



import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    FunnelBaseModel,
							    FunnelForMaskedLM,
							    FunnelForMultipleChoice,
							    FunnelForPreTraining,
							    FunnelForQuestionAnswering,
							    FunnelForSequenceClassification,
							    FunnelForTokenClassification,
							    FunnelModel,
							)







class 				a_    :

						def __init__(       self      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Dict=13  ,		_lowercase      :Union[str, Any]=7  ,		_lowercase      :str=True  ,		_lowercase      :List[str]=True  ,		_lowercase      :List[Any]=True  ,		_lowercase      :Optional[Any]=True  ,		_lowercase      :List[Any]=99  ,		_lowercase      :Optional[int]=[1, 1, 2]  ,		_lowercase      :Optional[int]=1  ,		_lowercase      :List[str]=32  ,		_lowercase      :str=4  ,		_lowercase      :Optional[Any]=8  ,		_lowercase      :int=37  ,		_lowercase      :Any="gelu_new"  ,		_lowercase      :Optional[Any]=0.1  ,		_lowercase      :List[str]=0.1  ,		_lowercase      :int=0.0  ,		_lowercase      :Any=512  ,		_lowercase      :str=3  ,		_lowercase      :List[Any]=0.02  ,		_lowercase      :Dict=3  ,		_lowercase      :str=4  ,		_lowercase      :Tuple=None  ,		_lowercase      :Union[str, Any]=False  ,		)						->   Any:
												UpperCAmelCase_					=	parent
												UpperCAmelCase_					=	batch_size
												UpperCAmelCase_					=	seq_length
												UpperCAmelCase_					=	is_training
												UpperCAmelCase_					=	use_input_mask
												UpperCAmelCase_					=	use_token_type_ids
												UpperCAmelCase_					=	use_labels
												UpperCAmelCase_					=	vocab_size
												UpperCAmelCase_					=	block_sizes
												UpperCAmelCase_					=	num_decoder_layers
												UpperCAmelCase_					=	d_model
												UpperCAmelCase_					=	n_head
												UpperCAmelCase_					=	d_head
												UpperCAmelCase_					=	d_inner
												UpperCAmelCase_					=	hidden_act
												UpperCAmelCase_					=	hidden_dropout
												UpperCAmelCase_					=	attention_dropout
												UpperCAmelCase_					=	activation_dropout
												UpperCAmelCase_					=	max_position_embeddings
												UpperCAmelCase_					=	type_vocab_size
												UpperCAmelCase_					=	2
												UpperCAmelCase_					=	num_labels
												UpperCAmelCase_					=	num_choices
												UpperCAmelCase_					=	scope
												UpperCAmelCase_					=	initializer_std

												# Used in the tests to check the size of the first attention layer
												UpperCAmelCase_					=	n_head
												# Used in the tests to check the size of the first hidden state
												UpperCAmelCase_					=	self.d_model
												# Used in the tests to check the number of output hidden states/attentions
												UpperCAmelCase_					=	sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
												# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
												# the last hidden state of the first block (which is the first hidden state of the decoder).
												if not base:
																		UpperCAmelCase_					=	self.num_hidden_layers + 2

						def       __a			(       self      :List[Any])						->   Dict:
												UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size)

												UpperCAmelCase_					=	None
												if self.use_input_mask:
																		UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		vocab_size=2)

												UpperCAmelCase_					=	None
												if self.use_token_type_ids:
																		UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		self.type_vocab_size)

												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												if self.use_labels:
																		UpperCAmelCase_					=	ids_tensor([self.batch_size]  ,		self.type_sequence_label_size)
																		UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels)
																		UpperCAmelCase_					=	ids_tensor([self.batch_size]  ,		self.num_choices)
																		UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		1)

												UpperCAmelCase_					=	self.get_config()

