






"""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_							:







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Tuple=1_3						,    __lowerCamelCase							:						Optional[Any]=7						,    __lowerCamelCase							:						int=True						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Union[str, Any]=True						,    __lowerCamelCase							:						int=True						,    __lowerCamelCase							:						Union[str, Any]=9_9						,    __lowerCamelCase							:						List[str]=6_4						,    __lowerCamelCase							:						Optional[Any]=5						,    __lowerCamelCase							:						str=4						,    __lowerCamelCase							:						List[str]=3_7						,    __lowerCamelCase							:						Optional[int]="gelu"						,    __lowerCamelCase							:						Dict=0.1						,    __lowerCamelCase							:						Dict=0.1						,    __lowerCamelCase							:						Tuple=5_1_2						,    __lowerCamelCase							:						Union[str, Any]=1_6						,    __lowerCamelCase							:						str=2						,    __lowerCamelCase							:						int=0.02						,    __lowerCamelCase							:						Optional[int]=3						,    __lowerCamelCase							:						List[str]=4						,    __lowerCamelCase							:						Any=None						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_input_mask
										__magic_name__            =     use_token_type_ids
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     type_vocab_size
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     num_labels
										__magic_name__            =     num_choices
										__magic_name__            =     scope
										__magic_name__            =     vocab_size - 1







				def     _snake_case					(							self							:						List[str]	)				->					List[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size	)

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

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

										__magic_name__            =     self.get_config()

										return config, input_ids, input_mask, token_labels







				def     _snake_case					(							self							:						Optional[int]	)				->					Union[str, Any]:
										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=__lowerCamelCase						,    initializer_range=self.initializer_range						,    pad_token_id=self.pad_token_id						,    )







				def     _snake_case					(							self							:						Any	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     self.prepare_config_and_inputs()

										__magic_name__            =     True

										return config, input_ids, input_mask, token_labels







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Any	)				->					Dict:
										__magic_name__            =     GPTNeoXModel(config=__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase	)
										self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple	)				->					Any:
										__magic_name__            =     True
										__magic_name__            =     GPTNeoXModel(__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)
										self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)	)







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str	)				->					Tuple:
										__magic_name__            =     GPTNeoXForCausalLM(config=__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    labels=__lowerCamelCase	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)	)







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict	)				->					Any:
										__magic_name__            =     self.num_labels
										__magic_name__            =     GPTNeoXForQuestionAnswering(__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)
										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     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Any	)				->					Tuple:
										__magic_name__            =     self.num_labels
										__magic_name__            =     GPTNeoXForSequenceClassification(__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     ids_tensor([self.batch_size]						,    self.type_sequence_label_size	)
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    labels=__lowerCamelCase	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)	)







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     self.num_labels
										__magic_name__            =     GPTNeoXForTokenClassification(__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    labels=__lowerCamelCase	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)	)







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Optional[Any]	)				->					Union[str, Any]:
										__magic_name__            =     True
										__magic_name__            =     GPTNeoXForCausalLM(config=__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()

										# first forward pass
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    use_cache=__lowerCamelCase	)
										__magic_name__            =     outputs.past_key_values

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

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

										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    output_hidden_states=__lowerCamelCase	)
										__magic_name__            =     output_from_no_past["hidden_states"][0]
										__magic_name__            =     model(
										    __lowerCamelCase						,    attention_mask=__lowerCamelCase						,    past_key_values=__lowerCamelCase						,    output_hidden_states=__lowerCamelCase						,    )["hidden_states"][0]

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







				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										__magic_name__            =     self.prepare_config_and_inputs()
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     config_and_inputs
										__magic_name__            =     {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict





@require_torch
class 						A_							(      snake_case_					,							snake_case_					,							snake_case_					,							unittest.TestCase  ):
				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     _snake_case					(							self							:						List[Any]	)				->					Optional[int]:
										__magic_name__            =     GPTNeoXModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase						,    hidden_size=6_4						,    num_attention_heads=8	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Any	)				->					List[Any]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[int]:
										# This regression test was failing with PyTorch < 1.3
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_decoder()

										__magic_name__            =     None

										self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					str:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					Any:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase	)







				@unittest.skip(reason="Feed forward chunking is not implemented"	)
				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										pass







				@parameterized.expand([("linear",), ("dynamic",)]	)
				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Any	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     ids_tensor([1, 1_0]						,    config.vocab_size	)
										__magic_name__            =     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
										__magic_name__            =     GPTNeoXModel(__lowerCamelCase	)
										original_model.to(__lowerCamelCase	)
										original_model.eval()
										__magic_name__            =     original_model(__lowerCamelCase	).last_hidden_state
										__magic_name__            =     original_model(__lowerCamelCase	).last_hidden_state

										set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
										__magic_name__            =     {"type": scaling_type, "factor": 10.0}
										__magic_name__            =     GPTNeoXModel(__lowerCamelCase	)
										scaled_model.to(__lowerCamelCase	)
										scaled_model.eval()
										__magic_name__            =     scaled_model(__lowerCamelCase	).last_hidden_state
										__magic_name__            =     scaled_model(__lowerCamelCase	).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(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-5	)	)
										else:
																self.assertFalse(torch.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-5	)	)

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





@require_torch
class 						A_							(      unittest.TestCase  ):







				@slow
				def     _snake_case					(							self							:						Tuple	)				->					Optional[Any]:
										__magic_name__            =     AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"	)
										for checkpointing in [True, False]:
																__magic_name__            =     GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"	)

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

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

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

																self.assertEqual(__lowerCamelCase						,    __lowerCamelCase	)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     n ** (1 / 3)
						return (val * val * val) == n


if __name__ == "__main__":
			print(perfect_cube(27))
			print(perfect_cube(4))



