



"""simple docstring"""
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCAmelCase     =   1_6
_UpperCAmelCase     =   3_2





def      __magic_name__				(							lowercase							,			lowercase = 16							,			lowercase = "bert-base-cased" ):
   SCREAMING_SNAKE_CASE_: int     			=AutoTokenizer.from_pretrained(snake_case__ )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_dataset("""glue"""							,			"""mrpc""" )

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: 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
   SCREAMING_SNAKE_CASE_: Any     			=datasets.map(
       snake_case__							,			batched=snake_case__							,			remove_columns=["""idx""", """sentence1""", """sentence2"""]							,			load_from_cache_file=snake_case__ )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   SCREAMING_SNAKE_CASE_: str     			=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(snake_case__							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(snake_case__							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=DataLoader(
       tokenized_datasets["""train"""]							,			shuffle=snake_case__							,			collate_fn=snake_case__							,			batch_size=snake_case__ )
   SCREAMING_SNAKE_CASE_: int     			=DataLoader(
       tokenized_datasets["""validation"""]							,			shuffle=snake_case__							,			collate_fn=snake_case__							,			batch_size=snake_case__ )

   return train_dataloader, eval_dataloader





def      __magic_name__				(							lowercase							,			lowercase ):
   # Initialize accelerator
   SCREAMING_SNAKE_CASE_: Any     			=Accelerator()

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

   set_seed(snake_case__ )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=get_dataloaders(snake_case__							,			snake_case__							,			snake_case__ )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   SCREAMING_SNAKE_CASE_: Any     			=AutoModelForSequenceClassification.from_pretrained(snake_case__							,			return_dict=snake_case__ )

   # Instantiate optimizer
   SCREAMING_SNAKE_CASE_: str     			=(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   SCREAMING_SNAKE_CASE_: Dict     			=optimizer_cls(params=model.parameters()							,			lr=snake_case__ )

   if accelerator.state.deepspeed_plugin is not None:
      SCREAMING_SNAKE_CASE_: Dict     			=accelerator.state.deepspeed_plugin.deepspeed_config[
          """gradient_accumulation_steps"""
      ]
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=1
   SCREAMING_SNAKE_CASE_: Any     			=(len(snake_case__ ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      SCREAMING_SNAKE_CASE_: str     			=get_linear_schedule_with_warmup(
          optimizer=snake_case__							,			num_warmup_steps=0							,			num_training_steps=snake_case__							,			)
   else:
      SCREAMING_SNAKE_CASE_: Dict     			=DummyScheduler(snake_case__							,			total_num_steps=snake_case__							,			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.
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=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
   SCREAMING_SNAKE_CASE_: int     			=0
   # We also need to keep track of the stating epoch so files are named properly
   SCREAMING_SNAKE_CASE_: Optional[int]     			=0

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

         overall_step += 1

      model.eval()
      SCREAMING_SNAKE_CASE_: List[str]     			=0
      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():
            SCREAMING_SNAKE_CASE_: Any     			=model(**snake_case__ )
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=outputs.logits.argmax(dim=-1 )
         # It is slightly faster to call this once, than multiple times
         SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=accelerator.gather(
             (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
         if accelerator.use_distributed:
            if step == len(snake_case__ ) - 1:
               SCREAMING_SNAKE_CASE_: Tuple     			=predictions[: len(eval_dataloader.dataset ) - samples_seen]
               SCREAMING_SNAKE_CASE_: List[Any]     			=references[: len(eval_dataloader.dataset ) - samples_seen]
            else:
               samples_seen += references.shape[0]
         metric.add_batch(
             predictions=snake_case__							,			references=snake_case__							,			)

      SCREAMING_SNAKE_CASE_: Dict     			=metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''							,			snake_case__ )
      SCREAMING_SNAKE_CASE_: Any     			=eval_metric["""accuracy"""]

      if best_performance < eval_metric["accuracy"]:
         SCREAMING_SNAKE_CASE_: List[Any]     			=eval_metric["""accuracy"""]

   if args.performance_lower_bound is not None:
      assert (
          args.performance_lower_bound <= best_performance
      ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir							,			"""all_results.json""" )							,			"""w""" ) as f:
         json.dump(snake_case__							,			snake_case__ )





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Any     			=argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
   parser.add_argument(
       """--model_name_or_path"""							,			type=snake_case__							,			default="""bert-base-cased"""							,			help="""Path to pretrained model or model identifier from huggingface.co/models."""							,			required=snake_case__							,			)
   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(
       """--performance_lower_bound"""							,			type=snake_case__							,			default=snake_case__							,			help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""							,			)
   parser.add_argument(
       """--num_epochs"""							,			type=snake_case__							,			default=3							,			help="""Number of train epochs."""							,			)
   SCREAMING_SNAKE_CASE_: Any     			=parser.parse_args()
   SCREAMING_SNAKE_CASE_: List[str]     			={"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
   training_function(snake_case__							,			snake_case__ )


if __name__ == "__main__":
   main()








"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