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_mask,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												    fake_token_labels,
												)

						def       __a			(       self      :Optional[int])						->   Dict:
												return FunnelConfig(
												    vocab_size=self.vocab_size  ,		block_sizes=self.block_sizes  ,		num_decoder_layers=self.num_decoder_layers  ,		d_model=self.d_model  ,		n_head=self.n_head  ,		d_head=self.d_head  ,		d_inner=self.d_inner  ,		hidden_act=self.hidden_act  ,		hidden_dropout=self.hidden_dropout  ,		attention_dropout=self.attention_dropout  ,		activation_dropout=self.activation_dropout  ,		max_position_embeddings=self.max_position_embeddings  ,		type_vocab_size=self.type_vocab_size  ,		initializer_std=self.initializer_std  ,		)

						def       __a			(       self      :List[str]  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Tuple  ,		_lowercase      :Optional[int]  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :List[str]  ,		_lowercase      :List[Any]  ,		_lowercase      :List[str]  ,		)						->   int:
												UpperCAmelCase_					=	FunnelModel(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase)
												UpperCAmelCase_					=	model(_lowercase  ,		token_type_ids=_lowercase)
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.d_model))

												UpperCAmelCase_					=	False
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.d_model))

												UpperCAmelCase_					=	False
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.d_model))

						def       __a			(       self      :Optional[int]  ,		_lowercase      :Optional[int]  ,		_lowercase      :int  ,		_lowercase      :int  ,		_lowercase      :Dict  ,		_lowercase      :List[Any]  ,		_lowercase      :Tuple  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :Any  ,		)						->   Optional[int]:
												UpperCAmelCase_					=	FunnelBaseModel(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase)
												UpperCAmelCase_					=	model(_lowercase  ,		token_type_ids=_lowercase)
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, 2, self.d_model))

												UpperCAmelCase_					=	False
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, 3, self.d_model))

												UpperCAmelCase_					=	False
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, 2, self.d_model))

						def       __a			(       self      :Optional[int]  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :List[Any]  ,		_lowercase      :Optional[int]  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[Any]  ,		_lowercase      :List[str]  ,		_lowercase      :List[Any]  ,		_lowercase      :Any  ,		)						->   Dict:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	FunnelForPreTraining(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase  ,		labels=_lowercase)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length))

						def       __a			(       self      :List[str]  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Any  ,		_lowercase      :List[Any]  ,		_lowercase      :int  ,		)						->   Optional[Any]:
												UpperCAmelCase_					=	FunnelForMaskedLM(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase  ,		labels=_lowercase)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size))

						def       __a			(       self      :str  ,		_lowercase      :List[Any]  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Any  ,		_lowercase      :List[Any]  ,		_lowercase      :Dict  ,		_lowercase      :Tuple  ,		_lowercase      :Optional[int]  ,		_lowercase      :List[Any]  ,		)						->   Optional[int]:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	FunnelForSequenceClassification(_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase  ,		labels=_lowercase)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels))

						def       __a			(       self      :Dict  ,		_lowercase      :Any  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[int]  ,		_lowercase      :List[Any]  ,		_lowercase      :Tuple  ,		_lowercase      :Any  ,		_lowercase      :Tuple  ,		_lowercase      :Dict  ,		)						->   Optional[Any]:
												UpperCAmelCase_					=	self.num_choices
												UpperCAmelCase_					=	FunnelForMultipleChoice(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	input_ids.unsqueeze(1).expand(-1  ,		self.num_choices  ,		-1).contiguous()
												UpperCAmelCase_					=	token_type_ids.unsqueeze(1).expand(-1  ,		self.num_choices  ,		-1).contiguous()
												UpperCAmelCase_					=	input_mask.unsqueeze(1).expand(-1  ,		self.num_choices  ,		-1).contiguous()
												UpperCAmelCase_					=	model(
												    _lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase  ,		labels=_lowercase  ,		)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_choices))

						def       __a			(       self      :Dict  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[int]  ,		_lowercase      :List[str]  ,		_lowercase      :Any  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[int]  ,		_lowercase      :List[str]  ,		_lowercase      :Tuple  ,		)						->   Dict:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	FunnelForTokenClassification(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase  ,		labels=_lowercase)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.num_labels))

