




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 lowerCamelCase__  (     _lowercase					,		_lowercase = 16					,		_lowercase = "bert-base-cased" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     AutoTokenizer.from_pretrained(_lowercase )
     UpperCAmelCase_     :				int  =     load_dataset('''glue'''					,		'''mrpc''' )

     def tokenize_function(_lowercase ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_     :				Any  =     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
     UpperCAmelCase_     :				Tuple  =     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
     UpperCAmelCase_     :				Union[str, Any]  =     tokenized_datasets.rename_column('''label'''					,		'''labels''' )

     def collate_fn(_lowercase ):
          # 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.
     UpperCAmelCase_     :				Union[str, Any]  =     DataLoader(
         tokenized_datasets['''train''']					,		shuffle=_lowercase					,		collate_fn=_lowercase					,		batch_size=_lowercase )
     UpperCAmelCase_     :				int  =     DataLoader(
         tokenized_datasets['''validation''']					,		shuffle=_lowercase					,		collate_fn=_lowercase					,		batch_size=_lowercase )

     return train_dataloader, eval_dataloader







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


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

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







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     Accelerator()

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

     set_seed(_lowercase )
     UpperCAmelCase_,       UpperCAmelCase_     :				int  =     get_dataloaders(_lowercase					,		_lowercase					,		_lowercase )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_     :				Optional[Any]  =     AutoModelForSequenceClassification.from_pretrained(_lowercase					,		return_dict=_lowercase )

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

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

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

     if args.partial_train_epoch is not None:
          UpperCAmelCase_     :				Union[str, Any]  =     args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint )
          UpperCAmelCase_     :				Dict  =     args.resume_from_checkpoint.split('''epoch_''' )[1]
          UpperCAmelCase_     :				int  =     ''''''
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          UpperCAmelCase_     :				Union[str, Any]  =     int(_lowercase ) + 1
          UpperCAmelCase_     :				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:
               UpperCAmelCase_     :				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
     UpperCAmelCase_     :				Union[str, Any]  =     {}
     for epoch in range(_lowercase					,		_lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               UpperCAmelCase_     :				List[str]  =     model(**_lowercase )
               UpperCAmelCase_     :				int  =     outputs.loss
               UpperCAmelCase_     :				Optional[int]  =     loss / gradient_accumulation_steps
               accelerator.backward(_lowercase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          UpperCAmelCase_     :				str  =     f'''epoch_{epoch}'''
          UpperCAmelCase_     :				Optional[Any]  =     os.path.join(args.output_dir					,		_lowercase )
          accelerator.save_state(_lowercase )
          UpperCAmelCase_     :				Any  =     evaluation_loop(_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )
          UpperCAmelCase_     :				Union[str, Any]  =     accuracy
          UpperCAmelCase_     :				List[str]  =     lr_scheduler.get_lr()[0]
          UpperCAmelCase_     :				List[Any]  =     optimizer.param_groups[0]['''lr''']
          UpperCAmelCase_     :				Any  =     epoch
          UpperCAmelCase_     :				Any  =     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 lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     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.'''					,		)
     UpperCAmelCase_     :				Union[str, Any]  =     parser.parse_args()
     UpperCAmelCase_     :				Dict  =     {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

     training_function(_lowercase					,		_lowercase )


if __name__ == "__main__":
     main()



'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							tempfile.mkdtemp()

          __A	:       List[Any]      =							[
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          __A	:       Tuple      =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]		)
          with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

          __A	:       List[str]      =							{
              "do_resize": True,
              "size": 20,
              "do_center_crop": True,
              "crop_size": 18,
              "do_normalize": True,
              "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
              "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
          }
          __A	:       Union[str, Any]      =							os.path.join(self.tmpdirname			,     lowerCamelCase		)
          with open(self.image_processor_file			,     "w"			,     encoding="utf-8"		) as fp:
               json.dump(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return BertTokenizer.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return BertTokenizerFast.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          shutil.rmtree(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							[np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta		)]
          __A	:       int      =							[Image.fromarray(np.moveaxis(lowerCamelCase			,     0			,     -1		)		) for x in image_inputs]
          return image_inputs


