






"""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
#
########################################################################


UpperCamelCase					=     16
UpperCamelCase					=     32






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       if args.with_tracking:
              lowercase__:							int         =     Accelerator(
                  cpu=args.cpu		,  mixed_precision=args.mixed_precision		,  log_with="all"		,  project_dir=args.project_dir						)
       else:
              lowercase__:							Tuple         =     Accelerator(cpu=args.cpu		,  mixed_precision=args.mixed_precision						)

       if hasattr(args.checkpointing_steps		,  "isdigit"						):
              if args.checkpointing_steps == "epoch":
                     lowercase__:							Any         =     args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     lowercase__:							List[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__:							List[Any]         =     None
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__:							Union[str, Any]         =     config["lr"]
       lowercase__:							int         =     int(config["num_epochs"]						)
       lowercase__:							Dict         =     int(config["seed"]						)
       lowercase__:							Union[str, Any]         =     int(config["batch_size"]						)

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

       lowercase__:							List[Any]         =     AutoTokenizer.from_pretrained("bert-base-cased"						)
       lowercase__:							str         =     load_dataset("glue"		,  "mrpc"						)
       lowercase__:							List[Any]         =     evaluate.load("glue"		,  "mrpc"						)

       def tokenize_function(snake_case_						):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__:							Optional[int]         =     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__:							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__:							Optional[Any]         =     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__:							Tuple         =     batch_size // MAX_GPU_BATCH_SIZE
              lowercase__:							str         =     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__:							List[Any]         =     128 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__:							Dict         =     16
              elif accelerator.mixed_precision != "no":
                     lowercase__:							Optional[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__:							Optional[int]         =     DataLoader(
           tokenized_datasets["train"]		,  shuffle=snake_case_		,  collate_fn=snake_case_		,  batch_size=snake_case_						)
       lowercase__:							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__:							Union[str, 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__:							Union[str, Any]         =     model.to(accelerator.device						)

       # Instantiate optimizer
       lowercase__:							str         =     AdamW(params=model.parameters()		,  lr=snake_case_						)

       # Instantiate scheduler
       lowercase__:							Any         =     get_linear_schedule_with_warmup(
           optimizer=snake_case_		,  num_warmup_steps=100		,  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__:							Union[str, 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__:							List[Any]         =     0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__:							Tuple         =     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__:							Optional[Any]         =     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__:							Tuple         =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              lowercase__:							Any         =     os.path.splitext(snake_case_						)[0]

              if "epoch" in training_difference:
                     lowercase__:							Dict         =     int(training_difference.replace("epoch_"		,  ""						)						) + 1
                     lowercase__:							int         =     None
              else:
                     lowercase__:							Tuple         =     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__:							Optional[Any]         =     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__:							int         =     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__:							str         =     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__:							Union[str, Any]         =     model(**snake_case_						)
                     lowercase__:							List[str]         =     outputs.loss
                     lowercase__:							Optional[int]         =     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__:							int         =     F"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          lowercase__:							List[Any]         =     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__:							Tuple         =     model(**snake_case_						)
                     lowercase__:							int         =     outputs.logits.argmax(dim=-1						)
                     lowercase__    ,      lowercase__:							int         =     accelerator.gather_for_metrics((predictions, batch["labels"])						)
                     metric.add_batch(
                         predictions=snake_case_		,  references=snake_case_		,  )

              lowercase__:							Union[str, Any]         =     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__:							Optional[int]         =     F"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            lowercase__:							List[str]         =     os.path.join(args.output_dir		,  snake_case_						)
                     accelerator.save_state(snake_case_						)

       if args.with_tracking:
              accelerator.end_training()






def 							A(    ):
       """simple docstring"""





       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__:							Union[str, Any]         =     parser.parse_args()
       lowercase__:							Optional[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				,						_lowerCamelCase				,						_lowerCamelCase=False				,						_lowerCamelCase=1_3				,						_lowerCamelCase=(3_2, 1_2_8)				,						_lowerCamelCase=4				,						_lowerCamelCase=3				,						_lowerCamelCase=2_7				,						_lowerCamelCase=3_8				,						_lowerCamelCase=9_9				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=4.0				,						_lowerCamelCase=2_5_7				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			is_training
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			patch_size
      lowercase				       =			num_channels
      lowercase				       =			max_token_length
      lowercase				       =			num_character_labels
      lowercase				       =			num_bpe_labels
      lowercase				       =			num_wordpiece_labels
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			mlp_ratio
      lowercase				       =			patch_embeds_hidden_size
      lowercase				       =			output_hidden_states







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
      lowercase				       =			self.get_config()
      return config, pixel_values







    def      UpperCamelCase_ (     self			):
      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      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			MgpstrForSceneTextRecognition(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)
      self.parent.assertEqual(
          generated_ids[0][0].shape				,						(self.batch_size, self.max_token_length, self.num_character_labels)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase       ,	lowercase				       =			config_and_inputs
      lowercase				       =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
    UpperCAmelCase_					:	Optional[Any]      	={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
    UpperCAmelCase_					:	Union[str, Any]      	=False

    UpperCAmelCase_					:	Optional[Any]      	=False
    UpperCAmelCase_					:	Dict      	=False
    UpperCAmelCase_					:	str      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			MgpstrModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						has_text_modality=_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase			)







    @unittest.skip(reason='MgpstrModel does not use inputs_embeds'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertIsInstance(model.get_input_embeddings()				,						(nn.Module)			)
        lowercase				       =			model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_lowerCamelCase				,						nn.Linear			)			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_lowerCamelCase			)
        lowercase				       =			inspect.signature(model.forward			)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase				       =			[*signature.parameters.keys()]

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







    @unittest.skip(reason='MgpstrModel does not support feedforward chunking'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			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				       =			True
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertTrue(model.is_gradient_checkpointing			)







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			getattr(
            self.model_tester				,						'expected_num_hidden_layers'				,						self.model_tester.num_hidden_layers + 1			)
        self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

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

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase				       =			True

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      lowercase				       =			_config_zero_init(_lowerCamelCase			)
      for model_class in self.all_model_classes:
        lowercase				       =			model_class(config=_lowerCamelCase			)
        for name, param in model.named_parameters():
          if isinstance(_lowerCamelCase				,						(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      UpperCamelCase_ (     self			):
      pass


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       ).convert('RGB'       )
  return im



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







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			'alibaba-damo/mgp-str-base'
      lowercase				       =			MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase			).to(_lowerCamelCase			)
      lowercase				       =			MgpstrProcessor.from_pretrained(_lowerCamelCase			)

      lowercase				       =			prepare_img()
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='pt'			).pixel_values.to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)

      # verify the logits
      self.assertEqual(outputs.logits[0].shape				,						torch.Size((1, 2_7, 3_8)			)			)

      lowercase				       =			processor.batch_decode(outputs.logits			)
      lowercase				       =			'ticket'

      self.assertEqual(out_strs['generated_text'][0]				,						_lowerCamelCase			)

      lowercase				       =			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=_lowerCamelCase				,						)

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


