



"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, 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 (
					    GPTNeoXForCausalLM,
					    GPTNeoXForQuestionAnswering,
					    GPTNeoXForSequenceClassification,
					    GPTNeoXForTokenClassification,
					    GPTNeoXModel,
					)
class 	_a       :
			"""simple docstring"""



			def __init__(						self   :			Union[str, Any] ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			List[str]=1_3 ,      __UpperCamelCase   :			Optional[int]=7 ,      __UpperCamelCase   :			Any=True ,      __UpperCamelCase   :			int=True ,      __UpperCamelCase   :			Union[str, Any]=True ,      __UpperCamelCase   :			List[Any]=True ,      __UpperCamelCase   :			Tuple=9_9 ,      __UpperCamelCase   :			str=6_4 ,      __UpperCamelCase   :			Union[str, Any]=5 ,      __UpperCamelCase   :			Tuple=4 ,      __UpperCamelCase   :			Union[str, Any]=3_7 ,      __UpperCamelCase   :			Optional[Any]="gelu" ,      __UpperCamelCase   :			Optional[Any]=0.1 ,      __UpperCamelCase   :			List[Any]=0.1 ,      __UpperCamelCase   :			Union[str, Any]=5_1_2 ,      __UpperCamelCase   :			Optional[int]=1_6 ,      __UpperCamelCase   :			List[str]=2 ,      __UpperCamelCase   :			Union[str, Any]=0.0_2 ,      __UpperCamelCase   :			Tuple=3 ,      __UpperCamelCase   :			Dict=4 ,      __UpperCamelCase   :			List[Any]=None ,      )->List[str]:
							_UpperCAmelCase							=			parent
							_UpperCAmelCase							=			batch_size
							_UpperCAmelCase							=			seq_length
							_UpperCAmelCase							=			is_training
							_UpperCAmelCase							=			use_input_mask
							_UpperCAmelCase							=			use_token_type_ids
							_UpperCAmelCase							=			use_labels
							_UpperCAmelCase							=			vocab_size
							_UpperCAmelCase							=			hidden_size
							_UpperCAmelCase							=			num_hidden_layers
							_UpperCAmelCase							=			num_attention_heads
							_UpperCAmelCase							=			intermediate_size
							_UpperCAmelCase							=			hidden_act
							_UpperCAmelCase							=			hidden_dropout_prob
							_UpperCAmelCase							=			attention_probs_dropout_prob
							_UpperCAmelCase							=			max_position_embeddings
							_UpperCAmelCase							=			type_vocab_size
							_UpperCAmelCase							=			type_sequence_label_size
							_UpperCAmelCase							=			initializer_range
							_UpperCAmelCase							=			num_labels
							_UpperCAmelCase							=			num_choices
							_UpperCAmelCase							=			scope
							_UpperCAmelCase							=			vocab_size - 1



			def        lowercase__  (						self   :			Dict      )->str:
							_UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length] ,      self.vocab_size      )

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

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

							_UpperCAmelCase							=			self.get_config()

							return config, input_ids, input_mask, token_labels



			def        lowercase__  (						self   :			Dict      )->Dict:
							return GPTNeoXConfig(
							    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 ,      pad_token_id=self.pad_token_id ,      )



			def        lowercase__  (						self   :			Optional[int]      )->Union[str, Any]:
							_UpperCAmelCase							=			self.prepare_config_and_inputs()

							_UpperCAmelCase							=			True

							return config, input_ids, input_mask, token_labels



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			Any ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			Tuple      )->str:
							_UpperCAmelCase							=			GPTNeoXModel(config=__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase      )
							_UpperCAmelCase							=			model(__UpperCamelCase      )
							self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Dict      )->Any:
							_UpperCAmelCase							=			True
							_UpperCAmelCase							=			GPTNeoXModel(__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase      )
							self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			Tuple      )->Optional[int]:
							_UpperCAmelCase							=			GPTNeoXForCausalLM(config=__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase ,      labels=__UpperCamelCase      )
							self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.seq_length, self.vocab_size)      )



