

'''simple docstring'''
import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case     						=						16
_snake_case     						=						32







def       _A	(			snake_case	,							snake_case  )    ->			Optional[int]:
   # Initialize accelerator
   if args.with_tracking:
      _lowercase    :   List[Any]      =     Accelerator(
          cpu=args.cpu	,							mixed_precision=args.mixed_precision	,							log_with="all"	,							project_dir=args.project_dir  )
   else:
      _lowercase    :   List[Any]      =     Accelerator(cpu=args.cpu	,							mixed_precision=args.mixed_precision  )

   if hasattr(args.checkpointing_steps	,							"isdigit"  ):
      if args.checkpointing_steps == "epoch":
         _lowercase    :   List[str]      =     args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         _lowercase    :   str      =     int(args.checkpointing_steps  )
      else:
         raise ValueError(
             F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''  )
   else:
      _lowercase    :   Tuple      =     None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _lowercase    :   str      =     config["lr"]
   _lowercase    :   Optional[Any]      =     int(config["num_epochs"]  )
   _lowercase    :   int      =     int(config["seed"]  )
   _lowercase    :   int      =     int(config["batch_size"]  )

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      _lowercase    :   str      =     os.path.split(snake_case  )[-1].split("."  )[0]
      accelerator.init_trackers(snake_case	,							snake_case  )

   _lowercase    :   str      =     AutoTokenizer.from_pretrained("bert-base-cased"  )
   _lowercase    :   str      =     load_dataset("glue"	,							"mrpc"  )
   _lowercase    :   Optional[int]      =     evaluate.load("glue"	,							"mrpc"  )

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      _lowercase    :   Union[str, Any]      =     datasets.map(
          snake_case	,							batched=snake_case	,							remove_columns=["idx", "sentence1", "sentence2"]	,							)

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

   # If the batch size is too big we use gradient accumulation
   _lowercase    :   Any      =     1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _lowercase    :   int      =     batch_size // MAX_GPU_BATCH_SIZE
      _lowercase    :   Dict      =     MAX_GPU_BATCH_SIZE

   def collate_fn(snake_case  ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _lowercase    :   Optional[Any]      =     1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _lowercase    :   int      =     16
      elif accelerator.mixed_precision != "no":
         _lowercase    :   List[Any]      =     8
      else:
         _lowercase    :   int      =     None

      return tokenizer.pad(
          snake_case	,							padding="longest"	,							max_length=snake_case	,							pad_to_multiple_of=snake_case	,							return_tensors="pt"	,							)

   # Instantiate dataloaders.
   _lowercase    :   Tuple      =     DataLoader(
       tokenized_datasets["train"]	,							shuffle=snake_case	,							collate_fn=snake_case	,							batch_size=snake_case  )
   _lowercase    :   Union[str, Any]      =     DataLoader(
       tokenized_datasets["validation"]	,							shuffle=snake_case	,							collate_fn=snake_case	,							batch_size=snake_case  )

   set_seed(snake_case  )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _lowercase    :   List[Any]      =     AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	,							return_dict=snake_case  )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   _lowercase    :   Optional[Any]      =     model.to(accelerator.device  )

   # Instantiate optimizer
   _lowercase    :   int      =     AdamW(params=model.parameters()	,							lr=snake_case  )

   # Instantiate scheduler
   _lowercase    :   Any      =     get_linear_schedule_with_warmup(
       optimizer=snake_case	,							num_warmup_steps=1_00	,							num_training_steps=(len(snake_case  ) * num_epochs) // gradient_accumulation_steps	,							)

   # 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.
   _lowercase  ,   _lowercase  ,   _lowercase  ,   _lowercase  ,   _lowercase    :   Optional[Any]      =     accelerator.prepare(
       snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case  )

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

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''  )
         accelerator.load_state(args.resume_from_checkpoint  )
         _lowercase    :   str      =     os.path.basename(args.resume_from_checkpoint  )
      else:
         # Get the most recent checkpoint
         _lowercase    :   int      =     [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
         dirs.sort(key=os.path.getctime  )
         _lowercase    :   Union[str, Any]      =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      _lowercase    :   Optional[Any]      =     os.path.splitext(snake_case  )[0]

      if "epoch" in training_difference:
         _lowercase    :   Union[str, Any]      =     int(training_difference.replace("epoch_"	,							""  )  ) + 1
         _lowercase    :   Union[str, Any]      =     None
      else:
         _lowercase    :   int      =     int(training_difference.replace("step_"	,							""  )  )
         _lowercase    :   Optional[int]      =     resume_step // len(snake_case  )
         resume_step -= starting_epoch * len(snake_case  )

