
"""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


UpperCAmelCase__          = 16
UpperCAmelCase__          = 32
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Accelerator ,					__lowerCamelCase   :       int = 16 ,					__lowerCamelCase   :       str = "bert-base-cased"		)     ->					List[str]:
  _snake_case					     =       AutoTokenizer.from_pretrained(lowerCAmelCase__		)
  _snake_case					     =       load_dataset('''glue''' ,					'''mrpc'''		)

  def tokenize_function(__lowerCamelCase   :       Any		):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case					     =       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					     =       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					     =       tokenized_datasets.rename_column('''label''' ,					'''labels'''		)

  def collate_fn(__lowerCamelCase   :       Any		):
    # 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					     =       DataLoader(
      tokenized_datasets['''train'''] ,					shuffle=lowerCAmelCase__ ,					collate_fn=lowerCAmelCase__ ,					batch_size=lowerCAmelCase__		)
  _snake_case					     =       DataLoader(
      tokenized_datasets['''validation'''] ,					shuffle=lowerCAmelCase__ ,					collate_fn=lowerCAmelCase__ ,					batch_size=lowerCAmelCase__		)

  return train_dataloader, eval_dataloader
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Any		)     ->					List[str]:
  # Initialize accelerator
  _snake_case					     =       Accelerator()

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

  set_seed(lowerCAmelCase__		)
  _snake_case     ,					_snake_case					     =       get_dataloaders(lowerCAmelCase__ ,					lowerCAmelCase__ ,					lowerCAmelCase__		)

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

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

  if accelerator.state.deepspeed_plugin is not None:
    _snake_case					     =       accelerator.state.deepspeed_plugin.deepspeed_config[
        '''gradient_accumulation_steps'''
    ]
  else:
    _snake_case					     =       1
  _snake_case					     =       (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					     =       get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase__ ,					num_warmup_steps=0 ,					num_training_steps=lowerCAmelCase__ ,					)
  else:
    _snake_case					     =       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					     =       accelerator.prepare(
      lowerCAmelCase__ ,					lowerCAmelCase__ ,					lowerCAmelCase__ ,					lowerCAmelCase__ ,					lowerCAmelCase__		)

  # We need to keep track of how many total steps we have iterated over
  _snake_case					     =       0
  # We also need to keep track of the stating epoch so files are named properly
  _snake_case					     =       0

  # Now we train the model
  _snake_case					     =       evaluate.load('''glue''' ,					'''mrpc'''		)
  _snake_case					     =       0
  _snake_case					     =       {}
  for epoch in range(lowerCAmelCase__ ,					lowerCAmelCase__		):
    model.train()
    for step, batch in enumerate(lowerCAmelCase__		):
      _snake_case					     =       model(**lowerCAmelCase__		)
      _snake_case					     =       outputs.loss
      _snake_case					     =       loss / gradient_accumulation_steps
      accelerator.backward(lowerCAmelCase__		)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    _snake_case					     =       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					     =       model(**lowerCAmelCase__		)
      _snake_case					     =       outputs.logits.argmax(dim=-1		)
      # It is slightly faster to call this once, than multiple times
      _snake_case     ,					_snake_case					     =       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					     =       predictions[: len(eval_dataloader.dataset		) - samples_seen]
          _snake_case					     =       references[: len(eval_dataloader.dataset		) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=lowerCAmelCase__ ,					references=lowerCAmelCase__ ,					)

    _snake_case					     =       metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:''' ,					lowerCAmelCase__		)
    _snake_case					     =       eval_metric['''accuracy''']

    if best_performance < eval_metric["accuracy"]:
      _snake_case					     =       eval_metric['''accuracy''']

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir ,					'''all_results.json'''		) ,					'''w'''		) as f:
      json.dump(lowerCAmelCase__ ,					lowerCAmelCase__		)
def 					_UpperCAmelCase      ( )     ->					int:
  _snake_case					     =       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(
      '''--performance_lower_bound''' ,					type=lowerCAmelCase__ ,					default=lowerCAmelCase__ ,					help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' ,					)
  parser.add_argument(
      '''--num_epochs''' ,					type=lowerCAmelCase__ ,					default=3 ,					help='''Number of train epochs.''' ,					)
  _snake_case					     =       parser.parse_args()
  _snake_case					     =       {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
  training_function(lowerCAmelCase__ ,					lowerCAmelCase__		)


if __name__ == "__main__":
 main()







"""simple docstring"""




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 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=14							, _a=7							, _a=True							, _a=True							, _a=True							, _a=True							, _a=True							, _a=99							, _a=32							, _a=5							, _a=4							, _a=37							, _a="gelu"							, _a=0.1							, _a=0.1							, _a=512							, _a=16							, _a=2							, _a=0.02							, _a=3							, _a=4							, _a=None							, ):
				__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 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self      ):
				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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__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 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, *_a      ):
				__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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(CTRLLMHeadModel,) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(
		    {
		        'feature-extraction': CTRLModel,
		        'text-classification': CTRLForSequenceClassification,
		        'text-generation': CTRLLMHeadModel,
		        'zero-shot': CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__UpperCAmelCase		:			Optional[Any]										=				True
		__UpperCAmelCase		:			List[Any]										=				False
		__UpperCAmelCase		:			str										=				False

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a      ):
				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 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, n_embd=37      )

		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self      ):
				pass

		@slow
		def 			__UpperCAmelCase       (  self      ):
				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 			__UpperCAmelCase       (  self      ):
				pass







@require_torch
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLLMHeadModel.from_pretrained('''ctrl'''      )
				model.to(_a      )
				__a           =			torch.tensor(
				    [[11_859, 0, 1_611, 8]]							, dtype=torch.long							, device=_a      )  # Legal the president is
				__a           =			[
				    11_859,
				    0,
				    1_611,
				    8,
				    5,
				    150,
				    26_449,
				    2,
				    19,
				    348,
				    469,
				    3,
				    2_595,
				    48,
				    20_740,
				    246_533,
				    246_533,
				    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      )






