

"""simple docstring"""




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.generation import (
         FlaxForcedBOSTokenLogitsProcessor,
         FlaxForcedEOSTokenLogitsProcessor,
         FlaxLogitsProcessorList,
         FlaxMinLengthLogitsProcessor,
         FlaxTemperatureLogitsWarper,
         FlaxTopKLogitsWarper,
         FlaxTopPLogitsWarper,
     )


@require_flax
class 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			jnp.ones((batch_size, length)		) / length
         return scores





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			None
         lowerCamelCase__						=			20

         lowerCamelCase__						=			self._get_uniform_logits(batch_size=2    ,						length=SCREAMING_SNAKE_CASE__		)

         # tweak scores to not be uniform anymore
         lowerCamelCase__						=			scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
         lowerCamelCase__						=			scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

         # compute softmax
         lowerCamelCase__						=			jax.nn.softmax(SCREAMING_SNAKE_CASE__    ,						axis=-1		)

         lowerCamelCase__						=			FlaxTemperatureLogitsWarper(temperature=0.5		)
         lowerCamelCase__						=			FlaxTemperatureLogitsWarper(temperature=1.3		)

         lowerCamelCase__						=			jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE__    ,						scores.copy()    ,						cur_len=SCREAMING_SNAKE_CASE__		)    ,						axis=-1		)
         lowerCamelCase__						=			jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE__    ,						scores.copy()    ,						cur_len=SCREAMING_SNAKE_CASE__		)    ,						axis=-1		)

         # uniform distribution stays uniform
         self.assertTrue(jnp.allclose(probs[0, :]    ,						warped_prob_sharp[0, :]    ,						atol=1e-3		)		)
         self.assertTrue(jnp.allclose(probs[0, :]    ,						warped_prob_smooth[0, :]    ,						atol=1e-3		)		)

         # sharp peaks get higher, valleys get lower
         self.assertLess(probs[1, :].max()    ,						warped_prob_sharp[1, :].max()		)
         self.assertGreater(probs[1, :].min()    ,						warped_prob_sharp[1, :].min()		)

         # smooth peaks get lower, valleys get higher
         self.assertGreater(probs[1, :].max()    ,						warped_prob_smooth[1, :].max()		)
         self.assertLess(probs[1, :].min()    ,						warped_prob_smooth[1, :].min()		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			None
         lowerCamelCase__						=			10
         lowerCamelCase__						=			2

         # create ramp distribution
         lowerCamelCase__						=			np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE__		)[None, :]    ,						(batch_size, vocab_size)		).copy()
         lowerCamelCase__						=			ramp_logits[1:, : vocab_size // 2] + vocab_size

         lowerCamelCase__						=			FlaxTopKLogitsWarper(3		)

         lowerCamelCase__						=			top_k_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)

         # check that correct tokens are filtered
         self.assertListEqual(jnp.isinf(scores[0]		).tolist()    ,						7 * [True] + 3 * [False]		)
         self.assertListEqual(jnp.isinf(scores[1]		).tolist()    ,						2 * [True] + 3 * [False] + 5 * [True]		)

         # check special case
         lowerCamelCase__						=			5
         lowerCamelCase__						=			FlaxTopKLogitsWarper(top_k=1    ,						filter_value=0.0    ,						min_tokens_to_keep=3		)

         lowerCamelCase__						=			np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE__		)[None, :]    ,						(batch_size, length)		).copy()
         lowerCamelCase__						=			top_k_warp_safety_check(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)

         # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
         self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()    ,						[2, 2]		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			None
         lowerCamelCase__						=			10
         lowerCamelCase__						=			2

         # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
         lowerCamelCase__						=			np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]		)		)

         lowerCamelCase__						=			FlaxTopPLogitsWarper(0.8		)
         lowerCamelCase__						=			np.exp(top_p_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)		)

         # dist should be filtered to keep min num values so that sum is >= top_p
         # exp (-inf) => 0
         lowerCamelCase__						=			np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]		)
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-3		)		)

         # check edge cases with negative and extreme logits
         lowerCamelCase__						=			np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE__		)[None, :]    ,						(batch_size, vocab_size)		).copy() - (
             vocab_size // 2
         )

         # make ramp_logits more extreme
         lowerCamelCase__						=			ramp_logits[1] * 1_00.0

         # make sure at least 2 tokens are kept
         lowerCamelCase__						=			FlaxTopPLogitsWarper(0.9    ,						min_tokens_to_keep=2    ,						filter_value=0.0		)
         lowerCamelCase__						=			top_p_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)

         # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()    ,						[3, 2]		)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			20
         lowerCamelCase__						=			4
         lowerCamelCase__						=			0

         lowerCamelCase__						=			FlaxMinLengthLogitsProcessor(min_length=10    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)

         # check that min length is applied at length 5
         lowerCamelCase__						=			ids_tensor((batch_size, 20)    ,						vocab_size=20		)
         lowerCamelCase__						=			5
         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			min_dist_processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()    ,						4 * [-float("inf"		)]		)

         # check that min length is not applied anymore at length 15
         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			15
         lowerCamelCase__						=			min_dist_processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE__		).any()		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			20
         lowerCamelCase__						=			4
         lowerCamelCase__						=			0

         lowerCamelCase__						=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE__		)

         # check that all scores are -inf except the bos_token_id score
         lowerCamelCase__						=			ids_tensor((batch_size, 1)    ,						vocab_size=20		)
         lowerCamelCase__						=			1
         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			logits_processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
         self.assertListEqual(scores[:, bos_token_id].tolist()    ,						4 * [0]		)  # score for bos_token_id shold be zero

         # check that bos_token_id is not forced if current length is greater than 1
         lowerCamelCase__						=			3
         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			logits_processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE__		).any()		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			20
         lowerCamelCase__						=			4
         lowerCamelCase__						=			0
         lowerCamelCase__						=			5

         lowerCamelCase__						=			FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE__    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)

         # check that all scores are -inf except the eos_token_id when max_length is reached
         lowerCamelCase__						=			ids_tensor((batch_size, 4)    ,						vocab_size=20		)
         lowerCamelCase__						=			4
         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			logits_processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
         self.assertListEqual(scores[:, eos_token_id].tolist()    ,						4 * [0]		)  # score for eos_token_id should be zero

         # check that eos_token_id is not forced if max_length is not reached
         lowerCamelCase__						=			3
         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			logits_processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE__		).any()		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			4
         lowerCamelCase__						=			10
         lowerCamelCase__						=			15
         lowerCamelCase__						=			2
         lowerCamelCase__						=			1
         lowerCamelCase__						=			15

         # dummy input_ids and scores
         lowerCamelCase__						=			ids_tensor((batch_size, sequence_length)    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			input_ids.copy()

         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			scores.copy()

         # instantiate all dist processors
         lowerCamelCase__						=			FlaxTemperatureLogitsWarper(temperature=0.5		)
         lowerCamelCase__						=			FlaxTopKLogitsWarper(3		)
         lowerCamelCase__						=			FlaxTopPLogitsWarper(0.8		)

         # instantiate all logits processors
         lowerCamelCase__						=			FlaxMinLengthLogitsProcessor(min_length=10    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE__    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			10

         # no processor list
         lowerCamelCase__						=			temp_dist_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			top_k_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			top_p_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			min_dist_proc(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			bos_dist_proc(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			eos_dist_proc(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)

         # with processor list
         lowerCamelCase__						=			FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
         lowerCamelCase__						=			processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)

         # scores should be equal
         self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-3		)		)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()    ,						input_ids_comp.tolist()		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			4
         lowerCamelCase__						=			10
         lowerCamelCase__						=			15
         lowerCamelCase__						=			2
         lowerCamelCase__						=			1
         lowerCamelCase__						=			15

         # dummy input_ids and scores
         lowerCamelCase__						=			ids_tensor((batch_size, sequence_length)    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			input_ids.copy()

         lowerCamelCase__						=			self._get_uniform_logits(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			scores.copy()

         # instantiate all dist processors
         lowerCamelCase__						=			FlaxTemperatureLogitsWarper(temperature=0.5		)
         lowerCamelCase__						=			FlaxTopKLogitsWarper(3		)
         lowerCamelCase__						=			FlaxTopPLogitsWarper(0.8		)

         # instantiate all logits processors
         lowerCamelCase__						=			FlaxMinLengthLogitsProcessor(min_length=10    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE__    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			10

         # no processor list
         def run_no_processor_list(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			temp_dist_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			top_k_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			top_p_warp(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			min_dist_proc(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			bos_dist_proc(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			eos_dist_proc(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            return scores

         # with processor list
         def run_processor_list(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
            lowerCamelCase__						=			processor(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						cur_len=SCREAMING_SNAKE_CASE__		)
            return scores

         lowerCamelCase__						=			jax.jit(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			jax.jit(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			jitted_run_no_processor_list(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			jitted_run_processor_list(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # scores should be equal
         self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-3		)		)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()    ,						input_ids_comp.tolist()		)







"""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()