    # Now we train the model
   for epoch in range(snake_case	,							snake_case  ):
      model.train()
      if args.with_tracking:
         _lowercase    :   str      =     0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         _lowercase    :   Tuple      =     accelerator.skip_first_batches(snake_case	,							snake_case  )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         _lowercase    :   Any      =     train_dataloader
      for step, batch in enumerate(snake_case  ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device  )
         _lowercase    :   Optional[int]      =     model(**snake_case  )
         _lowercase    :   Optional[int]      =     outputs.loss
         _lowercase    :   Tuple      =     loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(snake_case  )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(snake_case	,							snake_case  ):
            _lowercase    :   Optional[int]      =     F'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  _lowercase    :   Tuple      =     os.path.join(args.output_dir	,							snake_case  )
               accelerator.save_state(snake_case  )

      model.eval()
      for step, batch in enumerate(snake_case  ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device  )
         with torch.no_grad():
            _lowercase    :   List[Any]      =     model(**snake_case  )
         _lowercase    :   Union[str, Any]      =     outputs.logits.argmax(dim=-1  )
         _lowercase  ,   _lowercase    :   Any      =     accelerator.gather_for_metrics((predictions, batch["labels"])  )
         metric.add_batch(
             predictions=snake_case	,							references=snake_case	,							)

      _lowercase    :   List[str]      =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''	,							snake_case  )
      if args.with_tracking:
         accelerator.log(
             {
                 "accuracy": eval_metric["accuracy"],
                 "f1": eval_metric["f1"],
                 "train_loss": total_loss.item() / len(snake_case  ),
                 "epoch": epoch,
             }	,							step=snake_case	,							)

      if checkpointing_steps == "epoch":
         _lowercase    :   Tuple      =     F'''epoch_{epoch}'''
         if args.output_dir is not None:
            _lowercase    :   Optional[int]      =     os.path.join(args.output_dir	,							snake_case  )
         accelerator.save_state(snake_case  )

   if args.with_tracking:
      accelerator.end_training()







def       _A	(			)    ->			Dict:
   _lowercase    :   List[str]      =     argparse.ArgumentParser(description="Simple example of training script."  )
   parser.add_argument(
       "--mixed_precision"	,							type=snake_case	,							default=snake_case	,							choices=["no", "fp16", "bf16", "fp8"]	,							help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU."	,							)
   parser.add_argument("--cpu"	,							action="store_true"	,							help="If passed, will train on the CPU."  )
   parser.add_argument(
       "--checkpointing_steps"	,							type=snake_case	,							default=snake_case	,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."	,							)
   parser.add_argument(
       "--resume_from_checkpoint"	,							type=snake_case	,							default=snake_case	,							help="If the training should continue from a checkpoint folder."	,							)
   parser.add_argument(
       "--with_tracking"	,							action="store_true"	,							help="Whether to load in all available experiment trackers from the environment and use them for logging."	,							)
   parser.add_argument(
       "--output_dir"	,							type=snake_case	,							default="."	,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,							)
   parser.add_argument(
       "--project_dir"	,							type=snake_case	,							default="logs"	,							help="Location on where to store experiment tracking logs` and relevent project information"	,							)
   _lowercase    :   Tuple      =     parser.parse_args()
   _lowercase    :   Any      =     {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(snake_case	,							snake_case  )


if __name__ == "__main__":
    main()









'''simple docstring'''
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor


class      a__						:


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=False    ,       _UpperCamelCase=13    ,       _UpperCamelCase=(32, 128)    ,       _UpperCamelCase=4    ,       _UpperCamelCase=3    ,       _UpperCamelCase=27    ,       _UpperCamelCase=38    ,       _UpperCamelCase=99    ,       _UpperCamelCase=99    ,       _UpperCamelCase=32    ,       _UpperCamelCase=5    ,       _UpperCamelCase=4    ,       _UpperCamelCase=4.0    ,       _UpperCamelCase=257    ,       _UpperCamelCase=None    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     parent
      _lowercase    :   Dict      =     is_training
      _lowercase    :   Optional[Any]      =     batch_size
      _lowercase    :   int      =     image_size
      _lowercase    :   Dict      =     patch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   List[str]      =     max_token_length
      _lowercase    :   int      =     num_character_labels
      _lowercase    :   int      =     num_bpe_labels
      _lowercase    :   int      =     num_wordpiece_labels
      _lowercase    :   Union[str, Any]      =     hidden_size
      _lowercase    :   List[str]      =     num_hidden_layers
      _lowercase    :   int      =     num_attention_heads
      _lowercase    :   Any      =     mlp_ratio
      _lowercase    :   Optional[Any]      =     patch_embeds_hidden_size
      _lowercase    :   Optional[Any]      =     output_hidden_states


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
      _lowercase    :   int      =     self.get_config()
      return config, pixel_values


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return MgpstrConfig(
          image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       max_token_length=self.max_token_length    ,       num_character_labels=self.num_character_labels    ,       num_bpe_labels=self.num_bpe_labels    ,       num_wordpiece_labels=self.num_wordpiece_labels    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       mlp_ratio=self.mlp_ratio    ,       output_hidden_states=self.output_hidden_states    ,       )


