


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 :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=13     ,       _SCREAMING_SNAKE_CASE=7     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=99     ,       _SCREAMING_SNAKE_CASE=64     ,       _SCREAMING_SNAKE_CASE=5     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=37     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=None     ,       )					->			Optional[int]:




									"""simple docstring"""





									_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       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_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       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									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=_SCREAMING_SNAKE_CASE     ,       initializer_range=self.initializer_range     ,       pad_token_id=self.pad_token_id     ,       )
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     self.prepare_config_and_inputs()

									_UpperCAmelCase											=     True

									return config, input_ids, input_mask, token_labels
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     GPTNeoXModel(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     True
									_UpperCAmelCase											=     GPTNeoXModel(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     GPTNeoXForCausalLM(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     GPTNeoXForQuestionAnswering(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE		)
									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       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     GPTNeoXForSequenceClassification(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     ids_tensor([self.batch_size]     ,       self.type_sequence_label_size		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     GPTNeoXForTokenClassification(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     True
									_UpperCAmelCase											=     GPTNeoXForCausalLM(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()

									# first forward pass
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       use_cache=_SCREAMING_SNAKE_CASE		)
									_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(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       output_hidden_states=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     output_from_no_past['hidden_states'][0]
									_UpperCAmelCase											=     model(
									    _SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       past_key_values=_SCREAMING_SNAKE_CASE     ,       output_hidden_states=_SCREAMING_SNAKE_CASE     ,       )['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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       atol=1e-3		)		)






		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





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



@require_torch
class 						__a (							UpperCAmelCase       , UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase     ):
		_a      :    Tuple           =      (
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_a      :    Any           =      (GPTNeoXForCausalLM,) if is_torch_available() else ()
		_a      :    Any           =      (
		    {
		        "feature-extraction": GPTNeoXModel,
		        "question-answering": GPTNeoXForQuestionAnswering,
		        "text-classification": GPTNeoXForSequenceClassification,
		        "text-generation": GPTNeoXForCausalLM,
		        "token-classification": GPTNeoXForTokenClassification,
		        "zero-shot": GPTNeoXForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_a      :    Optional[int]           =      False
		_a      :    Tuple           =      False
		_a      :    Optional[Any]           =      False
		_a      :    List[str]           =      False
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     GPTNeoXModelTester(self		)
									_UpperCAmelCase											=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       hidden_size=64     ,       num_attention_heads=8		)
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_decoder()

									_UpperCAmelCase											=     None

									self.model_tester.create_and_check_model_as_decoder(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_causal_lm(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE		)
		@unittest.skip(reason='Feed forward chunking is not implemented'		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									pass






		@parameterized.expand([('linear',), ('dynamic',)]		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase											=     ids_tensor([1, 10]     ,       config.vocab_size		)
									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     GPTNeoXModel(_SCREAMING_SNAKE_CASE		)
									original_model.to(_SCREAMING_SNAKE_CASE		)
									original_model.eval()
									_UpperCAmelCase											=     original_model(_SCREAMING_SNAKE_CASE		).last_hidden_state
									_UpperCAmelCase											=     original_model(_SCREAMING_SNAKE_CASE		).last_hidden_state

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

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



@require_torch
class 						__a (							unittest.TestCase     ):
		@slow
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_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(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase											=     tokenizer('My favorite food is'     ,       return_tensors='pt'		).to(_SCREAMING_SNAKE_CASE		)
																# 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(**_SCREAMING_SNAKE_CASE     ,       do_sample=_SCREAMING_SNAKE_CASE     ,       max_new_tokens=20		)
																_UpperCAmelCase											=     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)[0]

																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)





def 	_lowerCAmelCase (    A__ = 600_851_475_143						):

      try:
            lowercase__  =	int(A__						)
      except (TypeError, ValueError):
            raise TypeError('Parameter n must be int or castable to int.'						)
      if n <= 0:
            raise ValueError('Parameter n must be greater than or equal to one.'						)
      lowercase__  =	2
      lowercase__  =	0
      if n == 2:
            return 2
      while n > 2:
            while n % i != 0:
                  i += 1
            lowercase__  =	i
            while n % i == 0:
                  lowercase__  =	n // i
            i += 1
      return int(A__						)


if __name__ == "__main__":
       print(F'''{solution() = }''')