						def       __a			(       self      :List[str]  ,		_lowercase      :List[str]  ,		_lowercase      :List[Any]  ,		_lowercase      :List[str]  ,		_lowercase      :Tuple  ,		_lowercase      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Optional[int]  ,		)						->   List[Any]:
												UpperCAmelCase_					=	FunnelForQuestionAnswering(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(
												    _lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase  ,		start_positions=_lowercase  ,		end_positions=_lowercase  ,		)
												self.parent.assertEqual(result.start_logits.shape  ,		(self.batch_size, self.seq_length))
												self.parent.assertEqual(result.end_logits.shape  ,		(self.batch_size, self.seq_length))




						def       __a			(       self      :List[str])						->   Any:
												UpperCAmelCase_					=	self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, (
												    UpperCAmelCase_
												)				, 
												)					=	config_and_inputs
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_torch
class 				a_    (						_snake_case   ,					_snake_case   ,					unittest.TestCase							):
						UpperCamelCase__ :		int										=False
						UpperCamelCase__ :		Tuple										=False
						UpperCamelCase__ :		Optional[int]										=(
						    (
						        FunnelModel,
						        FunnelForMaskedLM,
						        FunnelForPreTraining,
						        FunnelForQuestionAnswering,
						        FunnelForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase__ :		Dict										=(
						    {
						        "feature-extraction": (FunnelBaseModel, FunnelModel),
						        "fill-mask": FunnelForMaskedLM,
						        "question-answering": FunnelForQuestionAnswering,
						        "text-classification": FunnelForSequenceClassification,
						        "token-classification": FunnelForTokenClassification,
						        "zero-shot": FunnelForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :Any  ,		_lowercase      :Any  ,		_lowercase      :Optional[Any]=False)						->   str:
												UpperCAmelCase_					=	super()._prepare_for_class(_lowercase  ,		_lowercase  ,		return_labels=_lowercase)

												if return_labels:
																		if model_class in get_values(_lowercase):
																								UpperCAmelCase_					=	torch.zeros(
																								    (self.model_tester.batch_size, self.model_tester.seq_length)  ,		dtype=torch.long  ,		device=_lowercase)
												return inputs_dict

						def       __a			(       self      :str)						->   List[Any]:
												UpperCAmelCase_					=	FunnelModelTester(self)
												UpperCAmelCase_					=	ConfigTester(self  ,		config_class=_lowercase)

						def       __a			(       self      :List[str])						->   int:
												self.config_tester.run_common_tests()

						def       __a			(       self      :Optional[int])						->   Optional[Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_lowercase)

						def       __a			(       self      :str)						->   int:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*_lowercase)

						def       __a			(       self      :str)						->   Optional[int]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*_lowercase)

						def       __a			(       self      :str)						->   Dict:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_lowercase)

						def       __a			(       self      :str)						->   Optional[Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_lowercase)




						def       __a			(       self      :Tuple  ,		_lowercase      :str)						->   int:
												if hasattr(_lowercase  ,		'''weight''') and module.weight is not None:
																		module.weight.data.fill_(3)
												if hasattr(_lowercase  ,		'''bias''') and module.bias is not None:
																		module.bias.data.fill_(3)

												for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
																		if hasattr(_lowercase  ,		_lowercase) and getattr(_lowercase  ,		_lowercase) is not None:
																								UpperCAmelCase_					=	getattr(_lowercase  ,		_lowercase)
																								weight.data.fill_(3)







@require_torch
class 				a_    (						_snake_case   ,					unittest.TestCase							):
						UpperCamelCase__ :		List[Any]										=False
						UpperCamelCase__ :		List[str]										=False
						UpperCamelCase__ :		Tuple										=(
						    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
						)

						def       __a			(       self      :Tuple)						->   Optional[int]:
												UpperCAmelCase_					=	FunnelModelTester(self  ,		base=_lowercase)
												UpperCAmelCase_					=	ConfigTester(self  ,		config_class=_lowercase)

						def       __a			(       self      :str)						->   Optional[int]:
												self.config_tester.run_common_tests()

