

'''simple docstring'''


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase__						:       Dict										=						16
lowerCAmelCase__						:       Any										=						32




def 	_a       (				__lowerCAmelCase		:      Accelerator   ,							__lowerCAmelCase		:      int = 16   ,							__lowerCAmelCase		:      str = "bert-base-cased"     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  AutoTokenizer.from_pretrained(__lowerCAmelCase     )
		snake_case__						:       str       						=  load_dataset('''glue'''   ,							'''mrpc'''     )

		def tokenize_function(__lowerCAmelCase		:      str     ):
				# max_length=None => use the model max length (it's actually the default)
				snake_case__						:       int       						=  tokenizer(examples['''sentence1''']   ,							examples['''sentence2''']   ,							truncation=__lowerCAmelCase   ,							max_length=__lowerCAmelCase     )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		snake_case__						:       str       						=  datasets.map(
		    __lowerCAmelCase   ,							batched=__lowerCAmelCase   ,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']   ,							load_from_cache_file=__lowerCAmelCase     )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		snake_case__						:       Dict       						=  tokenized_datasets.rename_column('''label'''   ,							'''labels'''     )

		def collate_fn(__lowerCAmelCase		:      Tuple     ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(__lowerCAmelCase   ,							padding='''max_length'''   ,							max_length=1_28   ,							return_tensors='''pt'''     )
				return tokenizer.pad(__lowerCAmelCase   ,							padding='''longest'''   ,							return_tensors='''pt'''     )

		# Instantiate dataloaders.
		snake_case__						:       List[str]       						=  DataLoader(
		    tokenized_datasets['''train''']   ,							shuffle=__lowerCAmelCase   ,							collate_fn=__lowerCAmelCase   ,							batch_size=__lowerCAmelCase     )
		snake_case__						:       Tuple       						=  DataLoader(
		    tokenized_datasets['''validation''']   ,							shuffle=__lowerCAmelCase   ,							collate_fn=__lowerCAmelCase   ,							batch_size=__lowerCAmelCase     )

		return train_dataloader, eval_dataloader




def 	_a       (				__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		model.eval()
		snake_case__						:       str       						=  0
		for step, batch in enumerate(__lowerCAmelCase     ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device     )
				with torch.no_grad():
						snake_case__						:       Any       						=  model(**__lowerCAmelCase     )
				snake_case__						:       Optional[int]       						=  outputs.logits.argmax(dim=-1     )
				# It is slightly faster to call this once, than multiple times
				snake_case__						,       snake_case__						:       Any       						=  accelerator.gather(
				    (predictions, batch['''labels'''])     )  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(__lowerCAmelCase     ) - 1:
								snake_case__						:       Optional[int]       						=  predictions[: len(eval_dataloader.dataset     ) - samples_seen]
								snake_case__						:       Union[str, Any]       						=  references[: len(eval_dataloader.dataset     ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=__lowerCAmelCase   ,							references=__lowerCAmelCase   ,							)

		snake_case__						:       List[Any]       						=  metric.compute()
		return eval_metric["accuracy"]




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		snake_case__						:       Optional[int]       						=  config['''lr''']
		snake_case__						:       Optional[Any]       						=  int(config['''num_epochs''']     )
		snake_case__						:       Tuple       						=  int(config['''seed''']     )
		snake_case__						:       Any       						=  int(config['''batch_size''']     )
		snake_case__						:       Dict       						=  args.model_name_or_path

		set_seed(__lowerCAmelCase     )
		snake_case__						,       snake_case__						:       str       						=  get_dataloaders(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		snake_case__						:       Union[str, Any]       						=  AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase   ,							return_dict=__lowerCAmelCase     )

		# Instantiate optimizer
		snake_case__						:       int       						=  (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		snake_case__						:       Tuple       						=  optimizer_cls(params=model.parameters()   ,							lr=__lowerCAmelCase     )

		if accelerator.state.deepspeed_plugin is not None:
				snake_case__						:       str       						=  accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				snake_case__						:       Any       						=  1
		snake_case__						:       List[Any]       						=  (len(__lowerCAmelCase     ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				snake_case__						:       Union[str, Any]       						=  get_linear_schedule_with_warmup(
				    optimizer=__lowerCAmelCase   ,							num_warmup_steps=0   ,							num_training_steps=__lowerCAmelCase   ,							)
		else:
				snake_case__						:       Dict       						=  DummyScheduler(__lowerCAmelCase   ,							total_num_steps=__lowerCAmelCase   ,							warmup_num_steps=0     )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       str       						=  accelerator.prepare(
		    __lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )

