import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Tuple:
         return None







class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Dict:
         return None







class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				[
      # (model_name, model_kwargs)
      ("bert-base-cased", {}),
      ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
  ]







  @require_tf
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Dict:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                self._test_export(SCREAMING_SNAKE_CASE__      ,	'tf'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                self._test_export(SCREAMING_SNAKE_CASE__      ,	'pt'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         from transformers import BertModel

         A						:     Union[str, Any]         =['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
         with NamedTemporaryFile(mode='w+t'     ) as vocab_file:
                vocab_file.write('\n'.join(SCREAMING_SNAKE_CASE__     )     )
                vocab_file.flush()
                A						:     Optional[Any]         =BertTokenizerFast(vocab_file.name     )

         with TemporaryDirectory() as bert_save_dir:
                A						:     Any         =BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE__     )     )     )
                model.save_pretrained(SCREAMING_SNAKE_CASE__     )
                self._test_export(SCREAMING_SNAKE_CASE__      ,	'pt'      ,	12      ,	SCREAMING_SNAKE_CASE__     )







  @require_tf
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Any:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                A						:     Optional[int]         =self._test_export(SCREAMING_SNAKE_CASE__      ,	'tf'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )
                A						:     Dict         =quantize(Path(SCREAMING_SNAKE_CASE__     )     )

                # Ensure the actual quantized model is not bigger than the original one
                if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE__     ).stat().st_size:
                       self.fail('Quantized model is bigger than initial ONNX model'     )







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Dict:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                A						:     Optional[Any]         =self._test_export(SCREAMING_SNAKE_CASE__      ,	'pt'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )
                A						:     int         =quantize(SCREAMING_SNAKE_CASE__     )

                # Ensure the actual quantized model is not bigger than the original one
                if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE__     ).stat().st_size:
                       self.fail('Quantized model is bigger than initial ONNX model'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	**SCREAMING_SNAKE_CASE__    :					Any     )    ->				int:
         try:
                # Compute path
                with TemporaryDirectory() as tempdir:
                       A						:     Union[str, Any]         =Path(SCREAMING_SNAKE_CASE__     ).joinpath('model.onnx'     )

                # Remove folder if exists
                if path.parent.exists():
                       path.parent.rmdir()

                # Export
                convert(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

                return path
         except Exception as e:
                self.fail(SCREAMING_SNAKE_CASE__     )







  @require_torch
  @require_tokenizers
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[Any]:
         from transformers import BertModel

         A						:     Union[str, Any]         =BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'     )     )
         A						:     Optional[int]         =BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'     )
         self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'pt'     )







  @require_tf
  @require_tokenizers
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[Any]:
         from transformers import TFBertModel

         A						:     str         =TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'     )     )
         A						:     List[str]         =BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'     )
         self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'tf'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Tuple:
         A						:     Any         =FeatureExtractionPipeline(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     int         =['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
         A  ,  A  ,  A  ,  A						:     Any         =infer_shapes(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         # Assert all variables are present
         self.assertEqual(len(SCREAMING_SNAKE_CASE__     )      ,	len(SCREAMING_SNAKE_CASE__     )     )
         self.assertTrue(all(var_name in shapes for var_name in variable_names     )     )
         self.assertSequenceEqual(variable_names[:3]      ,	SCREAMING_SNAKE_CASE__     )
         self.assertSequenceEqual(variable_names[3:]      ,	SCREAMING_SNAKE_CASE__     )

         # Assert inputs are {0: batch, 1: sequence}
         for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
                self.assertDictEqual(shapes[var_name]      ,	{0: 'batch', 1: 'sequence'}     )

         # Assert outputs are {0: batch, 1: sequence} and {0: batch}
         self.assertDictEqual(shapes['output_0']      ,	{0: 'batch', 1: 'sequence'}     )
         self.assertDictEqual(shapes['output_1']      ,	{0: 'batch'}     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         A						:     Dict         =['input_ids', 'attention_mask', 'token_type_ids']
         A						:     Optional[Any]         ={'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
         A  ,  A						:     Optional[int]         =ensure_valid_input(FuncContiguousArgs()      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         # Should have exactly the same number of args (all are valid)
         self.assertEqual(len(SCREAMING_SNAKE_CASE__     )      ,	3     )

         # Should have exactly the same input names
         self.assertEqual(set(SCREAMING_SNAKE_CASE__     )      ,	set(SCREAMING_SNAKE_CASE__     )     )

         # Parameter should be reordered according to their respective place in the function:
         # (input_ids, token_type_ids, attention_mask)
         self.assertEqual(SCREAMING_SNAKE_CASE__      ,	(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])     )

         # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
         A  ,  A						:     List[Any]         =ensure_valid_input(FuncNonContiguousArgs()      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         # Should have exactly the one arg (all before the one not provided "some_other_args")
         self.assertEqual(len(SCREAMING_SNAKE_CASE__     )      ,	1     )
         self.assertEqual(len(SCREAMING_SNAKE_CASE__     )      ,	1     )

         # Should have only "input_ids"
         self.assertEqual(inputs_args[0]      ,	tokens['input_ids']     )
         self.assertEqual(ordered_input_names[0]      ,	'input_ids'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[str]:
         A						:     str         =generate_identified_filename(Path('/home/something/my_fake_model.onnx'     )      ,	'-test'     )
         self.assertEqual('/home/something/my_fake_model-test.onnx'      ,	generated.as_posix()     )







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


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

       def tokenize_function(lowercase:     Any					):
              # max_length=None => use the model max length (it's actually the default)
              A						:     List[str]         =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						:     Any         =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						:     Dict         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     Optional[int]					):
              # 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=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =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						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, 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(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

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

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

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

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(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						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =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						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

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

       if args.partial_train_epoch is not None:
              A						:     Dict         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     List[Any]         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     List[Any]         =''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     Union[str, Any]         =int(lowercase					) + 1
              A						:     List[str]         =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						:     Union[str, Any]         =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						:     str         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Tuple         =model(**lowercase					)
                     A						:     List[Any]         =outputs.loss
                     A						:     Any         =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						:     Union[str, Any]         =F'epoch_{epoch}'
              A						:     Optional[Any]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[Any]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Dict         =accuracy
              A						:     Optional[Any]         =lr_scheduler.get_lr()[0]
              A						:     Any         =optimizer.param_groups[0]['lr']
              A						:     str         =epoch
              A						:     Dict         =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__  (			)				->		Optional[int]:
       A						:     Optional[int]         =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						:     str         =parser.parse_args()
       A						:     Optional[int]         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