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_base_model(*_lowercase)

						def       __a			(       self      :Union[str, Any])						->   str:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_lowercase)

						def       __a			(       self      :int)						->   int:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*_lowercase)

						def       __a			(       self      :Dict)						->   int:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase_					=	True

												for model_class in self.all_model_classes:
																		if model_class.__name__ == "FunnelBaseModel":
																								continue
																		UpperCAmelCase_					=	model_class(_lowercase)
																		model.to(_lowercase)
																		model.train()
																		UpperCAmelCase_					=	self._prepare_for_class(_lowercase  ,		_lowercase  ,		return_labels=_lowercase)
																		UpperCAmelCase_					=	model(**_lowercase).loss
																		loss.backward()




						def       __a			(       self      :List[Any]  ,		_lowercase      :Optional[Any])						->   int:
												if hasattr(_lowercase  ,		'''weight''') and module.weight is not None:
																		module.weight.data.fill_(3)
												if hasattr(_lowercase  ,		'''bias''') and module.bias is not None:
																		module.bias.data.fill_(3)

												for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
																		if hasattr(_lowercase  ,		_lowercase) and getattr(_lowercase  ,		_lowercase) is not None:
																								UpperCAmelCase_					=	getattr(_lowercase  ,		_lowercase)
																								weight.data.fill_(3)







@require_torch
@require_sentencepiece
@require_tokenizers
class 				a_    (						unittest.TestCase							):

						def       __a			(       self      :Any)						->   str:
												UpperCAmelCase_					=	13
												UpperCAmelCase_					=	7
												UpperCAmelCase_					=	torch.arange(0  ,		batch_size * sequence_length).long().reshape(_lowercase  ,		_lowercase)
												UpperCAmelCase_					=	[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
												UpperCAmelCase_					=	torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])

												UpperCAmelCase_					=	FunnelModel.from_pretrained('''sgugger/funnel-random-tiny''')
												UpperCAmelCase_					=	model(_lowercase  ,		token_type_ids=_lowercase)[0].abs()

												UpperCAmelCase_					=	torch.tensor(2_344.8_352)
												UpperCAmelCase_					=	torch.tensor(0.8_052)
												self.assertTrue(torch.allclose(output.sum()  ,		_lowercase  ,		atol=1E-4))
												self.assertTrue(torch.allclose(output.mean()  ,		_lowercase  ,		atol=1E-4))

												UpperCAmelCase_					=	torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase  ,		token_type_ids=_lowercase)[0].abs()

												UpperCAmelCase_					=	torch.tensor(2_343.8_425)
												UpperCAmelCase_					=	torch.tensor(0.8_049)
												self.assertTrue(torch.allclose(output.sum()  ,		_lowercase  ,		atol=1E-4))
												self.assertTrue(torch.allclose(output.mean()  ,		_lowercase  ,		atol=1E-4))




						@slow
						def       __a			(       self      :List[Any])						->   List[Any]:
												UpperCAmelCase_					=	FunnelTokenizer.from_pretrained('''huggingface/funnel-small''')
												UpperCAmelCase_					=	FunnelModel.from_pretrained('''huggingface/funnel-small''')
												UpperCAmelCase_					=	tokenizer('''Hello! I am the Funnel Transformer model.'''  ,		return_tensors='''pt''')
												UpperCAmelCase_					=	model(**_lowercase)[0]

												UpperCAmelCase_					=	torch.tensor(235.7_246)
												UpperCAmelCase_					=	torch.tensor(0.0_256)
												self.assertTrue(torch.allclose(output.sum()  ,		_lowercase  ,		atol=1E-4))
												self.assertTrue(torch.allclose(output.mean()  ,		_lowercase  ,		atol=1E-4))










import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase_      			=  16
UpperCamelCase_      			=  32







def 							A     (						__UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						return int(x / 2**20      )







class 				a_    :