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.get_tokenizer()
          __A	:       int      =							self.get_rust_tokenizer()
          __A	:       List[str]      =							self.get_image_processor()

          __A	:       List[Any]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)
          processor_slow.save_pretrained(self.tmpdirname		)
          __A	:       List[Any]      =							AlignProcessor.from_pretrained(self.tmpdirname			,     use_fast=lowerCamelCase		)

          __A	:       int      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)
          processor_fast.save_pretrained(self.tmpdirname		)
          __A	:       List[str]      =							AlignProcessor.from_pretrained(self.tmpdirname		)

          self.assertEqual(processor_slow.tokenizer.get_vocab()			,     tokenizer_slow.get_vocab()		)
          self.assertEqual(processor_fast.tokenizer.get_vocab()			,     tokenizer_fast.get_vocab()		)
          self.assertEqual(tokenizer_slow.get_vocab()			,     tokenizer_fast.get_vocab()		)
          self.assertIsInstance(processor_slow.tokenizer			,     lowerCamelCase		)
          self.assertIsInstance(processor_fast.tokenizer			,     lowerCamelCase		)

          self.assertEqual(processor_slow.image_processor.to_json_string()			,     image_processor.to_json_string()		)
          self.assertEqual(processor_fast.image_processor.to_json_string()			,     image_processor.to_json_string()		)
          self.assertIsInstance(processor_slow.image_processor			,     lowerCamelCase		)
          self.assertIsInstance(processor_fast.image_processor			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							AlignProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()		)
          processor.save_pretrained(self.tmpdirname		)

          __A	:       List[str]      =							self.get_tokenizer(bos_token="(BOS)"			,     eos_token="(EOS)"		)
          __A	:       int      =							self.get_image_processor(do_normalize=lowerCamelCase			,     padding_value=1.0		)

          __A	:       Optional[int]      =							AlignProcessor.from_pretrained(
              self.tmpdirname			,     bos_token="(BOS)"			,     eos_token="(EOS)"			,     do_normalize=lowerCamelCase			,     padding_value=1.0		)

          self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer_add_kwargs.get_vocab()		)
          self.assertIsInstance(processor.tokenizer			,     lowerCamelCase		)

          self.assertEqual(processor.image_processor.to_json_string()			,     image_processor_add_kwargs.to_json_string()		)
          self.assertIsInstance(processor.image_processor			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.get_image_processor()
          __A	:       Optional[Any]      =							self.get_tokenizer()

          __A	:       List[str]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       int      =							self.prepare_image_inputs()

          __A	:       Optional[int]      =							image_processor(lowerCamelCase			,     return_tensors="np"		)
          __A	:       Dict      =							processor(images=lowerCamelCase			,     return_tensors="np"		)

          for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							self.get_image_processor()
          __A	:       int      =							self.get_tokenizer()

          __A	:       Optional[int]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       Optional[Any]      =							"lower newer"

          __A	:       Union[str, Any]      =							processor(text=lowerCamelCase		)

          __A	:       int      =							tokenizer(lowerCamelCase			,     padding="max_length"			,     max_length=64		)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,     encoded_processor[key]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.get_image_processor()
          __A	:       Optional[Any]      =							self.get_tokenizer()

          __A	:       int      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       Tuple      =							"lower newer"
          __A	:       int      =							self.prepare_image_inputs()

          __A	:       str      =							processor(text=lowerCamelCase			,     images=lowerCamelCase		)

          self.assertListEqual(list(inputs.keys()		)			,     ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]		)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase		):
               processor()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.get_image_processor()
          __A	:       Tuple      =							self.get_tokenizer()

          __A	:       Dict      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       Optional[int]      =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A	:       Optional[int]      =							processor.batch_decode(lowerCamelCase		)
          __A	:       Optional[int]      =							tokenizer.batch_decode(lowerCamelCase		)

          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							self.get_image_processor()
          __A	:       Union[str, Any]      =							self.get_tokenizer()

          __A	:       Union[str, Any]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       int      =							"lower newer"
          __A	:       str      =							self.prepare_image_inputs()

          __A	:       Tuple      =							processor(text=lowerCamelCase			,     images=lowerCamelCase		)

          self.assertListEqual(list(inputs.keys()		)			,     processor.model_input_names		)

