

'''simple docstring'''


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Dict			,			snake_case_					:      str			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      List[Any]=True			,			snake_case_					:      Any=None			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  parent
								snake_case__						:       List[Any]       						=  config_class
								snake_case__						:       Optional[int]       						=  has_text_modality
								snake_case__						:       str       						=  kwargs
								snake_case__						:       Dict       						=  common_properties







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       List[Any]       						=  (
								    ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
										common_properties.extend(['''vocab_size''']  )

								# Test that config has the common properties as getters
								for prop in common_properties:
										self.parent.assertTrue(hasattr(snake_case_			,			snake_case_  )			,			msg=F"""`{prop}` does not exist"""  )

								# Test that config has the common properties as setter
								for idx, name in enumerate(snake_case_  ):
										try:
												setattr(snake_case_			,			snake_case_			,			snake_case_  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(snake_case_  ):
										try:
												snake_case__						:       Dict       						=  self.config_class(**{name: idx}  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       str       						=  json.loads(config.to_json_string()  )
								for key, value in self.inputs_dict.items():
										self.parent.assertEqual(obj[key]			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[Any]       						=  os.path.join(snake_case_			,			'''config.json'''  )
										config_first.to_json_file(snake_case_  )
										snake_case__						:       Dict       						=  self.config_class.from_json_file(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       Optional[Any]       						=  self.config_class.from_pretrained(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								snake_case__						:       str       						=  '''test'''
								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[str]       						=  os.path.join(snake_case_			,			snake_case_  )
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       List[str]       						=  self.config_class.from_pretrained(snake_case_			,			subfolder=snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.config_class(**self.inputs_dict			,			num_labels=5  )
								self.parent.assertEqual(len(config.idalabel  )			,			5  )
								self.parent.assertEqual(len(config.labelaid  )			,			5  )

								snake_case__						:       List[Any]       						=  3
								self.parent.assertEqual(len(config.idalabel  )			,			3  )
								self.parent.assertEqual(len(config.labelaid  )			,			3  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								if self.config_class.is_composition:
										return
								snake_case__						:       Any       						=  self.config_class()
								self.parent.assertIsNotNone(snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  copy.deepcopy(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  self.config_class(**snake_case_  )
								snake_case__						:       List[str]       						=  []
								for key, value in config_common_kwargs.items():
										if key == "torch_dtype":
												if not is_torch_available():
														continue
												else:
														import torch

														if config.torch_dtype != torch.floataa:
																wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)  )
										elif getattr(snake_case_			,			snake_case_  ) != value:
												wrong_values.append((key, getattr(snake_case_			,			snake_case_  ), value)  )

								if len(snake_case_  ) > 0:
										snake_case__						:       Dict       						=  '''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]  )
										raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()






'''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			:






						"""simple docstring"""




						def __init__(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      Optional[Any]=1_3			,			snake_case_					:      int=7			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Dict=True			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=9_9			,			snake_case_					:      int=6_4			,			snake_case_					:      Dict=5			,			snake_case_					:      List[Any]=4			,			snake_case_					:      Union[str, Any]=3_7			,			snake_case_					:      Dict="gelu"			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      Dict=0.1			,			snake_case_					:      Any=5_1_2			,			snake_case_					:      Any=1_6			,			snake_case_					:      Any=2			,			snake_case_					:      Dict=0.0_2			,			snake_case_					:      List[str]=3			,			snake_case_					:      Optional[int]=4			,			snake_case_					:      str=None			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  parent
								snake_case__						:       int       						=  batch_size
								snake_case__						:       Dict       						=  seq_length
								snake_case__						:       int       						=  is_training
								snake_case__						:       Optional[Any]       						=  use_input_mask
								snake_case__						:       Optional[Any]       						=  use_token_type_ids
								snake_case__						:       Dict       						=  use_labels
								snake_case__						:       int       						=  vocab_size
								snake_case__						:       Any       						=  hidden_size
								snake_case__						:       int       						=  num_hidden_layers
								snake_case__						:       Union[str, Any]       						=  num_attention_heads
								snake_case__						:       List[Any]       						=  intermediate_size
								snake_case__						:       int       						=  hidden_act
								snake_case__						:       int       						=  hidden_dropout_prob
								snake_case__						:       List[Any]       						=  attention_probs_dropout_prob
								snake_case__						:       Optional[int]       						=  max_position_embeddings
								snake_case__						:       Optional[int]       						=  type_vocab_size
								snake_case__						:       Any       						=  type_sequence_label_size
								snake_case__						:       str       						=  initializer_range
								snake_case__						:       List[str]       						=  num_labels
								snake_case__						:       Dict       						=  num_choices
								snake_case__						:       Union[str, Any]       						=  scope
								snake_case__						:       List[Any]       						=  vocab_size - 1







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size  )

