


'''simple docstring'''



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	__lowercase	:						Optional[Any]       =	'platform'

	import jax
	import jax.numpy as jnp

	from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
	    FlaxBlenderbotSmallForConditionalGeneration,
	    FlaxBlenderbotSmallModel,
	    shift_tokens_right,
	)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]=None						,      _SCREAMING_SNAKE_CASE      :   Any=None						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]=None						,      _SCREAMING_SNAKE_CASE      :   List[Any]=None						,      _SCREAMING_SNAKE_CASE      :   List[Any]=None						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]=None						,      ):
					if attention_mask is None:
										__a			:       Union[str, Any]					    =  np.where(input_ids != config.pad_token_id						,      1						,      0   )
					if decoder_attention_mask is None:
										__a			:       List[Any]					    =  np.where(decoder_input_ids != config.pad_token_id						,      1						,      0   )
					if head_mask is None:
										__a			:       Any					    =  np.ones((config.encoder_layers, config.encoder_attention_heads)   )
					if decoder_head_mask is None:
										__a			:       Union[str, Any]					    =  np.ones((config.decoder_layers, config.decoder_attention_heads)   )
					if cross_attn_head_mask is None:
										__a			:       Union[str, Any]					    =  np.ones((config.decoder_layers, config.decoder_attention_heads)   )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					}





class 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a=13						,				__a=7						,				__a=True						,				__a=False						,				__a=99						,				__a=16						,				__a=2						,				__a=4						,				__a=4						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=32						,				__a=2						,				__a=1						,				__a=0						,				__a=0.02						,				):

						'''simple docstring'''





						__a			:       Dict					    =  parent
						__a			:       str					    =  batch_size
						__a			:       Tuple					    =  seq_length
						__a			:       str					    =  is_training
						__a			:       Optional[Any]					    =  use_labels
						__a			:       List[Any]					    =  vocab_size
						__a			:       Any					    =  hidden_size
						__a			:       Dict					    =  num_hidden_layers
						__a			:       int					    =  num_attention_heads
						__a			:       Optional[int]					    =  intermediate_size
						__a			:       Tuple					    =  hidden_act
						__a			:       List[Any]					    =  hidden_dropout_prob
						__a			:       Optional[int]					    =  attention_probs_dropout_prob
						__a			:       Optional[int]					    =  max_position_embeddings
						__a			:       Any					    =  eos_token_id
						__a			:       str					    =  pad_token_id
						__a			:       Union[str, Any]					    =  bos_token_id
						__a			:       Dict					    =  initializer_range




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,				self.vocab_size							)						,				3						,				self.vocab_size							)
						__a			:       Optional[Any]					    =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,				dtype=np.intaa							))						,				-1							)

						__a			:       int					    =  shift_tokens_right(__a						,				1						,				2							)

						__a			:       Union[str, Any]					    =  BlenderbotSmallConfig(
						    vocab_size=self.vocab_size						,				d_model=self.hidden_size						,				encoder_layers=self.num_hidden_layers						,				decoder_layers=self.num_hidden_layers						,				encoder_attention_heads=self.num_attention_heads						,				decoder_attention_heads=self.num_attention_heads						,				encoder_ffn_dim=self.intermediate_size						,				decoder_ffn_dim=self.intermediate_size						,				dropout=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				max_position_embeddings=self.max_position_embeddings						,				eos_token_id=self.eos_token_id						,				bos_token_id=self.bos_token_id						,				pad_token_id=self.pad_token_id						,				initializer_range=self.initializer_range						,				use_cache=__a						,				)
						__a			:       str					    =  prepare_blenderbot_inputs_dict(__a						,				__a						,				__a							)
						return config, inputs_dict




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       Union[str, Any]					    =  self.prepare_config_and_inputs()
						return config, inputs_dict




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       int					    =  20
						__a			:       Any					    =  model_class_name(__a							)

						__a			:       Union[str, Any]					    =  model.encode(inputs_dict['input_ids']							)

						__a    ,				__a			:       Union[str, Any]					    =  (
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						__a			:       Any					    =  model.init_cache(decoder_input_ids.shape[0]						,				__a						,				__a							)
						__a			:       Optional[Any]					    =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,				dtype='i4'							)

