

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


lowerCAmelCase_       	=	16
lowerCAmelCase_       	=	32


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return int(x / 2**20       )





class 	snake_case_   :







			'''simple docstring'''

			def __enter__(     self :				Any							)      ->Optional[Any]:
									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									snake_case_   =	torch.cuda.memory_allocated()
									return self







			def __exit__(     self :				List[str]							,   *_UpperCamelCase :				str							)      ->List[Any]:
									gc.collect()
									torch.cuda.empty_cache()
									snake_case_   =	torch.cuda.memory_allocated()
									snake_case_   =	torch.cuda.max_memory_allocated()
									snake_case_   =	bamb(self.end - self.begin							)
									snake_case_   =	bamb(self.peak - self.begin							)
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = 16  ,		SCREAMING_SNAKE_CASE__ = "bert-base-cased"  ,		SCREAMING_SNAKE_CASE__ = 320  ,		SCREAMING_SNAKE_CASE__ = 160  ,		):
						snake_case_   =	AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	load_dataset(
						    '''glue'''  ,		'''mrpc'''  ,		split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}       )

						def tokenize_function(SCREAMING_SNAKE_CASE__       ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case_   =	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
						snake_case_   =	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
						snake_case_   =	tokenized_datasets.rename_column('''label'''  ,		'''labels'''       )

						def collate_fn(SCREAMING_SNAKE_CASE__       ):
												# 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.
						snake_case_   =	DataLoader(
						    tokenized_datasets['''train''']  ,		shuffle=SCREAMING_SNAKE_CASE__  ,		collate_fn=SCREAMING_SNAKE_CASE__  ,		batch_size=SCREAMING_SNAKE_CASE__       )
						snake_case_   =	DataLoader(
						    tokenized_datasets['''validation''']  ,		shuffle=SCREAMING_SNAKE_CASE__  ,		collate_fn=SCREAMING_SNAKE_CASE__  ,		batch_size=SCREAMING_SNAKE_CASE__       )

						return train_dataloader, eval_dataloader


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# Initialize accelerator
						snake_case_   =	Accelerator()

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

						set_seed(SCREAMING_SNAKE_CASE__       )
						snake_case_,    snake_case_   =	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)
						snake_case_   =	AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__  ,		return_dict=SCREAMING_SNAKE_CASE__       )

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

						if accelerator.state.deepspeed_plugin is not None:
												snake_case_   =	accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												snake_case_   =	1
						snake_case_   =	(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
						):
												snake_case_   =	get_linear_schedule_with_warmup(
												    optimizer=SCREAMING_SNAKE_CASE__  ,		num_warmup_steps=0  ,		num_training_steps=SCREAMING_SNAKE_CASE__  ,		)
						else:
												snake_case_   =	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.
						snake_case_,    snake_case_,    snake_case_,    snake_case_,    snake_case_   =	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
						snake_case_   =	0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case_   =	0

						# Now we train the model
						snake_case_   =	{}
						for epoch in range(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
																								snake_case_   =	model(**SCREAMING_SNAKE_CASE__       )
																								snake_case_   =	outputs.loss
																								snake_case_   =	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       )       )       )
												snake_case_   =	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   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	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.'''  ,		)
						snake_case_   =	parser.parse_args()
						snake_case_   =	{'''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()

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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

_UpperCAmelCase					: Optional[Any]      =       {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       int	     =				"marian"
		__lowercase       :       List[str]	     =				["past_key_values"]
		__lowercase       :       Any	     =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__( self				,      A_=58_101				,      A_=None				,      A_=1_024				,      A_=12				,      A_=4_096				,      A_=16				,      A_=12				,      A_=4_096				,      A_=16				,      A_=0.0				,      A_=0.0				,      A_=True				,      A_=True				,      A_="gelu"				,      A_=1_024				,      A_=0.1				,      A_=0.0				,      A_=0.0				,      A_=0.02				,      A_=58_100				,      A_=False				,      A_=58_100				,      A_=0				,      A_=0				,      A_=True				,      **A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     decoder_vocab_size or vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     d_model
				UpperCamelCase		=     encoder_ffn_dim
				UpperCamelCase		=     encoder_layers
				UpperCamelCase		=     encoder_attention_heads
				UpperCamelCase		=     decoder_ffn_dim
				UpperCamelCase		=     decoder_layers
				UpperCamelCase		=     decoder_attention_heads
				UpperCamelCase		=     dropout
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     activation_dropout
				UpperCamelCase		=     activation_function
				UpperCamelCase		=     init_std
				UpperCamelCase		=     encoder_layerdrop
				UpperCamelCase		=     decoder_layerdrop
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     encoder_layers
				UpperCamelCase		=     scale_embedding  # scale factor will be sqrt(d_model) if True
				UpperCamelCase		=     share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=A_				,      eos_token_id=A_				,      is_encoder_decoder=A_				,      decoder_start_token_id=A_				,      forced_eos_token_id=A_				,      **A_				,      )