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

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	threading.Thread(target=self.peak_monitor_func)
												UpperCAmelCase_					=	True
												peak_monitor_thread.start()
												return self

						def       __a			(       self      :Tuple)						->   int:
												return self.process.memory_info().rss

						def       __a			(       self      :str)						->   Dict:
												UpperCAmelCase_					=	-1

												while True:
																		UpperCAmelCase_					=	max(self.cpu_mem_used()  ,		self.cpu_peak)

																		# can't sleep or will not catch the peak right (this comment is here on purpose)
																		# time.sleep(0.001) # 1msec

																		if not self.peak_monitoring:
																								break




						def __exit__(       self      :Optional[Any]  ,		*_lowercase      :Optional[Any])						->   List[str]:
												UpperCAmelCase_					=	False

												gc.collect()
												torch.cuda.empty_cache()
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	torch.cuda.max_memory_allocated()
												UpperCAmelCase_					=	bamb(self.end - self.begin)
												UpperCAmelCase_					=	bamb(self.peak - self.begin)

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	bamb(self.cpu_end - self.cpu_begin)
												UpperCAmelCase_					=	bamb(self.cpu_peak - self.cpu_begin)
												# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							UpperCamelCase_      			=  mocked_dataloaders  # noqa: F811







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,   __UpperCAmelCase      ) == "1":
												UpperCAmelCase_					=	2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						UpperCAmelCase_					=	FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   )

						# Initialize accelerator
						if args.with_tracking:
												UpperCAmelCase_					=	Accelerator(
												    cpu=args.cpu				,   mixed_precision=args.mixed_precision				,   log_with='''wandb'''				,   project_dir=args.logging_dir				,   fsdp_plugin=__UpperCAmelCase				,   )
						else:
												UpperCAmelCase_					=	Accelerator(fsdp_plugin=__UpperCAmelCase      )
						accelerator.print(accelerator.distributed_type      )

						if hasattr(args.checkpointing_steps				,   '''isdigit'''      ):
												if args.checkpointing_steps == "epoch":
																		UpperCAmelCase_					=	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		UpperCAmelCase_					=	int(args.checkpointing_steps      )
												else:
																		raise ValueError(
																		    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
						else:
												UpperCAmelCase_					=	None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_					=	config['''lr''']
						UpperCAmelCase_					=	int(config['''num_epochs''']      )
						UpperCAmelCase_					=	int(config['''seed''']      )
						UpperCAmelCase_					=	int(config['''batch_size''']      )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												UpperCAmelCase_					=	vars(__UpperCAmelCase      )
												accelerator.init_trackers('''fsdp_glue_no_trainer'''				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
						UpperCAmelCase_					=	load_dataset('''glue'''				,   '''mrpc'''      )
						UpperCAmelCase_					=	evaluate.load('''glue'''				,   '''mrpc'''      )

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

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												UpperCAmelCase_					=	datasets.map(
												    __UpperCAmelCase				,   batched=__UpperCAmelCase				,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,   )

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

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase_					=	1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase_					=	batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase_					=	MAX_GPU_BATCH_SIZE

						def collate_fn(__UpperCAmelCase      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase_					=	128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCAmelCase_					=	16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase_					=	8
												else:
																		UpperCAmelCase_					=	None

												return tokenizer.pad(
												    __UpperCAmelCase				,   padding='''longest'''				,   max_length=__UpperCAmelCase				,   pad_to_multiple_of=__UpperCAmelCase				,   return_tensors='''pt'''				,   )

						# Instantiate dataloaders.
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''train''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''validation''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )

						set_seed(__UpperCAmelCase      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_					=	AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path				,   return_dict=__UpperCAmelCase				,   low_cpu_mem_usage=__UpperCAmelCase      )