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			int ,      __UpperCamelCase   :			int ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			int      )->Union[str, Any]:
							_UpperCAmelCase							=			self.num_labels
							_UpperCAmelCase							=			GPTNeoXForQuestionAnswering(__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase      )
							self.parent.assertEqual(result.start_logits.shape ,      (self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape ,      (self.batch_size, self.seq_length)      )



			def        lowercase__  (						self   :			int ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Any ,      __UpperCamelCase   :			List[Any]      )->str:
							_UpperCAmelCase							=			self.num_labels
							_UpperCAmelCase							=			GPTNeoXForSequenceClassification(__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()
							_UpperCAmelCase							=			ids_tensor([self.batch_size] ,      self.type_sequence_label_size      )
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase ,      labels=__UpperCamelCase      )
							self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.num_labels)      )



			def        lowercase__  (						self   :			Optional[Any] ,      __UpperCamelCase   :			int ,      __UpperCamelCase   :			Dict ,      __UpperCamelCase   :			int ,      __UpperCamelCase   :			Tuple      )->Union[str, Any]:
							_UpperCAmelCase							=			self.num_labels
							_UpperCAmelCase							=			GPTNeoXForTokenClassification(__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase ,      labels=__UpperCamelCase      )
							self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.seq_length, self.num_labels)      )



			def        lowercase__  (						self   :			int ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			List[str] ,      __UpperCamelCase   :			List[Any]      )->Optional[int]:
							_UpperCAmelCase							=			True
							_UpperCAmelCase							=			GPTNeoXForCausalLM(config=__UpperCamelCase      )
							model.to(__UpperCamelCase      )
							model.eval()

							# first forward pass
							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase ,      use_cache=__UpperCamelCase      )
							_UpperCAmelCase							=			outputs.past_key_values

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

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

							_UpperCAmelCase							=			model(__UpperCamelCase ,      attention_mask=__UpperCamelCase ,      output_hidden_states=__UpperCamelCase      )
							_UpperCAmelCase							=			output_from_no_past['hidden_states'][0]
							_UpperCAmelCase							=			model(
							    __UpperCamelCase ,      attention_mask=__UpperCamelCase ,      past_key_values=__UpperCamelCase ,      output_hidden_states=__UpperCamelCase ,      )['hidden_states'][0]

							# select random slice
							_UpperCAmelCase							=			ids_tensor((1,) ,      output_from_past.shape[-1]      ).item()
							_UpperCAmelCase							=			output_from_no_past[:, -3:, random_slice_idx].detach()
							_UpperCAmelCase							=			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        lowercase__  (						self   :			Optional[int]      )->Dict:
							_UpperCAmelCase							=			self.prepare_config_and_inputs()
							_UpperCAmelCase							=			config_and_inputs
							_UpperCAmelCase							=			{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict



@require_torch
class 	_a       (    lowercase_	,							lowercase_	,							lowercase_	,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__						=		(
			    (
			        GPTNeoXModel,
			        GPTNeoXForCausalLM,
			        GPTNeoXForQuestionAnswering,
			        GPTNeoXForSequenceClassification,
			        GPTNeoXForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__						=		(GPTNeoXForCausalLM,) if is_torch_available() else ()
			UpperCamelCase__						=		(
			    {
			        """feature-extraction""": GPTNeoXModel,
			        """question-answering""": GPTNeoXForQuestionAnswering,
			        """text-classification""": GPTNeoXForSequenceClassification,
			        """text-generation""": GPTNeoXForCausalLM,
			        """token-classification""": GPTNeoXForTokenClassification,
			        """zero-shot""": GPTNeoXForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__						=		False
			UpperCamelCase__						=		False
			UpperCamelCase__						=		False
			UpperCamelCase__						=		False



			def        lowercase__  (						self   :			Union[str, Any]      )->Union[str, Any]:
							_UpperCAmelCase							=			GPTNeoXModelTester(self      )
							_UpperCAmelCase							=			ConfigTester(self ,      config_class=__UpperCamelCase ,      hidden_size=6_4 ,      num_attention_heads=8      )



			def        lowercase__  (						self   :			List[Any]      )->Tuple:
							self.config_tester.run_common_tests()



			def        lowercase__  (						self   :			Dict      )->Optional[Any]:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase      )



			def        lowercase__  (						self   :			Optional[int]      )->List[Any]:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase      )



			def        lowercase__  (						self   :			str      )->str:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_decoder()

