

"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
     from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			{
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}


class 		_A       (			__a					):
      __a								=	'bloom'
      __a								=	['past_key_values']
      __a								=	{
          'num_hidden_layers': 'n_layer',
          'num_attention_heads': 'n_head',
      }





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=250880    ,						SCREAMING_SNAKE_CASE__=64    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=1e-5    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=1    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=1    ,						SCREAMING_SNAKE_CASE__=False    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Union[str, Any]:
         lowerCamelCase__						=			vocab_size
         # Backward compatibility with n_embed kwarg
         lowerCamelCase__						=			kwargs.pop("n_embed"    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			hidden_size if n_embed is None else n_embed
         lowerCamelCase__						=			n_layer
         lowerCamelCase__						=			n_head
         lowerCamelCase__						=			layer_norm_epsilon
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			use_cache
         lowerCamelCase__						=			pretraining_tp
         lowerCamelCase__						=			apply_residual_connection_post_layernorm
         lowerCamelCase__						=			hidden_dropout
         lowerCamelCase__						=			attention_dropout

         lowerCamelCase__						=			bos_token_id
         lowerCamelCase__						=			eos_token_id
         lowerCamelCase__						=			slow_but_exact

         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE__    ,						eos_token_id=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)






class 		_A       (			__a					):
      __a								=	version.parse('1.12'					)





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = "default"    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = False    ,						)   ->					str:
         super().__init__(SCREAMING_SNAKE_CASE__    ,						task=SCREAMING_SNAKE_CASE__    ,						patching_specs=SCREAMING_SNAKE_CASE__    ,						use_past=SCREAMING_SNAKE_CASE__		)
         if not getattr(self._config    ,						"pad_token_id"    ,						SCREAMING_SNAKE_CASE__		):
            # TODO: how to do that better?
            lowerCamelCase__						=			0





      @property
      def 					_lowerCamelCase  (  self		)   ->					Mapping[str, Mapping[int, str]]:
         lowerCamelCase__						=			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
         if self.use_past:
            # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
            self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__    ,						direction="inputs"    ,						inverted_values_shape=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			{0: "batch", 1: "past_sequence + sequence"}
         else:
            lowerCamelCase__						=			{0: "batch", 1: "sequence"}

         return common_inputs





      @property
      def 					_lowerCamelCase  (  self		)   ->					int:
         return self._config.n_layer





      @property
      def 					_lowerCamelCase  (  self		)   ->					int:
         return self._config.n_head





      @property
      def 					_lowerCamelCase  (  self		)   ->					float:
         return 1e-3





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = -1    ,						SCREAMING_SNAKE_CASE__ = -1    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = None    ,						)   ->					Mapping[str, Any]:
         lowerCamelCase__						=			super(SCREAMING_SNAKE_CASE__    ,						self		).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE__    ,						batch_size=SCREAMING_SNAKE_CASE__    ,						seq_length=SCREAMING_SNAKE_CASE__    ,						is_pair=SCREAMING_SNAKE_CASE__    ,						framework=SCREAMING_SNAKE_CASE__		)

         # We need to order the input in the way they appears in the forward()
         lowerCamelCase__						=			OrderedDict({"input_ids": common_inputs["input_ids"]}		)

         # Need to add the past_keys
         if self.use_past:
            if not is_torch_available():
               raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
            else:
               import torch

               lowerCamelCase__					,       lowerCamelCase__						=			common_inputs["input_ids"].shape
               # Not using the same length for past_key_values
               lowerCamelCase__						=			seqlen + 2
               lowerCamelCase__						=			self._config.hidden_size // self.num_attention_heads
               lowerCamelCase__						=			(
                   batch * self.num_attention_heads,
                   head_dim,
                   past_key_values_length,
               )
               lowerCamelCase__						=			(
                   batch * self.num_attention_heads,
                   past_key_values_length,
                   head_dim,
               )
               lowerCamelCase__						=			[
                   (torch.zeros(SCREAMING_SNAKE_CASE__		), torch.zeros(SCREAMING_SNAKE_CASE__		)) for _ in range(self.num_layers		)
               ]

         lowerCamelCase__						=			common_inputs["attention_mask"]
         if self.use_past:
            lowerCamelCase__						=			ordered_inputs["attention_mask"].dtype
            lowerCamelCase__						=			torch.cat(
                [ordered_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						dtype=SCREAMING_SNAKE_CASE__		)]    ,						dim=1		)

         return ordered_inputs





      @property
      def 					_lowerCamelCase  (  self		)   ->					int:
         return 13







"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			int	  =			16
SCREAMING_SNAKE_CASE_     :			int	  =			32



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   return int(x / 2**20   )


class 		_A       :





      def __enter__(  self		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         return self





      def __exit__(  self    ,						*SCREAMING_SNAKE_CASE__		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         lowerCamelCase__						=			bamb(self.end - self.begin		)
         lowerCamelCase__						=			bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	UpperCAmelCase__   (				A__	,    A__ = 16	,    A__ = "bert-base-cased"	,    A__ = 320	,    A__ = 160	,    )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			AutoTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			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)
      lowerCamelCase__						=			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
   lowerCamelCase__						=			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
   lowerCamelCase__						=			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.
   lowerCamelCase__						=			DataLoader(
       tokenized_datasets["train"]	,    shuffle=A__	,    collate_fn=A__	,    batch_size=A__   )
   lowerCamelCase__						=			DataLoader(
       tokenized_datasets["validation"]	,    shuffle=A__	,    collate_fn=A__	,    batch_size=A__   )

   return train_dataloader, eval_dataloader



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   # Initialize accelerator
   lowerCamelCase__						=			Accelerator()

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

   set_seed(A__   )
   lowerCamelCase__					,       lowerCamelCase__						=			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)
   lowerCamelCase__						=			AutoModelForSequenceClassification.from_pretrained(A__	,    return_dict=A__   )

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

   if accelerator.state.deepspeed_plugin is not None:
      lowerCamelCase__						=			accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      lowerCamelCase__						=			1
   lowerCamelCase__						=			(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
   ):
      lowerCamelCase__						=			get_linear_schedule_with_warmup(
          optimizer=A__	,    num_warmup_steps=0	,    num_training_steps=A__	,    )
   else:
      lowerCamelCase__						=			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.
   lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
       A__	,    A__	,    A__	,    A__	,    A__   )

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

   # Now we train the model
   lowerCamelCase__						=			{}
   for epoch in range(A__	,    A__   ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(A__   ):
            lowerCamelCase__						=			model(**A__   )
            lowerCamelCase__						=			outputs.loss
            lowerCamelCase__						=			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   )   )   )
      lowerCamelCase__						=			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 	UpperCAmelCase__   (				)      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			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."	,    )
   lowerCamelCase__						=			parser.parse_args()
   lowerCamelCase__						=			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(A__	,    A__   )


if __name__ == "__main__":
     main()




