



'''simple docstring'''




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							random.seed(_lowercase		)
							np.random.seed(_lowercase		)
							torch.manual_seed(_lowercase		)
							torch.cuda.manual_seed_all(_lowercase		)
							# ^^ safe to call this function even if cuda is not available


class 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A = 0.9_9_9_9		,		A = 0.0		,		A = 0		,		A = False		,		A = 1.0		,		A = 2 / 3		,		A = None		,		A = None		,		**A		,		)			->			Optional[Any]:

								if isinstance(A		,		torch.nn.Module    ):
															UpperCAmelCase     :		Union[str, Any]							     =		(
															    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
															    """Please pass the parameters of the module instead."""
															)
															deprecate(
															    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""		,		"""1.0.0"""		,		A		,		standard_warn=A		,		)
															UpperCAmelCase     :		Union[str, Any]							     =		parameters.parameters()

															# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
															UpperCAmelCase     :		Union[str, Any]							     =		True

								if kwargs.get("""max_value"""		,		A    ) is not None:
															UpperCAmelCase     :		List[str]							     =		"""The `max_value` argument is deprecated. Please use `decay` instead."""
															deprecate("""max_value"""		,		"""1.0.0"""		,		A		,		standard_warn=A    )
															UpperCAmelCase     :		Optional[Any]							     =		kwargs["""max_value"""]

								if kwargs.get("""min_value"""		,		A    ) is not None:
															UpperCAmelCase     :		Tuple							     =		"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
															deprecate("""min_value"""		,		"""1.0.0"""		,		A		,		standard_warn=A    )
															UpperCAmelCase     :		Union[str, Any]							     =		kwargs["""min_value"""]

								UpperCAmelCase     :		List[str]							     =		list(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		[p.clone().detach() for p in parameters]

								if kwargs.get("""device"""		,		A    ) is not None:
															UpperCAmelCase     :		Tuple							     =		"""The `device` argument is deprecated. Please use `to` instead."""
															deprecate("""device"""		,		"""1.0.0"""		,		A		,		standard_warn=A    )
															self.to(device=kwargs["""device"""]    )

								UpperCAmelCase     :		Union[str, Any]							     =		None

								UpperCAmelCase     :		Tuple							     =		decay
								UpperCAmelCase     :		str							     =		min_decay
								UpperCAmelCase     :		str							     =		update_after_step
								UpperCAmelCase     :		Dict							     =		use_ema_warmup
								UpperCAmelCase     :		Union[str, Any]							     =		inv_gamma
								UpperCAmelCase     :		str							     =		power
								UpperCAmelCase     :		List[Any]							     =		0
								UpperCAmelCase     :		int							     =		None  # set in `step()`

								UpperCAmelCase     :		Optional[int]							     =		model_cls
								UpperCAmelCase     :		Any							     =		model_config





	@classmethod
	def  _lowercase(  cls		,		A		,		A    )			->			"EMAModel":
								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		model_cls.load_config(A		,		return_unused_kwargs=A    )
								UpperCAmelCase     :		List[str]							     =		model_cls.from_pretrained(A    )

								UpperCAmelCase     :		Any							     =		cls(model.parameters()		,		model_cls=A		,		model_config=model.config    )

								ema_model.load_state_dict(A    )
								return ema_model





	def  _lowercase(  self		,		A    )			->			Dict:
								if self.model_cls is None:
															raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""    )

								if self.model_config is None:
															raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""    )

								UpperCAmelCase     :		List[str]							     =		self.model_cls.from_config(self.model_config    )
								UpperCAmelCase     :		Optional[Any]							     =		self.state_dict()
								state_dict.pop("""shadow_params"""		,		A    )

								model.register_to_config(**A    )
								self.copy_to(model.parameters()    )
								model.save_pretrained(A    )





	def  _lowercase(  self		,		A    )			->			float:
								UpperCAmelCase     :		Optional[int]							     =		max(0		,		optimization_step - self.update_after_step - 1    )

								if step <= 0:
															return 0.0

								if self.use_ema_warmup:
															UpperCAmelCase     :		List[str]							     =		1 - (1 + step / self.inv_gamma) ** -self.power
								else:
															UpperCAmelCase     :		Tuple							     =		(1 + step) / (10 + step)

								UpperCAmelCase     :		Optional[int]							     =		min(A		,		self.decay    )
								# make sure decay is not smaller than min_decay
								UpperCAmelCase     :		Union[str, Any]							     =		max(A		,		self.min_decay    )
								return cur_decay_value





	@torch.no_grad()
	def  _lowercase(  self		,		A    )			->			List[Any]:
								if isinstance(A		,		torch.nn.Module    ):
															UpperCAmelCase     :		Optional[int]							     =		(
															    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
															    """Please pass the parameters of the module instead."""
															)
															deprecate(
															    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""		,		"""1.0.0"""		,		A		,		standard_warn=A		,		)
															UpperCAmelCase     :		Dict							     =		parameters.parameters()

