



"""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__              =							16
lowerCAmelCase__              =							32






def 			snake_case_    ( A_   :						Accelerator,							A_   :						int = 16,							A_   :						str = "bert-base-cased"		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					AutoTokenizer.from_pretrained(A_		)
       _lowerCamelCase		:							Tuple            =					load_dataset('''glue''',							'''mrpc'''		)

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

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

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

       def collate_fn(A_   :						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(A_,							padding='''max_length''',							max_length=1_28,							return_tensors='''pt'''		)
              return tokenizer.pad(A_,							padding='''longest''',							return_tensors='''pt'''		)

       # Instantiate dataloaders.
       _lowerCamelCase		:							Optional[Any]            =					DataLoader(
           tokenized_datasets['''train'''],							shuffle=A_,							collate_fn=A_,							batch_size=A_		)
       _lowerCamelCase		:							int            =					DataLoader(
           tokenized_datasets['''validation'''],							shuffle=A_,							collate_fn=A_,							batch_size=A_		)

       return train_dataloader, eval_dataloader






def 			snake_case_    ( A_   :						List[str],							A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							Union[str, Any]            =					Accelerator()

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

       set_seed(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					get_dataloaders(A_,							A_,							A_		)

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

       # Instantiate optimizer
       _lowerCamelCase		:							Optional[Any]            =					(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       _lowerCamelCase		:							str            =					optimizer_cls(params=model.parameters(),							lr=A_		)

       if accelerator.state.deepspeed_plugin is not None:
              _lowerCamelCase		:							Any            =					accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              _lowerCamelCase		:							Union[str, Any]            =					1
       _lowerCamelCase		:							Tuple            =					(len(A_		) * num_epochs) // gradient_accumulation_steps

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

       # We need to keep track of how many total steps we have iterated over
       _lowerCamelCase		:							Union[str, Any]            =					0
       # We also need to keep track of the stating epoch so files are named properly
       _lowerCamelCase		:							Tuple            =					0

       # Now we train the model
       _lowerCamelCase		:							int            =					evaluate.load('''glue''',							'''mrpc'''		)
       _lowerCamelCase		:							Union[str, Any]            =					0
       _lowerCamelCase		:							List[str]            =					{}
       for epoch in range(A_,							A_		):
              model.train()
              for step, batch in enumerate(A_		):
                     _lowerCamelCase		:							Tuple            =					model(**A_		)
                     _lowerCamelCase		:							List[str]            =					outputs.loss
                     _lowerCamelCase		:							Any            =					loss / gradient_accumulation_steps
                     accelerator.backward(A_		)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

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

              _lowerCamelCase		:							Optional[Any]            =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:''',							A_		)
              _lowerCamelCase		:							Optional[Any]            =					eval_metric['''accuracy''']

              if best_performance < eval_metric["accuracy"]:
                     _lowerCamelCase		:							Optional[int]            =					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(A_,							A_		)






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[Any]            =					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
       parser.add_argument(
           '''--model_name_or_path''',							type=A_,							default='''bert-base-cased''',							help='''Path to pretrained model or model identifier from huggingface.co/models.''',							required=A_,							)
       parser.add_argument(
           '''--output_dir''',							type=A_,							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=A_,							default=A_,							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=A_,							default=3,							help='''Number of train epochs.''',							)
       _lowerCamelCase		:							Tuple            =					parser.parse_args()
       _lowerCamelCase		:							str            =					{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(A_,							A_		)


if __name__ == "__main__":
   main()










import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[str]=13			,						UpperCamelCase_							:      int=7			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      str=False			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      Union[str, Any]=99			,						UpperCamelCase_							:      Any=32			,						UpperCamelCase_							:      Union[str, Any]=5			,						UpperCamelCase_							:      List[str]=4			,						UpperCamelCase_							:      Union[str, Any]=64			,						UpperCamelCase_							:      Any="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      List[Any]=0.1			,						UpperCamelCase_							:      int=512			,						UpperCamelCase_							:      Dict=16			,						UpperCamelCase_							:      List[str]=2			,						UpperCamelCase_							:      int=0.02			,						UpperCamelCase_							:      Dict=3			,						UpperCamelCase_							:      List[Any]=4			,						UpperCamelCase_							:      int=None			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      Optional[Any]=2			,						UpperCamelCase_							:      str=4			,						UpperCamelCase_							:      List[str]=1			,						):


         """simple docstring"""



         __A       =						parent
         __A       =						batch_size
         __A       =						seq_length
         __A       =						is_training
         __A       =						use_input_mask
         __A       =						use_token_type_ids
         __A       =						use_labels
         __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       =						q_groups
         __A       =						k_groups
         __A       =						v_groups
         __A       =						post_attention_groups
         __A       =						intermediate_groups
         __A       =						output_groups




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __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
         __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()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return SqueezeBertConfig(
             embedding_size=self.hidden_size			,						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			,						attention_probs_dropout_prob=self.hidden_dropout_prob			,						attention_dropout=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						q_groups=self.q_groups			,						k_groups=self.k_groups			,						v_groups=self.v_groups			,						post_attention_groups=self.post_attention_groups			,						intermediate_groups=self.intermediate_groups			,						output_groups=self.output_groups			,						)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						SqueezeBertModel(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForMaskedLM(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						SqueezeBertForQuestionAnswering(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						start_positions=UpperCamelCase_			,						end_positions=UpperCamelCase_					)
         self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)					)
         self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForTokenClassification(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()

         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						self.num_choices
         __A       =						SqueezeBertForMultipleChoice(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						input_ids.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						input_mask.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_			,						)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_choices)					)





     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						self.prepare_config_and_inputs()
         ((__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A))       =						config_and_inputs
         __A       =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


@require_torch
class    __lowercase   (						lowercase_      ,   lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     SCREAMING_SNAKE_CASE     =					(
         {
             "feature-extraction": SqueezeBertModel,
             "fill-mask": SqueezeBertForMaskedLM,
             "question-answering": SqueezeBertForQuestionAnswering,
             "text-classification": SqueezeBertForSequenceClassification,
             "token-classification": SqueezeBertForTokenClassification,
             "zero-shot": SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						SqueezeBertModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						dim=37					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_model(*UpperCamelCase_					)




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


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCamelCase_					)




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


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCamelCase_					)




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


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCamelCase_					)





     @slow
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A       =						SqueezeBertModel.from_pretrained(UpperCamelCase_					)
             self.assertIsNotNone(UpperCamelCase_					)


@require_sentencepiece
@require_tokenizers
@require_torch
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""					)

         __A       =						torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]					)
         __A       =						model(UpperCamelCase_					)[0]
         __A       =						torch.Size((1, 3)					)
         self.assertEqual(output.shape			,						UpperCamelCase_					)
         __A       =						torch.tensor([[0.6401, -0.0349, -0.6041]]					)
         self.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-4					)					)