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]       )

						if self.use_past:
								UpperCamelCase		=     {0: 'batch'}
								UpperCamelCase		=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
								UpperCamelCase		=     {0: 'batch', 1: 'decoder_sequence'}
								UpperCamelCase		=     {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
								self.fill_with_past_key_values_(A_				,      direction='inputs'       )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]       )
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				else:
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
						        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
						    ]       )

				return common_inputs



		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super().outputs
				else:
						UpperCamelCase		=     super(A_				,      self       ).outputs
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

				# Generate decoder inputs
				UpperCamelCase		=     seq_length if not self.use_past else 1
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )
				UpperCamelCase		=     {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase		=     dict(**A_				,      **A_       )

				if self.use_past:
						if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
						else:
								import torch
						UpperCamelCase	,  UpperCamelCase		=     common_inputs['input_ids'].shape
						UpperCamelCase		=     common_inputs['decoder_input_ids'].shape[1]
						UpperCamelCase	,  UpperCamelCase		=     self.num_attention_heads
						UpperCamelCase		=     (
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						UpperCamelCase		=     decoder_seq_length + 3
						UpperCamelCase		=     (
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						UpperCamelCase		=     torch.cat(
						    [common_inputs['decoder_attention_mask'], torch.ones(A_				,      A_       )]				,      dim=1       )

						UpperCamelCase		=     []
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						UpperCamelCase	,  UpperCamelCase		=     self.num_layers
						UpperCamelCase		=     min(A_				,      A_       )
						UpperCamelCase		=     max(A_				,      A_       ) - min_num_layers
						UpperCamelCase		=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

						for _ in range(A_       ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(A_       ),
								        torch.zeros(A_       ),
								        torch.zeros(A_       ),
								        torch.zeros(A_       ),
								    )       )
						# TODO: test this.
						UpperCamelCase		=     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
						for _ in range(A_				,      A_       ):
								common_inputs["past_key_values"].append((torch.zeros(A_       ), torch.zeros(A_       ))       )
				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

				if self.use_past:
						if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
						else:
								import torch
						UpperCamelCase	,  UpperCamelCase		=     common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						UpperCamelCase		=     seqlen + 2
						UpperCamelCase	,  UpperCamelCase		=     self.num_layers
						UpperCamelCase	,  UpperCamelCase		=     self.num_attention_heads
						UpperCamelCase		=     (
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						UpperCamelCase		=     common_inputs['attention_mask'].dtype
						UpperCamelCase		=     torch.cat(
						    [common_inputs['attention_mask'], torch.ones(A_				,      A_				,      dtype=A_       )]				,      dim=1       )
						UpperCamelCase		=     [
						    (torch.zeros(A_       ), torch.zeros(A_       )) for _ in range(A_       )
						]
				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_batch				,      num_token_to_add=0       )

				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				UpperCamelCase		=     tokenizer.num_special_tokens_to_add(A_       )
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_sequence				,      num_token_to_add=A_       )

				# Generate dummy inputs according to compute batch and sequence
				UpperCamelCase		=     [' '.join([tokenizer.unk_token]       ) * seq_length] * batch_size
				UpperCamelCase		=     dict(tokenizer(A_				,      return_tensors=A_       )       )
				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				else:
						UpperCamelCase		=     self._generate_dummy_inputs_for_causal_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super()._flatten_past_key_values_(A_				,      A_				,      A_				,      A_       )
				else:
						UpperCamelCase		=     super(A_				,      self       )._flatten_past_key_values_(
						    A_				,      A_				,      A_				,      A_       )



		@property
		def __UpperCamelCase ( self       )  ->					float:
				"""simple docstring"""
				return 1e-4