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   List[str]      =     MgpstrForSceneTextRecognition(_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      with torch.no_grad():
         _lowercase    :   Tuple      =     model(_UpperCamelCase						)
      self.parent.assertEqual(
          generated_ids[0][0].shape    ,       (self.batch_size, self.max_token_length, self.num_character_labels)						)






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     self.prepare_config_and_inputs()
      _lowercase  ,   _lowercase    :   Optional[int]      =     config_and_inputs
      _lowercase    :   Union[str, Any]      =     {"pixel_values": pixel_values}
      return config, inputs_dict




@require_torch
class      a__						(							lowerCamelCase_						,   lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  str			 =							(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False

   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							False
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							False
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     MgpstrModelTester(self						)
      _lowercase    :   Optional[Any]      =     ConfigTester(self    ,       config_class=_UpperCamelCase    ,       has_text_modality=_UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase						)


   @unittest.skip(reason="MgpstrModel does not use inputs_embeds"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   str      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   Optional[int]      =     model_class(_UpperCamelCase						)
         self.assertIsInstance(model.get_input_embeddings()    ,       (nn.Module)						)
         _lowercase    :   Any      =     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_UpperCamelCase    ,       nn.Linear						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Any      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   int      =     model_class(_UpperCamelCase						)
         _lowercase    :   Union[str, Any]      =     inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowercase    :   Optional[int]      =     [*signature.parameters.keys()]

         _lowercase    :   Optional[Any]      =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]    ,       _UpperCamelCase						)


   @unittest.skip(reason="MgpstrModel does not support feedforward chunking"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   List[str]      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         if not model_class.supports_gradient_checkpointing:
            continue

         _lowercase    :   List[Any]      =     True
         _lowercase    :   int      =     model_class(_UpperCamelCase						)
         self.assertTrue(model.is_gradient_checkpointing						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      def check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						):
         _lowercase    :   Dict      =     model_class(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()

         with torch.no_grad():
            _lowercase    :   Dict      =     model(**self._prepare_for_class(_UpperCamelCase    ,       _UpperCamelCase						)						)

         _lowercase    :   int      =     outputs.hidden_states

         _lowercase    :   Any      =     getattr(
             self.model_tester    ,       "expected_num_hidden_layers"    ,       self.model_tester.num_hidden_layers + 1						)
         self.assertEqual(len(_UpperCamelCase						)    ,       _UpperCamelCase						)

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]						)    ,       [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]    ,       )

      _lowercase  ,   _lowercase    :   Optional[int]      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   Dict      =     True
         check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowercase    :   Dict      =     True

         check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Union[str, Any]      =     self.model_tester.prepare_config_and_inputs_for_common()

      _lowercase    :   str      =     _config_zero_init(_UpperCamelCase						)
      for model_class in self.all_model_classes:
         _lowercase    :   Union[str, Any]      =     model_class(config=_UpperCamelCase						)
         for name, param in model.named_parameters():
            if isinstance(_UpperCamelCase    ,       (nn.Linear, nn.Convad, nn.LayerNorm)						):
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1E9).round() / 1E9).item()    ,       [0.0, 1.0]    ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,       )






   @unittest.skip(reason="Retain_grad is tested in individual model tests"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass







def       _A	(			)    ->			str:
   _lowercase    :   Any      =     "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
   _lowercase    :   Any      =     Image.open(requests.get(snake_case	,							stream=snake_case  ).raw  ).convert("RGB"  )
   return im


@require_vision
@require_torch
class      a__						(							unittest.TestCase						):


   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     "alibaba-damo/mgp-str-base"
      _lowercase    :   Any      =     MgpstrForSceneTextRecognition.from_pretrained(_UpperCamelCase						).to(_UpperCamelCase						)
      _lowercase    :   List[Any]      =     MgpstrProcessor.from_pretrained(_UpperCamelCase						)

      _lowercase    :   Tuple      =     prepare_img()
      _lowercase    :   Any      =     processor(images=_UpperCamelCase    ,       return_tensors="pt"						).pixel_values.to(_UpperCamelCase						)

      # forward pass
      with torch.no_grad():
         _lowercase    :   Tuple      =     model(_UpperCamelCase						)

      # verify the logits
      self.assertEqual(outputs.logits[0].shape    ,       torch.Size((1, 27, 38)						)						)

      _lowercase    :   Tuple      =     processor.batch_decode(outputs.logits						)
      _lowercase    :   Optional[Any]      =     "ticket"

      self.assertEqual(out_strs["generated_text"][0]    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     torch.tensor(
          [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]    ,       device=_UpperCamelCase    ,       )

      self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]    ,       _UpperCamelCase    ,       atol=1E-4						)						)