						__a			:       Tuple					    =  jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]						,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,				)
						__a			:       str					    =  model.decode(
						    decoder_input_ids[:, :-1]						,				__a						,				decoder_attention_mask=__a						,				past_key_values=__a						,				decoder_position_ids=__a						,				)

						__a			:       Dict					    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,				dtype='i4'							)
						__a			:       Optional[Any]					    =  model.decode(
						    decoder_input_ids[:, -1:]						,				__a						,				decoder_attention_mask=__a						,				past_key_values=outputs_cache.past_key_values						,				decoder_position_ids=__a						,				)

						__a			:       List[str]					    =  model.decode(__a						,				__a							)

						__a			:       Optional[int]					    =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
						self.parent.assertTrue(diff < 1E-3						,				msg=f"""Max diff is {diff}"""							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  20
						__a			:       Optional[Any]					    =  model_class_name(__a							)

						__a			:       Optional[Any]					    =  model.encode(inputs_dict['input_ids']							)

						__a    ,				__a			:       List[str]					    =  (
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						__a			:       Tuple					    =  jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
						    ]						,				axis=-1						,				)

						__a			:       Tuple					    =  model.init_cache(decoder_input_ids.shape[0]						,				__a						,				__a							)
						__a			:       List[Any]					    =  jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]						,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,				)

						__a			:       Union[str, Any]					    =  model.decode(
						    decoder_input_ids[:, :-1]						,				__a						,				decoder_attention_mask=__a						,				past_key_values=__a						,				decoder_position_ids=__a						,				)
						__a			:       Dict					    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,				dtype='i4'							)
						__a			:       int					    =  model.decode(
						    decoder_input_ids[:, -1:]						,				__a						,				past_key_values=outputs_cache.past_key_values						,				decoder_attention_mask=__a						,				decoder_position_ids=__a						,				)

						__a			:       int					    =  model.decode(__a						,				__a						,				decoder_attention_mask=__a							)

						__a			:       List[Any]					    =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
						self.parent.assertTrue(diff < 1E-3						,				msg=f"""Max diff is {diff}"""							)





@require_flax
class 							__UpperCamelCase				(		unittest.TestCase					):
	A_				=    99




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  np.array(
						    [
						        [71, 82, 18, 33, 46, 91, 2],
						        [68, 34, 26, 58, 30, 82, 2],
						        [5, 97, 17, 39, 94, 40, 2],
						        [76, 83, 94, 25, 70, 78, 2],
						        [87, 59, 41, 35, 48, 66, 2],
						        [55, 13, 16, 58, 5, 2, 1],  # note padding
						        [64, 27, 31, 51, 12, 75, 2],
						        [52, 64, 86, 17, 83, 39, 2],
						        [48, 61, 9, 24, 71, 82, 2],
						        [26, 1, 60, 48, 22, 13, 2],
						        [21, 5, 62, 28, 14, 76, 2],
						        [45, 98, 37, 86, 59, 48, 2],
						        [70, 70, 50, 9, 28, 0, 2],
						    ]						,				dtype=np.intaa						,				)

						__a			:       int					    =  input_ids.shape[0]
						__a			:       Optional[int]					    =  BlenderbotSmallConfig(
						    vocab_size=self.vocab_size						,				d_model=24						,				encoder_layers=2						,				decoder_layers=2						,				encoder_attention_heads=2						,				decoder_attention_heads=2						,				encoder_ffn_dim=32						,				decoder_ffn_dim=32						,				max_position_embeddings=48						,				eos_token_id=2						,				pad_token_id=1						,				bos_token_id=0						,				)
						return config, input_ids, batch_size