							_UpperCAmelCase							=			None

							self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase      )



			def        lowercase__  (						self   :			Optional[Any]      )->Optional[Any]:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase      )



			def        lowercase__  (						self   :			List[Any]      )->str:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*__UpperCamelCase      )



			def        lowercase__  (						self   :			Optional[Any]      )->Optional[int]:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase      )



			def        lowercase__  (						self   :			str      )->str:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase      )



			def        lowercase__  (						self   :			Optional[Any]      )->str:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase      )



			@unittest.skip(reason='''Feed forward chunking is not implemented'''      )
			def        lowercase__  (						self   :			Optional[Any]      )->List[Any]:
							pass



			@parameterized.expand([('''linear''',), ('''dynamic''',)]      )
			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			Optional[int]      )->int:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase							=			ids_tensor([1, 1_0] ,      config.vocab_size      )
							_UpperCAmelCase							=			ids_tensor([1, int(config.max_position_embeddings * 1.5      )] ,      config.vocab_size      )

							set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
							_UpperCAmelCase							=			GPTNeoXModel(__UpperCamelCase      )
							original_model.to(__UpperCamelCase      )
							original_model.eval()
							_UpperCAmelCase							=			original_model(__UpperCamelCase      ).last_hidden_state
							_UpperCAmelCase							=			original_model(__UpperCamelCase      ).last_hidden_state

							set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
							_UpperCAmelCase							=			{'type': scaling_type, 'factor': 1_0.0}
							_UpperCAmelCase							=			GPTNeoXModel(__UpperCamelCase      )
							scaled_model.to(__UpperCamelCase      )
							scaled_model.eval()
							_UpperCAmelCase							=			scaled_model(__UpperCamelCase      ).last_hidden_state
							_UpperCAmelCase							=			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      )      )



@require_torch
class 	_a       (    unittest.TestCase):
			"""simple docstring"""



			@slow
			def        lowercase__  (						self   :			Any      )->Union[str, Any]:
							_UpperCAmelCase							=			AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''      )
							for checkpointing in [True, False]:
											_UpperCAmelCase							=			GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''      )

											if checkpointing:
															model.gradient_checkpointing_enable()
											else:
															model.gradient_checkpointing_disable()
											model.to(__UpperCamelCase      )

											_UpperCAmelCase							=			tokenizer('''My favorite food is''' ,      return_tensors='''pt'''      ).to(__UpperCamelCase      )
											# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
											# See: https://github.com/huggingface/transformers/pull/24193
											_UpperCAmelCase							=			'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

											_UpperCAmelCase							=			model.generate(**__UpperCamelCase ,      do_sample=__UpperCamelCase ,      max_new_tokens=2_0      )
											_UpperCAmelCase							=			tokenizer.batch_decode(__UpperCamelCase      )[0]

											self.assertEqual(__UpperCamelCase ,      __UpperCamelCase      )




"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):

     if len(__SCREAMING_SNAKE_CASE      ) < 2:
          return collection

     def circle_sort_util(__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ) -> bool:
          lowercase_	:				Any													=	False

          if low == high:
               return swapped

          lowercase_	:				str													=	low
          lowercase_	:				int													=	high

          while left < right:
               if collection[left] > collection[right]:
                    lowercase_							,  lowercase_	:				Optional[Any]													=	(
                        collection[right],
                        collection[left],
                    )
                    lowercase_	:				Tuple													=	True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               lowercase_							,  lowercase_	:				Dict													=	(
                   collection[right + 1],
                   collection[left],
               )

               lowercase_	:				str													=	True

          lowercase_	:				Optional[Any]													=	low + int((high - low) / 2      )
          lowercase_	:				str													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			mid + 1     ,			__SCREAMING_SNAKE_CASE      )

          return swapped or left_swap or right_swap

     lowercase_	:				Dict													=	True

     while is_not_sorted is True:
          lowercase_	:				Optional[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      )

     return collection


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

