






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__     =	"scheduler_config.json"

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : List[Any]         =    1
							snake_case       : str         =    2
							snake_case       : Dict         =    3
							snake_case       : Any         =    4
							snake_case       : str         =    5






@dataclass
class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : jnp.ndarray






class     __SCREAMING_SNAKE_CASE   :
							snake_case       : Optional[Any]         =    SCHEDULER_CONFIG_NAME
							snake_case       : Tuple         =    ["""dtype"""]
							snake_case       : List[str]         =    []
							snake_case       : str         =    True






							@classmethod
							def 			_lowerCamelCase						(  cls					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase=False					,	**__lowerCAmelCase					,	):
														UpperCamelCase__       ,       UpperCamelCase__			       =     cls.load_config(
														    pretrained_model_name_or_path=__lowerCAmelCase					,	subfolder=__lowerCAmelCase					,	return_unused_kwargs=__lowerCAmelCase					,	**__lowerCAmelCase					,	)
														UpperCamelCase__       ,       UpperCamelCase__			       =     cls.from_config(__lowerCAmelCase					,	return_unused_kwargs=__lowerCAmelCase					,	**__lowerCAmelCase					)

														if hasattr(__lowerCAmelCase					,	"""create_state"""					) and getattr(__lowerCAmelCase					,	"""has_state"""					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = False					,	**__lowerCAmelCase					):
														self.save_config(save_directory=__lowerCAmelCase					,	push_to_hub=__lowerCAmelCase					,	**__lowerCAmelCase					)






							@property
							def 			_lowerCamelCase						(  self					):
														return self._get_compatibles()







							@classmethod
							def 			_lowerCamelCase						(  cls					):
														UpperCamelCase__			       =     list(set([cls.__name__] + cls._compatibles					)					)
														UpperCamelCase__			       =     importlib.import_module(__name__.split("""."""					)[0]					)
														UpperCamelCase__			       =     [
														    getattr(__lowerCAmelCase					,	__lowerCAmelCase					) for c in compatible_classes_str if hasattr(__lowerCAmelCase					,	__lowerCAmelCase					)
														]
														return compatible_classes






def       _UpperCamelCase       (a__				:jnp.ndarray				,     a__				:Tuple[int]    ):






							"""simple docstring"""





							assert len(a__    ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a__    ) - x.ndim)    )				,     a__    )





def       _UpperCamelCase       (a__				:int				,     a__				:str=0.999				,     a__				:List[Any]=jnp.floataa    ):






							"""simple docstring"""





							def alpha_bar(a__				:List[str]    ):
														return math.cos((time_step + 0.008) / 1.008 * math.pi / 2    ) ** 2

							UpperCamelCase__			       =     []
							for i in range(a__    ):
														UpperCamelCase__			       =     i / num_diffusion_timesteps
														UpperCamelCase__			       =     (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(a__    ) / alpha_bar(a__    )				,     a__    )    )
							return jnp.array(a__				,     dtype=a__    )

@flax.struct.dataclass
class     __SCREAMING_SNAKE_CASE   :
							snake_case       : jnp.ndarray
							snake_case       : jnp.ndarray
							snake_case       : jnp.ndarray






							@classmethod
							def 			_lowerCamelCase						(  cls					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     scheduler.config

														if config.trained_betas is not None:
																					UpperCamelCase__			       =     jnp.asarray(config.trained_betas					,	dtype=scheduler.dtype					)
														elif config.beta_schedule == "linear":
																					UpperCamelCase__			       =     jnp.linspace(config.beta_start					,	config.beta_end					,	config.num_train_timesteps					,	dtype=scheduler.dtype					)
														elif config.beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					UpperCamelCase__			       =     (
																					    jnp.linspace(
																					        config.beta_start**0.5					,	config.beta_end**0.5					,	config.num_train_timesteps					,	dtype=scheduler.dtype					)
																					    ** 2
																					)
														elif config.beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					UpperCamelCase__			       =     betas_for_alpha_bar(config.num_train_timesteps					,	dtype=scheduler.dtype					)
														else:
																					raise NotImplementedError(
																					    f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""					)

														UpperCamelCase__			       =     1.0 - betas

														UpperCamelCase__			       =     jnp.cumprod(__lowerCAmelCase					,	axis=0					)

														return cls(
														    alphas=__lowerCAmelCase					,	betas=__lowerCAmelCase					,	alphas_cumprod=__lowerCAmelCase					,	)






def       _UpperCamelCase       (a__				:CommonSchedulerState				,     a__				:jnp.ndarray				,     a__				:jnp.ndarray				,     a__				:jnp.ndarray    ):






							"""simple docstring"""





							UpperCamelCase__			       =     state.alphas_cumprod

							UpperCamelCase__			       =     alphas_cumprod[timesteps] ** 0.5
							UpperCamelCase__			       =     sqrt_alpha_prod.flatten()
							UpperCamelCase__			       =     broadcast_to_shape_from_left(a__				,     original_samples.shape    )