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

								snake_case__						:       Union[str, Any]       						=  None
								if self.use_labels:
										snake_case__						:       Optional[Any]       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels  )

								snake_case__						:       Dict       						=  self.get_config()

								return config, input_ids, input_mask, token_labels







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								return GPTNeoXConfig(
								    vocab_size=self.vocab_size			,			hidden_size=self.hidden_size			,			num_hidden_layers=self.num_hidden_layers			,			num_attention_heads=self.num_attention_heads			,			intermediate_size=self.intermediate_size			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			type_vocab_size=self.type_vocab_size			,			is_decoder=snake_case_			,			initializer_range=self.initializer_range			,			pad_token_id=self.pad_token_id			,			)







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       str       						=  self.prepare_config_and_inputs()

								snake_case__						:       List[str]       						=  True

								return config, input_ids, input_mask, token_labels







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Any			,			snake_case_					:      str			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  GPTNeoXModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Union[str, Any]       						=  model(snake_case_			,			attention_mask=snake_case_  )
								snake_case__						:       int       						=  model(snake_case_  )
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  True
								snake_case__						:       Tuple       						=  GPTNeoXModel(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Dict       						=  model(snake_case_			,			attention_mask=snake_case_  )
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  GPTNeoXForCausalLM(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Tuple       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      str			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.num_labels
								snake_case__						:       List[Any]       						=  GPTNeoXForQuestionAnswering(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Any       						=  model(snake_case_			,			attention_mask=snake_case_  )
								self.parent.assertEqual(result.start_logits.shape			,			(self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape			,			(self.batch_size, self.seq_length)  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.num_labels
								snake_case__						:       List[str]       						=  GPTNeoXForSequenceClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Tuple       						=  ids_tensor([self.batch_size]			,			self.type_sequence_label_size  )
								snake_case__						:       str       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      Dict			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.num_labels
								snake_case__						:       Any       						=  GPTNeoXForTokenClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels)  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  True
								snake_case__						:       Union[str, Any]       						=  GPTNeoXForCausalLM(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()

								# first forward pass
								snake_case__						:       int       						=  model(snake_case_			,			attention_mask=snake_case_			,			use_cache=snake_case_  )
								snake_case__						:       Dict       						=  outputs.past_key_values

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

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

								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			attention_mask=snake_case_			,			output_hidden_states=snake_case_  )
								snake_case__						:       Union[str, Any]       						=  output_from_no_past['''hidden_states'''][0]
								snake_case__						:       str       						=  model(
								    snake_case_			,			attention_mask=snake_case_			,			past_key_values=snake_case_			,			output_hidden_states=snake_case_			,			)['''hidden_states'''][0]

								# select random slice
								snake_case__						:       str       						=  ids_tensor((1,)			,			output_from_past.shape[-1]  ).item()
								snake_case__						:       str       						=  output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case__						:       Union[str, Any]       						=  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(snake_case_			,			snake_case_			,			atol=1e-3  )  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Tuple       						=  config_and_inputs
								snake_case__						:       str       						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__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       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  GPTNeoXModelTester(self  )
								snake_case__						:       List[str]       						=  ConfigTester(self			,			config_class=snake_case_			,			hidden_size=6_4			,			num_attention_heads=8  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case__						:       Optional[int]       						=  None

								self.model_tester.create_and_check_model_as_decoder(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Dict       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case_  )







						@unittest.skip(reason='''Feed forward chunking is not implemented'''  )
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						@parameterized.expand([('''linear''',), ('''dynamic''',)]  )
						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       List[Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()
								snake_case__						:       Dict       						=  ids_tensor([1, 1_0]			,			config.vocab_size  )
								snake_case__						:       List[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
								snake_case__						:       Tuple       						=  GPTNeoXModel(snake_case_  )
								original_model.to(snake_case_  )
								original_model.eval()
								snake_case__						:       Any       						=  original_model(snake_case_  ).last_hidden_state
								snake_case__						:       List[str]       						=  original_model(snake_case_  ).last_hidden_state

								set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
								snake_case__						:       Optional[Any]       						=  {'''type''': scaling_type, '''factor''': 1_0.0}
								snake_case__						:       Optional[Any]       						=  GPTNeoXModel(snake_case_  )
								scaled_model.to(snake_case_  )
								scaled_model.eval()
								snake_case__						:       Optional[int]       						=  scaled_model(snake_case_  ).last_hidden_state
								snake_case__						:       List[str]       						=  scaled_model(snake_case_  ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(snake_case_			,			snake_case_			,			atol=1e-5  )  )
								else:
										self.assertFalse(torch.allclose(snake_case_			,			snake_case_			,			atol=1e-5  )  )

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





@require_torch
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@slow
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )
								for checkpointing in [True, False]:
										snake_case__						:       str       						=  GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )

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

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

										snake_case__						:       Optional[int]       						=  model.generate(**snake_case_			,			do_sample=snake_case_			,			max_new_tokens=2_0  )
										snake_case__						:       Tuple       						=  tokenizer.batch_decode(snake_case_  )[0]

										self.assertEqual(snake_case_			,			snake_case_  )