		# We need to keep track of how many total steps we have iterated over
		snake_case__						:       int       						=  0
		# We also need to keep track of the stating epoch so files are named properly
		snake_case__						:       Dict       						=  0
		snake_case__						:       Any       						=  evaluate.load('''glue'''   ,							'''mrpc'''     )
		snake_case__						:       List[Any]       						=  num_epochs

		if args.partial_train_epoch is not None:
				snake_case__						:       Any       						=  args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint     )
				snake_case__						:       Dict       						=  args.resume_from_checkpoint.split('''epoch_'''     )[1]
				snake_case__						:       Any       						=  ''''''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				snake_case__						:       Dict       						=  int(__lowerCAmelCase     ) + 1
				snake_case__						:       Any       						=  evaluation_loop(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )
				accelerator.print('''resumed checkpoint performance:'''   ,							__lowerCAmelCase     )
				accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''   ,							lr_scheduler.get_lr()[0]     )
				accelerator.print('''resumed optimizers\'s lr:'''   ,							optimizer.param_groups[0]['''lr''']     )
				with open(os.path.join(args.output_dir   ,							F"""state_{starting_epoch-1}.json"""     )   ,							'''r'''     ) as f:
						snake_case__						:       Tuple       						=  json.load(__lowerCAmelCase     )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		snake_case__						:       Union[str, Any]       						=  {}
		for epoch in range(__lowerCAmelCase   ,							__lowerCAmelCase     ):
				model.train()
				for step, batch in enumerate(__lowerCAmelCase     ):
						snake_case__						:       List[str]       						=  model(**__lowerCAmelCase     )
						snake_case__						:       Optional[int]       						=  outputs.loss
						snake_case__						:       List[Any]       						=  loss / gradient_accumulation_steps
						accelerator.backward(__lowerCAmelCase     )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				snake_case__						:       Optional[Any]       						=  F"""epoch_{epoch}"""
				snake_case__						:       Tuple       						=  os.path.join(args.output_dir   ,							__lowerCAmelCase     )
				accelerator.save_state(__lowerCAmelCase     )
				snake_case__						:       Optional[int]       						=  evaluation_loop(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )
				snake_case__						:       int       						=  accuracy
				snake_case__						:       List[str]       						=  lr_scheduler.get_lr()[0]
				snake_case__						:       str       						=  optimizer.param_groups[0]['''lr''']
				snake_case__						:       Dict       						=  epoch
				snake_case__						:       Any       						=  overall_step
				accelerator.print(F"""epoch {epoch}:"""   ,							__lowerCAmelCase     )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir   ,							F"""state_{epoch}.json"""     )   ,							'''w'''     ) as f:
								json.dump(__lowerCAmelCase   ,							__lowerCAmelCase     )




def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''     )
		parser.add_argument(
		    '''--model_name_or_path'''   ,							type=__lowerCAmelCase   ,							default='''bert-base-cased'''   ,							help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,							required=__lowerCAmelCase   ,							)
		parser.add_argument(
		    '''--output_dir'''   ,							type=__lowerCAmelCase   ,							default='''.'''   ,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,							)
		parser.add_argument(
		    '''--resume_from_checkpoint'''   ,							type=__lowerCAmelCase   ,							default=__lowerCAmelCase   ,							help='''If the training should continue from a checkpoint folder.'''   ,							)
		parser.add_argument(
		    '''--partial_train_epoch'''   ,							type=__lowerCAmelCase   ,							default=__lowerCAmelCase   ,							help='''If passed, the training will stop after this number of epochs.'''   ,							)
		parser.add_argument(
		    '''--num_epochs'''   ,							type=__lowerCAmelCase   ,							default=2   ,							help='''Number of train epochs.'''   ,							)
		snake_case__						:       str       						=  parser.parse_args()
		snake_case__						:       List[Any]       						=  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

		training_function(__lowerCAmelCase   ,							__lowerCAmelCase     )


if __name__ == "__main__":
					main()






'''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_  )



