






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


__A      =				16
__A      =				32




def      __A					(     _lowercase     :   str					,			_lowercase     :   List[str] = 16					,			_lowercase     :   Union[str, Any] = "bert-base-cased" ):

					'''simple docstring'''







					_A								= AutoTokenizer.from_pretrained(_lowercase )
					_A								= load_dataset('''glue'''					,			'''mrpc''' )

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

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

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

					def collate_fn(_lowercase     :   List[str] ):
										# 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(_lowercase					,			padding='''max_length'''					,			max_length=1_28					,			return_tensors='''pt''' )
										return tokenizer.pad(_lowercase					,			padding='''longest'''					,			return_tensors='''pt''' )

					# Instantiate dataloaders.
					_A								= DataLoader(
					    tokenized_datasets['''train''']					,			shuffle=_lowercase					,			collate_fn=_lowercase					,			batch_size=_lowercase )
					_A								= DataLoader(
					    tokenized_datasets['''validation''']					,			shuffle=_lowercase					,			collate_fn=_lowercase					,			batch_size=_lowercase )

					return train_dataloader, eval_dataloader




def      __A					(     _lowercase     :   Union[str, Any]					,			_lowercase     :   Optional[int]					,			_lowercase     :   Tuple					,			_lowercase     :   Tuple ):

					'''simple docstring'''







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

					_A								= metric.compute()
					return eval_metric["accuracy"]




def      __A					(     _lowercase     :   Optional[int]					,			_lowercase     :   Dict ):

					'''simple docstring'''







					_A								= Accelerator()

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

					set_seed(_lowercase )
					_A							,_A								= get_dataloaders(_lowercase					,			_lowercase					,			_lowercase )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_A								= AutoModelForSequenceClassification.from_pretrained(_lowercase					,			return_dict=_lowercase )

					# Instantiate optimizer
					_A								= (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					_A								= optimizer_cls(params=model.parameters()					,			lr=_lowercase )

					if accelerator.state.deepspeed_plugin is not None:
										_A								= accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										_A								= 1
					_A								= (len(_lowercase ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										_A								= get_linear_schedule_with_warmup(
										    optimizer=_lowercase					,			num_warmup_steps=0					,			num_training_steps=_lowercase					,			)
					else:
										_A								= DummyScheduler(_lowercase					,			total_num_steps=_lowercase					,			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.
					_A							,_A							,_A							,_A							,_A								= accelerator.prepare(
					    _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

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

					if args.partial_train_epoch is not None:
										_A								= args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint )
										_A								= args.resume_from_checkpoint.split('''epoch_''' )[1]
										_A								= ''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										_A								= int(_lowercase ) + 1
										_A								= evaluation_loop(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
										accelerator.print('''resumed checkpoint performance:'''					,			_lowercase )
										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:
															_A								= json.load(_lowercase )
															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
					_A								= {}
					for epoch in range(_lowercase					,			_lowercase ):
										model.train()
										for step, batch in enumerate(_lowercase ):
															_A								= model(**_lowercase )
															_A								= outputs.loss
															_A								= loss / gradient_accumulation_steps
															accelerator.backward(_lowercase )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										_A								= f"""epoch_{epoch}"""
										_A								= os.path.join(args.output_dir					,			_lowercase )
										accelerator.save_state(_lowercase )
										_A								= evaluation_loop(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
										_A								= accuracy
										_A								= lr_scheduler.get_lr()[0]
										_A								= optimizer.param_groups[0]['''lr''']
										_A								= epoch
										_A								= overall_step
										accelerator.print(f"""epoch {epoch}:"""					,			_lowercase )

										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(_lowercase					,			_lowercase )




def      __A					(     ):

					'''simple docstring'''







					_A								= argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
					parser.add_argument(
					    '''--model_name_or_path'''					,			type=_lowercase					,			default='''bert-base-cased'''					,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,			required=_lowercase					,			)
					parser.add_argument(
					    '''--output_dir'''					,			type=_lowercase					,			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=_lowercase					,			default=_lowercase					,			help='''If the training should continue from a checkpoint folder.'''					,			)
					parser.add_argument(
					    '''--partial_train_epoch'''					,			type=_lowercase					,			default=_lowercase					,			help='''If passed, the training will stop after this number of epochs.'''					,			)
					parser.add_argument(
					    '''--num_epochs'''					,			type=_lowercase					,			default=2					,			help='''Number of train epochs.'''					,			)
					_A								= parser.parse_args()
					_A								= {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(_lowercase					,			_lowercase )


if __name__ == "__main__":
				main()











import gc
import unittest

from transformers import CTRLConfig, is_torch_available
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 (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   Any		,     __A:   List[Any]=14		,     __A:   Dict=7		,     __A:   List[str]=True		,     __A:   Tuple=True		,     __A:   Union[str, Any]=True		,     __A:   List[Any]=True		,     __A:   Optional[int]=True		,     __A:   Tuple=99		,     __A:   Optional[Any]=32		,     __A:   List[str]=5		,     __A:   Dict=4		,     __A:   str=37		,     __A:   Dict="gelu"		,     __A:   List[str]=0.1		,     __A:   str=0.1		,     __A:   Any=5_12		,     __A:   Union[str, Any]=16		,     __A:   List[Any]=2		,     __A:   Tuple=0.02		,     __A:   Tuple=3		,     __A:   Union[str, Any]=4		,     __A:   Any=None		,     )    ->				Optional[Any]:
												_A								= parent
												_A								= batch_size
												_A								= seq_length
												_A								= is_training
												_A								= use_token_type_ids
												_A								= use_input_mask
												_A								= use_labels
												_A								= use_mc_token_ids
												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= intermediate_size
												_A								= hidden_act
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= type_sequence_label_size
												_A								= initializer_range
												_A								= num_labels
												_A								= num_choices
												_A								= scope
												_A								= self.vocab_size - 1

							def       __A (  self:   Optional[int]   )    ->				Union[str, Any]:
												_A								= ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size   )

