from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         return yaml.safe_dump(
             {
                 (key.replace("_"	,  "-"		) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }	,  sort_keys=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)




import argparse
import gc
import json
import os

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        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      return int(x / 2**20							)





class 					A							:


   def __enter__(self					:						Dict		)						->    int:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         return self





   def __exit__(self					:						List[str]	,  *__UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         UpperCAmelCase__								=  torch.cuda.max_memory_allocated()
         UpperCAmelCase__								=  bamb(self.end - self.begin		)
         UpperCAmelCase__								=  bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def        lowerCAmelCase_			(					__A,  __A = 16,  __A = "bert-base-cased",  __A = 320,  __A = 160,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      UpperCAmelCase__								=  load_dataset(
          "glue",  "mrpc",  split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""}							)

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

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCAmelCase__								=  datasets.map(
          __A,  batched=__A,  remove_columns=["idx", "sentence1", "sentence2"],  load_from_cache_file=__A							)

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

      def collate_fn(__A							):
            # 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(__A,  padding="max_length",  max_length=128,  return_tensors="pt"							)
            return tokenizer.pad(__A,  padding="longest",  return_tensors="pt"							)

      # Instantiate dataloaders.
      UpperCAmelCase__								=  DataLoader(
          tokenized_datasets["train"],  shuffle=__A,  collate_fn=__A,  batch_size=__A							)
      UpperCAmelCase__								=  DataLoader(
          tokenized_datasets["validation"],  shuffle=__A,  collate_fn=__A,  batch_size=__A							)

      return train_dataloader, eval_dataloader




def        lowerCAmelCase_			(					__A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase__								=  config["lr"]
      UpperCAmelCase__								=  int(config["num_epochs"]							)
      UpperCAmelCase__								=  int(config["seed"]							)
      UpperCAmelCase__								=  int(config["batch_size"]							)
      UpperCAmelCase__								=  args.model_name_or_path

      set_seed(__A							)
      UpperCAmelCase__		,				UpperCAmelCase__								=  get_dataloaders(__A,  __A,  __A,  args.n_train,  args.n_val							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase__								=  AutoModelForSequenceClassification.from_pretrained(__A,  return_dict=__A							)

      # Instantiate optimizer
      UpperCAmelCase__								=  (
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCAmelCase__								=  optimizer_cls(params=model.parameters(),  lr=__A							)

      if accelerator.state.deepspeed_plugin is not None:
            UpperCAmelCase__								=  accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCAmelCase__								=  1
      UpperCAmelCase__								=  (len(__A							) * 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__								=  get_linear_schedule_with_warmup(
                optimizer=__A,  num_warmup_steps=0,  num_training_steps=__A,  )
      else:
            UpperCAmelCase__								=  DummyScheduler(__A,  total_num_steps=__A,  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__								=  accelerator.prepare(
          __A,  __A,  __A,  __A,  __A							)

      # We need to keep track of how many total steps we have iterated over
      UpperCAmelCase__								=  0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCAmelCase__								=  0

      # Now we train the model
      UpperCAmelCase__								=  {}
      for epoch in range(__A,  __A							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(__A							):
                        UpperCAmelCase__								=  model(**__A							)
                        UpperCAmelCase__								=  outputs.loss
                        UpperCAmelCase__								=  loss / gradient_accumulation_steps
                        accelerator.backward(__A							)
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
            accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin							)							)							)
            accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used							)							)
            accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked							)							)
            accelerator.print(
                "Total Peak Memory consumed during the train (max): {}".format(
                    tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
            UpperCAmelCase__								=  tracemalloc.peaked + bamb(tracemalloc.begin							)
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

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




def        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."							)
      parser.add_argument(
          "--model_name_or_path",  type=__A,  default="bert-base-cased",  help="Path to pretrained model or model identifier from huggingface.co/models.",  required=__A,  )
      parser.add_argument(
          "--output_dir",  type=__A,  default=".",  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",  )
      parser.add_argument(
          "--peak_memory_upper_bound",  type=__A,  default=__A,  help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.",  )
      parser.add_argument(
          "--n_train",  type=__A,  default=320,  help="Number of training examples to use.",  )
      parser.add_argument(
          "--n_val",  type=__A,  default=160,  help="Number of validation examples to use.",  )
      parser.add_argument(
          "--num_epochs",  type=__A,  default=1,  help="Number of train epochs.",  )
      UpperCAmelCase__								=  parser.parse_args()
      UpperCAmelCase__								=  {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(__A,  __A							)


if __name__ == "__main__":
      main()