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a    ,				__a			:       Any					    =  self._get_config_and_data()
						__a			:       str					    =  FlaxBlenderbotSmallForConditionalGeneration(__a							)
						__a			:       List[str]					    =  lm_model(input_ids=__a							)
						__a			:       List[Any]					    =  (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs['logits'].shape						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  BlenderbotSmallConfig(
						    vocab_size=self.vocab_size						,				d_model=14						,				encoder_layers=2						,				decoder_layers=2						,				encoder_attention_heads=2						,				decoder_attention_heads=2						,				encoder_ffn_dim=8						,				decoder_ffn_dim=8						,				max_position_embeddings=48						,				)
						__a			:       Union[str, Any]					    =  FlaxBlenderbotSmallForConditionalGeneration(__a							)
						__a			:       Any					    =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,				dtype=np.intaa							)
						__a			:       Union[str, Any]					    =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,				dtype=np.intaa							)
						__a			:       Any					    =  lm_model(input_ids=__a						,				decoder_input_ids=__a							)
						__a			:       List[str]					    =  (*summary.shape, config.vocab_size)
						self.assertEqual(outputs['logits'].shape						,				__a							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,				dtype=np.intaa							)
						__a			:       Dict					    =  shift_tokens_right(__a						,				1						,				2							)
						__a			:       Any					    =  np.equal(__a						,				1							).astype(np.floataa							).sum()
						__a			:       Tuple					    =  np.equal(__a						,				1							).astype(np.floataa							).sum()
						self.assertEqual(shifted.shape						,				input_ids.shape							)
						self.assertEqual(__a						,				n_pad_before - 1							)
						self.assertTrue(np.equal(shifted[:, 0]						,				2							).all()							)





@require_flax
class 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase		,      lowerCAmelCase_					):
	A_				=    True
	A_				=    (
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	A_				=    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  FlaxBlenderbotSmallModelTester(self							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
																__a			:       str					    =  self._prepare_for_class(__a						,				__a							)
																__a			:       List[str]					    =  model_class(__a							)

																@jax.jit
																def encode_jitted(__a						,				__a=None						,				**__a							):
																					return model.encode(input_ids=__a						,				attention_mask=__a							)

																with self.subTest('JIT Enabled'							):
																					__a			:       str					    =  encode_jitted(**__a							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										__a			:       List[str]					    =  encode_jitted(**__a							).to_tuple()

																self.assertEqual(len(__a							)						,				len(__a							)							)
																for jitted_output, output in zip(__a						,				__a							):
																					self.assertEqual(jitted_output.shape						,				output.shape							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       Any					    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
																__a			:       str					    =  model_class(__a							)
																__a			:       Optional[Any]					    =  model.encode(inputs_dict['input_ids']						,				inputs_dict['attention_mask']							)

																__a			:       int					    =  {
																    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																    'encoder_outputs': encoder_outputs,
																}

																@jax.jit
																def decode_jitted(__a						,				__a						,				__a							):
																					return model.decode(
																					    decoder_input_ids=__a						,				decoder_attention_mask=__a						,				encoder_outputs=__a						,				)

																with self.subTest('JIT Enabled'							):
																					__a			:       List[Any]					    =  decode_jitted(**__a							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										__a			:       Optional[Any]					    =  decode_jitted(**__a							).to_tuple()

																self.assertEqual(len(__a							)						,				len(__a							)							)
																for jitted_output, output in zip(__a						,				__a							):
																					self.assertEqual(jitted_output.shape						,				output.shape							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_class_name in self.all_model_classes:
											__a			:       Dict					    =  model_class_name.from_pretrained('facebook/blenderbot_small-90M'							)
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											__a			:       int					    =  np.ones((1, 1)							) * model.config.eos_token_id
											__a			:       Any					    =  model(__a							)
											self.assertIsNotNone(__a							)