							UpperCamelCase__			       =     (1 - alphas_cumprod[timesteps]) ** 0.5
							UpperCamelCase__			       =     sqrt_one_minus_alpha_prod.flatten()
							UpperCamelCase__			       =     broadcast_to_shape_from_left(a__				,     original_samples.shape    )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def       _UpperCamelCase       (a__				:CommonSchedulerState				,     a__				:jnp.ndarray				,     a__				:jnp.ndarray				,     a__				:jnp.ndarray    ):






							"""simple docstring"""





							UpperCamelCase__       ,       UpperCamelCase__			       =     get_sqrt_alpha_prod(a__				,     a__				,     a__				,     a__    )
							UpperCamelCase__			       =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples





def       _UpperCamelCase       (a__				:CommonSchedulerState				,     a__				:jnp.ndarray				,     a__				:jnp.ndarray				,     a__				:jnp.ndarray    ):






							"""simple docstring"""





							UpperCamelCase__       ,       UpperCamelCase__			       =     get_sqrt_alpha_prod(a__				,     a__				,     a__				,     a__    )
							UpperCamelCase__			       =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity











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     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=13					,	__lowerCAmelCase=7					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=99					,	__lowerCAmelCase=64					,	__lowerCAmelCase=5					,	__lowerCAmelCase=4					,	__lowerCAmelCase=37					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=512					,	__lowerCAmelCase=16					,	__lowerCAmelCase=2					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=3					,	__lowerCAmelCase=4					,	__lowerCAmelCase=None					,	):
														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 			_lowerCamelCase						(  self					):
														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 			_lowerCamelCase						(  self					):
														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 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.prepare_config_and_inputs()

														UpperCamelCase__			       =     True

														return config, input_ids, input_mask, token_labels






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     GPTNeoXModel(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														UpperCamelCase__			       =     model(__lowerCAmelCase					)
														self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     True
														UpperCamelCase__			       =     GPTNeoXModel(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     GPTNeoXForCausalLM(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForQuestionAnswering(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForSequenceClassification(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     ids_tensor([self.batch_size]					,	self.type_sequence_label_size					)
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForTokenClassification(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     True
														UpperCamelCase__			       =     GPTNeoXForCausalLM(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()

														# first forward pass
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	use_cache=__lowerCAmelCase					)
														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(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					)
														UpperCamelCase__			       =     output_from_no_past["""hidden_states"""][0]
														UpperCamelCase__			       =     model(
														    __lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	past_key_values=__lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					,	)["""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(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-3					)					)







							def 			_lowerCamelCase						(  self					):
														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     __SCREAMING_SNAKE_CASE   (       _a				,						_a				,						_a				,						unittest.TestCase    ):
							snake_case       : Optional[Any]         =    (
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case       : Union[str, Any]         =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
							snake_case       : Dict         =    (
							    {
							        """feature-extraction""": GPTNeoXModel,
							        """question-answering""": GPTNeoXForQuestionAnswering,
							        """text-classification""": GPTNeoXForSequenceClassification,
							        """text-generation""": GPTNeoXForCausalLM,
							        """token-classification""": GPTNeoXForTokenClassification,
							        """zero-shot""": GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							snake_case       : Tuple         =    False
							snake_case       : Dict         =    False
							snake_case       : Tuple         =    False
							snake_case       : Any         =    False






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     GPTNeoXModelTester(self					)
														UpperCamelCase__			       =     ConfigTester(self					,	config_class=__lowerCAmelCase					,	hidden_size=64					,	num_attention_heads=8					)






							def 			_lowerCamelCase						(  self					):
														self.config_tester.run_common_tests()






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														# This regression test was failing with PyTorch < 1.3
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_decoder()

														UpperCamelCase__			       =     None

														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     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 			_lowerCamelCase						(  self					):
														pass







							@parameterized.expand([("""linear""",), ("""dynamic""",)]					)
							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														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(__lowerCAmelCase					)
														original_model.to(__lowerCAmelCase					)
														original_model.eval()
														UpperCamelCase__			       =     original_model(__lowerCAmelCase					).last_hidden_state
														UpperCamelCase__			       =     original_model(__lowerCAmelCase					).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(__lowerCAmelCase					)
														scaled_model.to(__lowerCAmelCase					)
														scaled_model.eval()
														UpperCamelCase__			       =     scaled_model(__lowerCAmelCase					).last_hidden_state
														UpperCamelCase__			       =     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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							@slow
							def 			_lowerCamelCase						(  self					):
														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(__lowerCAmelCase					)

																					UpperCamelCase__			       =     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
																					UpperCamelCase__			       =     """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

																					UpperCamelCase__			       =     model.generate(**__lowerCAmelCase					,	do_sample=__lowerCAmelCase					,	max_new_tokens=20					)
																					UpperCamelCase__			       =     tokenizer.batch_decode(__lowerCAmelCase					)[0]

																					self.assertEqual(__lowerCAmelCase					,	__lowerCAmelCase					)




