase       )
         A__										=  ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size       )
         A__										=  OpenLlamaForSequenceClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						labels=UpperCamelCase       )
         self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  3
         A__										=  """single_label_classification"""
         A__										=  input_dict["""input_ids"""]
         A__										=  input_ids.ne(1       ).to(UpperCamelCase       )
         A__										=  ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size       )
         A__										=  OpenLlamaForSequenceClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						labels=UpperCamelCase       )
         self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)       )

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  3
         A__										=  """multi_label_classification"""
         A__										=  input_dict["""input_ids"""]
         A__										=  input_ids.ne(1       ).to(UpperCamelCase       )
         A__										=  ids_tensor(
             [self.model_tester.batch_size, config.num_labels]    ,						self.model_tester.type_sequence_label_size       ).to(torch.float       )
         A__										=  OpenLlamaForSequenceClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						labels=UpperCamelCase       )
         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 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         pass





    @parameterized.expand([("""linear""",), ("""dynamic""",)]       )
    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  ids_tensor([1, 10]    ,						config.vocab_size       )
         A__										=  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__										=  OpenLlamaModel(UpperCamelCase       )
         original_model.to(UpperCamelCase       )
         original_model.eval()
         A__										=  original_model(UpperCamelCase       ).last_hidden_state
         A__										=  original_model(UpperCamelCase       ).last_hidden_state

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

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









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					list			):
     if len(UpperCAmelCase_			) <= 1:
          return lst

     A__										=  1

     while i < len(UpperCAmelCase_			):
          if lst[i - 1] <= lst[i]:
               i += 1
          else:
               A__			,				A__										=  lst[i], lst[i - 1]
               i -= 1
               if i == 0:
                    A__										=  1

     return lst


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