								UpperCAmelCase     :		Optional[int]							     =		list(A    )

								self.optimization_step += 1

								# Compute the decay factor for the exponential moving average.
								UpperCAmelCase     :		Union[str, Any]							     =		self.get_decay(self.optimization_step    )
								UpperCAmelCase     :		Dict							     =		decay
								UpperCAmelCase     :		List[Any]							     =		1 - decay

								UpperCAmelCase     :		Tuple							     =		contextlib.nullcontext
								if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
															import deepspeed

								for s_param, param in zip(self.shadow_params		,		A    ):
															if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						UpperCAmelCase     :		List[Any]							     =		deepspeed.zero.GatheredParameters(A		,		modifier_rank=A    )

															with context_manager():
																						if param.requires_grad:
																													s_param.sub_(one_minus_decay * (s_param - param)    )
																						else:
																													s_param.copy_(A    )





	def  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		List[Any]							     =		list(A    )
								for s_param, param in zip(self.shadow_params		,		A    ):
															param.data.copy_(s_param.to(param.device    ).data    )





	def  _lowercase(  self		,		A=None		,		A=None    )			->			None:
								UpperCAmelCase     :		List[str]							     =		[
								    p.to(device=A		,		dtype=A    ) if p.is_floating_point() else p.to(device=A    )
								    for p in self.shadow_params
								]





	def  _lowercase(  self    )			->			dict:
								return {
								    "decay": self.decay,
								    "min_decay": self.min_decay,
								    "optimization_step": self.optimization_step,
								    "update_after_step": self.update_after_step,
								    "use_ema_warmup": self.use_ema_warmup,
								    "inv_gamma": self.inv_gamma,
								    "power": self.power,
								    "shadow_params": self.shadow_params,
								}





	def  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		Dict							     =		[param.detach().cpu().clone() for param in parameters]





	def  _lowercase(  self		,		A    )			->			None:
								if self.temp_stored_params is None:
															raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""    )
								for c_param, param in zip(self.temp_stored_params		,		A    ):
															param.data.copy_(c_param.data    )

								# Better memory-wise.
								UpperCAmelCase     :		Optional[Any]							     =		None






	def  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		Tuple							     =		copy.deepcopy(A    )

								UpperCAmelCase     :		Optional[Any]							     =		state_dict.get("""decay"""		,		self.decay    )
								if self.decay < 0.0 or self.decay > 1.0:
															raise ValueError("""Decay must be between 0 and 1"""    )

								UpperCAmelCase     :		Tuple							     =		state_dict.get("""min_decay"""		,		self.min_decay    )
								if not isinstance(self.min_decay		,		A    ):
															raise ValueError("""Invalid min_decay"""    )

								UpperCAmelCase     :		List[Any]							     =		state_dict.get("""optimization_step"""		,		self.optimization_step    )
								if not isinstance(self.optimization_step		,		A    ):
															raise ValueError("""Invalid optimization_step"""    )

								UpperCAmelCase     :		str							     =		state_dict.get("""update_after_step"""		,		self.update_after_step    )
								if not isinstance(self.update_after_step		,		A    ):
															raise ValueError("""Invalid update_after_step"""    )

								UpperCAmelCase     :		Dict							     =		state_dict.get("""use_ema_warmup"""		,		self.use_ema_warmup    )
								if not isinstance(self.use_ema_warmup		,		A    ):
															raise ValueError("""Invalid use_ema_warmup"""    )

								UpperCAmelCase     :		str							     =		state_dict.get("""inv_gamma"""		,		self.inv_gamma    )
								if not isinstance(self.inv_gamma		,		(float, int)    ):
															raise ValueError("""Invalid inv_gamma"""    )

								UpperCAmelCase     :		List[str]							     =		state_dict.get("""power"""		,		self.power    )
								if not isinstance(self.power		,		(float, int)    ):
															raise ValueError("""Invalid power"""    )

								UpperCAmelCase     :		Optional[int]							     =		state_dict.get("""shadow_params"""		,		A    )
								if shadow_params is not None:
															UpperCAmelCase     :		Optional[Any]							     =		shadow_params
															if not isinstance(self.shadow_params		,		A    ):
																						raise ValueError("""shadow_params must be a list"""    )
															if not all(isinstance(A		,		torch.Tensor    ) for p in self.shadow_params    ):
																						raise ValueError("""shadow_params must all be Tensors"""    )






