


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 				snake_case__:







						'''simple docstring'''




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




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      List[Any]    				=				ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size							)

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

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

										lowerCAmelCase_ :      str    				=				self.get_config()

										return config, input_ids, input_mask, token_labels




						def 						lowercase_ (				self							)					->    Optional[int]:
										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=__lowercase   ,						initializer_range=self.initializer_range   ,						pad_token_id=self.pad_token_id   ,						)




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[Any]    				=				self.prepare_config_and_inputs()

										lowerCAmelCase_ :      str    				=				True

										return config, input_ids, input_mask, token_labels




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      str    				=				GPTNeoXModel(config=__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      List[Any]    				=				model(__lowercase   ,						attention_mask=__lowercase							)
										lowerCAmelCase_ :      Union[str, Any]    				=				model(__lowercase							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    int:
										lowerCAmelCase_ :      Optional[int]    				=				True
										lowerCAmelCase_ :      List[Any]    				=				GPTNeoXModel(__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      int    				=				model(__lowercase   ,						attention_mask=__lowercase							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Tuple:
										lowerCAmelCase_ :      Any    				=				GPTNeoXForCausalLM(config=__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      Dict    				=				model(__lowercase   ,						attention_mask=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Any:
										lowerCAmelCase_ :      Optional[int]    				=				self.num_labels
										lowerCAmelCase_ :      Union[str, Any]    				=				GPTNeoXForQuestionAnswering(__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      Optional[Any]    				=				model(__lowercase   ,						attention_mask=__lowercase							)
										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   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      Any    				=				self.num_labels
										lowerCAmelCase_ :      Union[str, Any]    				=				GPTNeoXForSequenceClassification(__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      Optional[int]    				=				ids_tensor([self.batch_size]   ,						self.type_sequence_label_size							)
										lowerCAmelCase_ :      Any    				=				model(__lowercase   ,						attention_mask=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Union[str, Any]    				=				self.num_labels
										lowerCAmelCase_ :      Union[str, Any]    				=				GPTNeoXForTokenClassification(__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      Optional[Any]    				=				model(__lowercase   ,						attention_mask=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[Any]:
										lowerCAmelCase_ :      Any    				=				True
										lowerCAmelCase_ :      List[Any]    				=				GPTNeoXForCausalLM(config=__lowercase							)
										model.to(__lowercase							)
										model.eval()

										# first forward pass
										lowerCAmelCase_ :      Dict    				=				model(__lowercase   ,						attention_mask=__lowercase   ,						use_cache=__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				outputs.past_key_values

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

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

										lowerCAmelCase_ :      Dict    				=				model(__lowercase   ,						attention_mask=__lowercase   ,						output_hidden_states=__lowercase							)
										lowerCAmelCase_ :      Any    				=				output_from_no_past['''hidden_states'''][0]
										lowerCAmelCase_ :      Union[str, Any]    				=				model(
										    __lowercase   ,						attention_mask=__lowercase   ,						past_key_values=__lowercase   ,						output_hidden_states=__lowercase   ,						)['''hidden_states'''][0]

										# select random slice
										lowerCAmelCase_ :      int    				=				ids_tensor((1,)   ,						output_from_past.shape[-1]							).item()
										lowerCAmelCase_ :      Any    				=				output_from_no_past[:, -3:, random_slice_idx].detach()
										lowerCAmelCase_ :      Tuple    				=				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 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Optional[Any]    				=				self.prepare_config_and_inputs()
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Union[str, Any]    				=				config_and_inputs
										lowerCAmelCase_ :      List[Any]    				=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict







