


"""simple docstring"""







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
#
########################################################################


__a      			= 16
__a      			= 32






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						return int(x / 2**20			)
class 				lowerCamelCase							:


							"""simple docstring"""






							def __enter__(    self			:  Dict					):
													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        lowerCAmelCase  (    self			:  Optional[int]					):
													return self.process.memory_info().rss






							def        lowerCAmelCase  (    self			:  str					):
													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			:  int		,			*lowerCamelCase_			:  Any					):
													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

						__a      			= mocked_dataloaders  # noqa: F811






def        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""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__			(							):





						"""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()



"""simple docstring"""







import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__a      			= [nn.LayerNorm]

__a      			= logging.get_logger(__name__)

__a      			= version.parse(version.parse(torch.__version__).base_version)

__a      			= parsed_torch_version_base >= version.parse("2.0")
__a      			= parsed_torch_version_base >= version.parse("1.12")
__a      			= parsed_torch_version_base >= version.parse("1.11")
__a      			= parsed_torch_version_base < version.parse("1.11")






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      self			):





						"""simple docstring"""

						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(_UpperCamelCase						,      _UpperCamelCase						,      parent.dim						,      self			)
						else:
												return _softmax_backward_data(_UpperCamelCase						,      _UpperCamelCase						,      parent.dim						,      self.dtype			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 0			):





						"""simple docstring"""

						UpperCAmelCase__												=     index.to(layer.weight.device			)
						UpperCAmelCase__												=     layer.weight.index_select(_UpperCamelCase						,      _UpperCamelCase			).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		UpperCAmelCase__												=     layer.bias.clone().detach()
												else:
																		UpperCAmelCase__												=     layer.bias[index].clone().detach()
						UpperCAmelCase__												=     list(layer.weight.size()			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     nn.Linear(new_size[1]						,      new_size[0]						,      bias=layer.bias is not None			).to(layer.weight.device			)
						UpperCAmelCase__												=     False
						new_layer.weight.copy_(W.contiguous()			)
						UpperCAmelCase__												=     True
						if layer.bias is not None:
												UpperCAmelCase__												=     False
												new_layer.bias.copy_(b.contiguous()			)
												UpperCAmelCase__												=     True
						return new_layer
class 				lowerCamelCase							(      nn.Module			):


							"""simple docstring"""






							def __init__(    self			:  str		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Dict					):
													super().__init__()
													UpperCAmelCase__												=     nf
													UpperCAmelCase__												=     nn.Parameter(torch.empty(lowerCamelCase_		,			lowerCamelCase_					)					)
													UpperCAmelCase__												=     nn.Parameter(torch.zeros(lowerCamelCase_					)					)
													nn.init.normal_(self.weight		,			std=0.02					)






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Tuple					):
													UpperCAmelCase__												=     x.size()[:-1] + (self.nf,)
													UpperCAmelCase__												=     torch.addmm(self.bias		,			x.view(-1		,			x.size(-1					)					)		,			self.weight					)
													UpperCAmelCase__												=     x.view(lowerCamelCase_					)
													return x






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 1			):





						"""simple docstring"""

						UpperCAmelCase__												=     index.to(layer.weight.device			)
						UpperCAmelCase__												=     layer.weight.index_select(_UpperCamelCase						,      _UpperCamelCase			).clone().detach()
						if dim == 0:
												UpperCAmelCase__												=     layer.bias.clone().detach()
						else:
												UpperCAmelCase__												=     layer.bias[index].clone().detach()
						UpperCAmelCase__												=     list(layer.weight.size()			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     ConvaD(new_size[1]						,      new_size[0]			).to(layer.weight.device			)
						UpperCAmelCase__												=     False
						new_layer.weight.copy_(W.contiguous()			)
						UpperCAmelCase__												=     True
						UpperCAmelCase__												=     False
						new_layer.bias.copy_(b.contiguous()			)
						UpperCAmelCase__												=     True
						return new_layer






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = None			):





						"""simple docstring"""

						if isinstance(_UpperCamelCase						,      nn.Linear			):
												return prune_linear_layer(_UpperCamelCase						,      _UpperCamelCase						,      dim=0 if dim is None else dim			)
						elif isinstance(_UpperCamelCase						,      _UpperCamelCase			):
												return prune_convad_layer(_UpperCamelCase						,      _UpperCamelCase						,      dim=1 if dim is None else dim			)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}"""			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      *_UpperCamelCase			):





						"""simple docstring"""

						assert len(_UpperCamelCase			) > 0, F"""{input_tensors} has to be a tuple/list of tensors"""

						# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
						UpperCAmelCase__												=     len(inspect.signature(_UpperCamelCase			).parameters			)
						if num_args_in_forward_chunk_fn != len(_UpperCamelCase			):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_UpperCamelCase			)} input """
												    '''tensors are given'''			)

						if chunk_size > 0:
												UpperCAmelCase__												=     input_tensors[0].shape[chunk_dim]
												for input_tensor in input_tensors:
																		if input_tensor.shape[chunk_dim] != tensor_shape:
																								raise ValueError(
																								    F"""All input tenors have to be of the same shape: {tensor_shape}, """
																								    F"""found shape {input_tensor.shape[chunk_dim]}"""			)

												if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																		raise ValueError(
																		    F"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
																		    F"""size {chunk_size}"""			)

												UpperCAmelCase__												=     input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												UpperCAmelCase__												=     tuple(input_tensor.chunk(_UpperCamelCase						,      dim=_UpperCamelCase			) for input_tensor in input_tensors			)
												# apply forward fn to every tuple
												UpperCAmelCase__												=     tuple(forward_fn(*_UpperCamelCase			) for input_tensors_chunk in zip(*_UpperCamelCase			)			)
												# concatenate output at same dimension
												return torch.cat(_UpperCamelCase						,      dim=_UpperCamelCase			)

						return forward_fn(*_UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     torch.ones(_UpperCamelCase						,      _UpperCamelCase			)
						UpperCAmelCase__												=     set(_UpperCamelCase			) - already_pruned_heads  # Convert to set and remove already pruned heads
						for head in heads:
												# Compute how many pruned heads are before the head and move the index accordingly
												UpperCAmelCase__												=     head - sum(1 if h < head else 0 for h in already_pruned_heads			)
												UpperCAmelCase__												=     0
						UpperCAmelCase__												=     mask.view(-1			).contiguous().eq(1			)
						UpperCAmelCase__												=     torch.arange(len(_UpperCamelCase			)			)[mask].long()
						return heads, index






def        a__			(							*_UpperCamelCase						,      _UpperCamelCase = None			):





						"""simple docstring"""

						return torch.meshgrid(*_UpperCamelCase						,      indexing=_UpperCamelCase			)






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						return tensor.device, storage_ptr(_UpperCamelCase			), storage_size(_UpperCamelCase			)