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						UpperCAmelCase_					=	accelerator.prepare(__UpperCAmelCase      )
						accelerator.print(__UpperCAmelCase      )

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						UpperCAmelCase_					=	torch.optim.AdamW(params=model.parameters()				,   lr=__UpperCAmelCase				,   weight_decay=2e-4      )

						# Instantiate scheduler
						UpperCAmelCase_					=	get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase				,   num_warmup_steps=10				,   num_training_steps=(len(__UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps				,   )

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.prepare(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"      )
																		accelerator.load_state(args.resume_from_checkpoint      )
																		UpperCAmelCase_					=	os.path.basename(args.resume_from_checkpoint      )
												else:
																		# Get the most recent checkpoint
																		UpperCAmelCase_					=	[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime      )
																		UpperCAmelCase_					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												UpperCAmelCase_					=	os.path.splitext(__UpperCAmelCase      )[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''				,   ''''''      )      )
																		UpperCAmelCase_					=	None
												else:
																		UpperCAmelCase_					=	int(training_difference.replace('''step_'''				,   ''''''      )      )
																		num_epochs -= resume_step // len(__UpperCAmelCase      )
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		UpperCAmelCase_					=	(num_epochs * len(__UpperCAmelCase      )) - resume_step

    # Now we train the model
						for epoch in range(__UpperCAmelCase      ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								UpperCAmelCase_					=	0
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.loss
																								UpperCAmelCase_					=	loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(__UpperCAmelCase      )
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
																														UpperCAmelCase_					=	f"step_{overall_step}"
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																																				accelerator.save_state(__UpperCAmelCase      )
        # New Code #
        # 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      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }				,   step=__UpperCAmelCase				,   )

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								with torch.no_grad():
																														UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.logits.argmax(dim=-1      )
																								UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
																								metric.add_batch(
																								    predictions=__UpperCAmelCase				,   references=__UpperCAmelCase				,   )

																		UpperCAmelCase_					=	metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"epoch {epoch}:"				,   __UpperCAmelCase      )
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        '''accuracy''': eval_metric['''accuracy'''],
																								        '''f1''': eval_metric['''f1'''],
																								        '''train_loss''': total_loss.item() / len(__UpperCAmelCase      ),
																								    }				,   step=__UpperCAmelCase				,   )

																		if checkpointing_steps == "epoch":
																								UpperCAmelCase_					=	f"epoch_{epoch}"
																								if args.output_dir is not None:
																														UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																								accelerator.save_state(__UpperCAmelCase      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin      )      )      )
												accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used      )      )
												accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked      )      )
												accelerator.print(
												    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }				,   step=__UpperCAmelCase				,   )

						if args.with_tracking:
												accelerator.end_training()







def 							A     (						)			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	argparse.ArgumentParser(description='''Simple example of training script.'''      )
						parser.add_argument(
						    '''--mixed_precision'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,   help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''				,   )
						parser.add_argument('''--cpu'''				,   action='''store_true'''				,   help='''If passed, will train on the CPU.'''      )
						parser.add_argument(
						    '''--checkpointing_steps'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,   )
						parser.add_argument(
						    '''--resume_from_checkpoint'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''If the training should continue from a checkpoint folder.'''				,   )
						parser.add_argument(
						    '''--with_tracking'''				,   action='''store_true'''				,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''				,   )
						parser.add_argument(
						    '''--output_dir'''				,   type=__UpperCAmelCase				,   default='''.'''				,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,   )
						parser.add_argument(
						    '''--logging_dir'''				,   type=__UpperCAmelCase				,   default='''logs'''				,   help='''Location on where to store experiment tracking logs`'''				,   )
						parser.add_argument(
						    '''--model_name_or_path'''				,   type=__UpperCAmelCase				,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,   required=__UpperCAmelCase				,   )
						UpperCAmelCase_					=	parser.parse_args()
						UpperCAmelCase_					=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							main()