'''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


__lowercase	:						Dict       =	16
__lowercase	:						List[Any]       =	32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					return int(x / 2**20   )





class 							__UpperCamelCase				:




	def __enter__(   self							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						__a			:       Optional[int]					    =  torch.cuda.memory_allocated()
						return self





	def __exit__(   self						,				*__a							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						__a			:       Dict					    =  torch.cuda.memory_allocated()
						__a			:       List[Any]					    =  torch.cuda.max_memory_allocated()
						__a			:       Tuple					    =  bamb(self.end - self.begin							)
						__a			:       Tuple					    =  bamb(self.peak - self.begin							)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Accelerator						,      _SCREAMING_SNAKE_CASE      :   int = 16						,      _SCREAMING_SNAKE_CASE      :   str = "bert-base-cased"						,      _SCREAMING_SNAKE_CASE      :   int = 320						,      _SCREAMING_SNAKE_CASE      :   int = 160						,      ):
					__a			:       int					    =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  load_dataset(
					    'glue'						,      'mrpc'						,      split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}   )

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

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__a			:       List[str]					    =  datasets.map(
					    _SCREAMING_SNAKE_CASE						,      batched=_SCREAMING_SNAKE_CASE						,      remove_columns=['idx', 'sentence1', 'sentence2']						,      load_from_cache_file=_SCREAMING_SNAKE_CASE   )

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

					def collate_fn(_SCREAMING_SNAKE_CASE      :   Tuple   ):
										# 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(_SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=128						,      return_tensors='pt'   )
										return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='longest'						,      return_tensors='pt'   )

					# Instantiate dataloaders.
					__a			:       int					    =  DataLoader(
					    tokenized_datasets['train']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  DataLoader(
					    tokenized_datasets['validation']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )

					return train_dataloader, eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					# Initialize accelerator
					__a			:       str					    =  Accelerator()

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

					set_seed(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       int					    =  get_dataloaders(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      args.n_train						,      args.n_val   )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a			:       Optional[int]					    =  AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE						,      return_dict=_SCREAMING_SNAKE_CASE   )

					# Instantiate optimizer
					__a			:       Optional[Any]					    =  (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__a			:       Optional[Any]					    =  optimizer_cls(params=model.parameters()						,      lr=_SCREAMING_SNAKE_CASE   )

					if accelerator.state.deepspeed_plugin is not None:
										__a			:       int					    =  accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										__a			:       Union[str, Any]					    =  1
					__a			:       Tuple					    =  (len(_SCREAMING_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
					):
										__a			:       str					    =  get_linear_schedule_with_warmup(
										    optimizer=_SCREAMING_SNAKE_CASE						,      num_warmup_steps=0						,      num_training_steps=_SCREAMING_SNAKE_CASE						,      )
					else:
										__a			:       List[Any]					    =  DummyScheduler(_SCREAMING_SNAKE_CASE						,      total_num_steps=_SCREAMING_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.
					__a    ,				__a    ,				__a    ,				__a    ,				__a			:       Optional[Any]					    =  accelerator.prepare(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# We need to keep track of how many total steps we have iterated over
					__a			:       Union[str, Any]					    =  0
					# We also need to keep track of the stating epoch so files are named properly
					__a			:       Dict					    =  0

					# Now we train the model
					__a			:       str					    =  {}
					for epoch in range(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       List[Any]					    =  model(**_SCREAMING_SNAKE_CASE   )
																				__a			:       str					    =  outputs.loss
																				__a			:       str					    =  loss / gradient_accumulation_steps
																				accelerator.backward(_SCREAMING_SNAKE_CASE   )
																				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   )   )   )
										__a			:       List[Any]					    =  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(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							():
					__a			:       int					    =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
					parser.add_argument(
					    '--model_name_or_path'						,      type=_SCREAMING_SNAKE_CASE						,      default='bert-base-cased'						,      help='Path to pretrained model or model identifier from huggingface.co/models.'						,      required=_SCREAMING_SNAKE_CASE						,      )
					parser.add_argument(
					    '--output_dir'						,      type=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      default=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      default=320						,      help='Number of training examples to use.'						,      )
					parser.add_argument(
					    '--n_val'						,      type=_SCREAMING_SNAKE_CASE						,      default=160						,      help='Number of validation examples to use.'						,      )
					parser.add_argument(
					    '--num_epochs'						,      type=_SCREAMING_SNAKE_CASE						,      default=1						,      help='Number of train epochs.'						,      )
					__a			:       List[str]					    =  parser.parse_args()
					__a			:       List[Any]					    =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()