												_A								= None
												if self.use_input_mask:
																	_A								= random_attention_mask([self.batch_size, self.seq_length]   )

												_A								= None
												if self.use_token_type_ids:
																	_A								= ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size   )

												_A								= None
												if self.use_mc_token_ids:
																	_A								= ids_tensor([self.batch_size, self.num_choices]		,     self.seq_length   )

												_A								= None
												_A								= None
												_A								= None
												if self.use_labels:
																	_A								= ids_tensor([self.batch_size]		,     self.type_sequence_label_size   )
																	_A								= ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels   )
																	_A								= ids_tensor([self.batch_size]		,     self.num_choices   )

												_A								= self.get_config()

												_A								= ids_tensor([self.num_hidden_layers, self.num_attention_heads]		,     2   )

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												    token_type_ids,
												    mc_token_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)

							def       __A (  self:   Optional[int]   )    ->				List[Any]:
												return CTRLConfig(
												    vocab_size=self.vocab_size		,     n_embd=self.hidden_size		,     n_layer=self.num_hidden_layers		,     n_head=self.num_attention_heads		,     n_positions=self.max_position_embeddings		,     pad_token_id=self.pad_token_id		,     )

							def       __A (  self:   Union[str, Any]		,     __A:   Union[str, Any]		,     __A:   Dict		,     __A:   Optional[int]		,     __A:   List[str]		,     __A:   List[str]		,     *__A:   Optional[int]   )    ->				Optional[Any]:
												_A								= CTRLModel(config=__A   )
												model.to(__A   )
												model.eval()

												model(__A		,     token_type_ids=__A		,     head_mask=__A   )
												model(__A		,     token_type_ids=__A   )
												_A								= model(__A   )
												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)   )
												self.parent.assertEqual(len(result.past_key_values   )		,     config.n_layer   )

							def       __A (  self:   Optional[Any]		,     __A:   List[str]		,     __A:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any		,     *__A:   Any   )    ->				str:
												_A								= CTRLLMHeadModel(__A   )
												model.to(__A   )
												model.eval()

												_A								= model(__A		,     token_type_ids=__A		,     labels=__A   )
												self.parent.assertEqual(result.loss.shape		,     ()   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)   )

							def       __A (  self:   Optional[int]   )    ->				Dict:
												_A								= self.prepare_config_and_inputs()

												(
												    (
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,
												)								= config_and_inputs

												_A								= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

												return config, inputs_dict



							def       __A (  self:   List[str]		,     __A:   Dict		,     __A:   Dict		,     __A:   Tuple		,     __A:   List[Any]		,     *__A:   Optional[int]   )    ->				Any:
												_A								= self.num_labels
												_A								= CTRLForSequenceClassification(__A   )
												model.to(__A   )
												model.eval()
												_A								= ids_tensor([self.batch_size]		,     self.type_sequence_label_size   )
												_A								= model(__A		,     token_type_ids=__A		,     labels=__A   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)   )





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





							"""simple docstring"""

							A_						  =					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							A_						  =					(CTRLLMHeadModel,) if is_torch_available() else ()
							A_						  =					(
							    {
							        "feature-extraction": CTRLModel,
							        "text-classification": CTRLForSequenceClassification,
							        "text-generation": CTRLLMHeadModel,
							        "zero-shot": CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							A_						  =					True
							A_						  =					False
							A_						  =					False

							def       __A (  self:   Any		,     __A:   List[Any]		,     __A:   int		,     __A:   Optional[Any]		,     __A:   Optional[int]		,     __A:   List[Any]   )    ->				List[str]:
												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

												return False

							def       __A (  self:   Any   )    ->				Union[str, Any]:
												_A								= CTRLModelTester(self   )
												_A								= ConfigTester(self		,     config_class=__A		,     n_embd=37   )

							def       __A (  self:   Optional[int]   )    ->				List[Any]:
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()

							def       __A (  self:   Dict   )    ->				Any:
												self.config_tester.run_common_tests()

							def       __A (  self:   str   )    ->				Optional[Any]:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*__A   )

							def       __A (  self:   List[str]   )    ->				Any:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*__A   )

							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
							def       __A (  self:   Optional[Any]   )    ->				int:
												pass

							@slow
							def       __A (  self:   Tuple   )    ->				Dict:
												for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A								= CTRLModel.from_pretrained(__A   )
																	self.assertIsNotNone(__A   )



							@unittest.skip('''The model doesn\'t support left padding'''   )  # and it's not used enough to be worth fixing :)
							def       __A (  self:   Any   )    ->				Union[str, Any]:
												pass





@require_torch
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   int   )    ->				Union[str, Any]:
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()



							@slow
							def       __A (  self:   Any   )    ->				Any:
												_A								= CTRLLMHeadModel.from_pretrained('''ctrl'''   )
												model.to(__A   )
												_A								= torch.tensor(
												    [[1_18_59, 0, 16_11, 8]]		,     dtype=torch.long		,     device=__A   )  # Legal the president is
												_A								= [
												    1_18_59,
												    0,
												    16_11,
												    8,
												    5,
												    1_50,
												    2_64_49,
												    2,
												    19,
												    3_48,
												    4_69,
												    3,
												    25_95,
												    48,
												    2_07_40,
												    24_65_33,
												    24_65_33,
												    19,
												    30,
												    5,
												]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

												_A								= model.generate(__A		,     do_sample=__A   )
												self.assertListEqual(output_ids[0].tolist()		,     __A   )