'''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 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=13		,		A=7		,		A=True		,		A=True		,		A=True		,		A=True		,		A=99		,		A=64		,		A=5		,		A=4		,		A=37		,		A="gelu"		,		A=0.1		,		A=0.1		,		A=512		,		A=16		,		A=2		,		A=0.0_2		,		A=3		,		A=4		,		A=None		,		)			->			Optional[int]:
								UpperCAmelCase     :		List[Any]							     =		parent
								UpperCAmelCase     :		Optional[int]							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		Dict							     =		use_input_mask
								UpperCAmelCase     :		str							     =		use_token_type_ids
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		List[Any]							     =		vocab_size
								UpperCAmelCase     :		Dict							     =		hidden_size
								UpperCAmelCase     :		Dict							     =		num_hidden_layers
								UpperCAmelCase     :		Optional[int]							     =		num_attention_heads
								UpperCAmelCase     :		int							     =		intermediate_size
								UpperCAmelCase     :		List[str]							     =		hidden_act
								UpperCAmelCase     :		List[str]							     =		hidden_dropout_prob
								UpperCAmelCase     :		int							     =		attention_probs_dropout_prob
								UpperCAmelCase     :		str							     =		max_position_embeddings
								UpperCAmelCase     :		Optional[Any]							     =		type_vocab_size
								UpperCAmelCase     :		List[str]							     =		type_sequence_label_size
								UpperCAmelCase     :		int							     =		initializer_range
								UpperCAmelCase     :		str							     =		num_labels
								UpperCAmelCase     :		Optional[int]							     =		num_choices
								UpperCAmelCase     :		Dict							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		vocab_size - 1





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		List[str]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

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

								UpperCAmelCase     :		List[str]							     =		None
								if self.use_labels:
															UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels    )

								UpperCAmelCase     :		Optional[int]							     =		self.get_config()

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self    )			->			Optional[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=A		,		initializer_range=self.initializer_range		,		pad_token_id=self.pad_token_id		,		)





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.prepare_config_and_inputs()

								UpperCAmelCase     :		Any							     =		True

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		str							     =		GPTNeoXModel(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A    )
								UpperCAmelCase     :		List[str]							     =		model(A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





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





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			List[str]:
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)    )





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





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			str:
								UpperCAmelCase     :		List[Any]							     =		self.num_labels
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForTokenClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		int							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		True
								UpperCAmelCase     :		str							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()

								# first forward pass
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A		,		use_cache=A    )
								UpperCAmelCase     :		List[Any]							     =		outputs.past_key_values

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

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

								UpperCAmelCase     :		Dict							     =		model(A		,		attention_mask=A		,		output_hidden_states=A    )
								UpperCAmelCase     :		Any							     =		output_from_no_past["""hidden_states"""][0]
								UpperCAmelCase     :		List[str]							     =		model(
								    A		,		attention_mask=A		,		past_key_values=A		,		output_hidden_states=A		,		)["""hidden_states"""][0]

								# select random slice
								UpperCAmelCase     :		Tuple							     =		ids_tensor((1,)		,		output_from_past.shape[-1]    ).item()
								UpperCAmelCase     :		List[Any]							     =		output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase     :		List[str]							     =		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(A		,		A		,		atol=1e-3    )    )






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Tuple							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		config_and_inputs
								UpperCAmelCase     :		Union[str, Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = (
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase          = (GPTNeoXForCausalLM,) if is_torch_available() else ()
	lowercase          = (
	    {
	        'feature-extraction': GPTNeoXModel,
	        'question-answering': GPTNeoXForQuestionAnswering,
	        'text-classification': GPTNeoXForSequenceClassification,
	        'text-generation': GPTNeoXForCausalLM,
	        'token-classification': GPTNeoXForTokenClassification,
	        'zero-shot': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase          = False
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		GPTNeoXModelTester(self    )
								UpperCAmelCase     :		Optional[Any]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=64		,		num_attention_heads=8    )





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





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								# This regression test was failing with PyTorch < 1.3
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCAmelCase     :		Optional[Any]							     =		None

								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(A		,		A		,		A    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*A    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*A    )





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






	@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
	def  _lowercase(  self		,		A    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase     :		int							     =		ids_tensor([1, 10]		,		config.vocab_size    )
								UpperCAmelCase     :		Optional[Any]							     =		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     :		Dict							     =		GPTNeoXModel(A    )
								original_model.to(A    )
								original_model.eval()
								UpperCAmelCase     :		List[str]							     =		original_model(A    ).last_hidden_state
								UpperCAmelCase     :		Any							     =		original_model(A    ).last_hidden_state

								set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase     :		Any							     =		{"""type""": scaling_type, """factor""": 1_0.0}
								UpperCAmelCase     :		str							     =		GPTNeoXModel(A    )
								scaled_model.to(A    )
								scaled_model.eval()
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).last_hidden_state
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).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(A		,		A		,		atol=1e-5    )    )
								else:
															self.assertFalse(torch.allclose(A		,		A		,		atol=1e-5    )    )

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




@require_torch
class 			UpperCamelCase_ (							unittest.TestCase			):





	@slow
	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )
								for checkpointing in [True, False]:
															UpperCAmelCase     :		int							     =		GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )

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

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

															UpperCAmelCase     :		Union[str, Any]							     =		model.generate(**A		,		do_sample=A		,		max_new_tokens=20    )
															UpperCAmelCase     :		Tuple							     =		tokenizer.batch_decode(A    )[0]

															self.assertEqual(A		,		A    )