@require_torch
class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Tuple					=							(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE__      :  str					=							(GPTNeoXForCausalLM,) if is_torch_available() else ()
						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							(
						    {
						        """feature-extraction""": GPTNeoXModel,
						        """question-answering""": GPTNeoXForQuestionAnswering,
						        """text-classification""": GPTNeoXForSequenceClassification,
						        """text-generation""": GPTNeoXForCausalLM,
						        """token-classification""": GPTNeoXForTokenClassification,
						        """zero-shot""": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE__      :  Dict					=							False
						SCREAMING_SNAKE_CASE__      :  List[str]					=							False
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							False
						SCREAMING_SNAKE_CASE__      :  List[str]					=							False




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Any    				=				GPTNeoXModelTester(self							)
										lowerCAmelCase_ :      Optional[int]    				=				ConfigTester(self   ,						config_class=__lowercase   ,						hidden_size=6_4   ,						num_attention_heads=8							)




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




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Any    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(__lowercase   ,						__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Dict    				=				self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(__lowercase   ,						__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self							)					->    Dict:
										# This regression test was failing with PyTorch < 1.3
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[int]    				=				self.model_tester.prepare_config_and_inputs_for_decoder()

										lowerCAmelCase_ :      Dict    				=				None

										self.model_tester.create_and_check_model_as_decoder(__lowercase   ,						__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowercase   ,						__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      List[str]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      int    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Tuple    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__lowercase							)




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__lowercase							)




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




						@parameterized.expand([('''linear''',), ('''dynamic''',)]							)
						def 						lowercase_ (				self   ,						__lowercase							)					->    Tuple:
										lowerCAmelCase_				,  lowerCAmelCase_ :      str    				=				self.model_tester.prepare_config_and_inputs_for_common()
										lowerCAmelCase_ :      Optional[Any]    				=				ids_tensor([1, 1_0]   ,						config.vocab_size							)
										lowerCAmelCase_ :      Union[str, Any]    				=				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
										lowerCAmelCase_ :      int    				=				GPTNeoXModel(__lowercase							)
										original_model.to(__lowercase							)
										original_model.eval()
										lowerCAmelCase_ :      List[Any]    				=				original_model(__lowercase							).last_hidden_state
										lowerCAmelCase_ :      Tuple    				=				original_model(__lowercase							).last_hidden_state

										set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
										lowerCAmelCase_ :      List[str]    				=				{'''type''': scaling_type, '''factor''': 10.0}
										lowerCAmelCase_ :      Optional[Any]    				=				GPTNeoXModel(__lowercase							)
										scaled_model.to(__lowercase							)
										scaled_model.eval()
										lowerCAmelCase_ :      List[Any]    				=				scaled_model(__lowercase							).last_hidden_state
										lowerCAmelCase_ :      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							)							)







@require_torch
class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@slow
						def 						lowercase_ (				self							)					->    Optional[int]:
										lowerCAmelCase_ :      Dict    				=				AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''							)
										for checkpointing in [True, False]:
														lowerCAmelCase_ :      Tuple    				=				GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''							)

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

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

														lowerCAmelCase_ :      Optional[Any]    				=				model.generate(**__lowercase   ,						do_sample=__lowercase   ,						max_new_tokens=2_0							)
														lowerCAmelCase_ :      List[str]    				=				tokenizer.batch_decode(__lowercase							)[0]

														self.assertEqual(__lowercase   ,						__lowercase							)



# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    Any:
										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
														return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
														if self.gradient_state.adjust_scheduler:
																		self.scheduler._step_count += 1
														return

										for opt in self.optimizers:
														if opt.step_was_skipped:
																		return
										if self.split_batches:
														# Split batches -> the training dataloader batch size is not changed so one step per training step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														lowerCAmelCase_ :      Optional[Any]    				=				AcceleratorState().num_processes
														for _ in range(__lowercase							):
																		# Special case when using OneCycle and `drop_last` was not used
																		if hasattr(self.scheduler   ,						'''total_steps'''							):
																						if self.scheduler._step_count <= self.scheduler.total_steps:
																										self.scheduler.step(*__lowercase   ,						**__lowercase							)
																		else:
																						self.scheduler.step(*__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)