


from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


_snake_case	:						Optional[Any]         =							[
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
_snake_case	:						Dict         =							"UperNetConfig"

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Union[int, Tuple[int, int]]       ,      lowerCamelCase			:    Union[int, Tuple[int, int], str] = 0       ,      lowerCamelCase			:    bool = False       ,      lowerCamelCase			:    Union[int, Tuple[int, int]] = 1       ,      )      ->       None:
														super().__init__()
														__snake_case						:     Union[str, Any]									=  nn.Convad(
														    in_channels=lowerCamelCase       ,      out_channels=lowerCamelCase       ,      kernel_size=lowerCamelCase       ,      padding=lowerCamelCase       ,      bias=lowerCamelCase       ,      dilation=lowerCamelCase       ,      )
														__snake_case						:     Dict									=  nn.BatchNormad(lowerCamelCase							)
														__snake_case						:     List[Any]									=  nn.ReLU()






							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    torch.Tensor							)      ->       torch.Tensor:
														__snake_case						:     Dict									=  self.conv(lowerCamelCase							)
														__snake_case						:     int									=  self.batch_norm(lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  self.activation(lowerCamelCase							)

														return output

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    str       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int							)      ->       None:
														super().__init__()
														__snake_case						:     Tuple									=  [
														    nn.AdaptiveAvgPoolad(lowerCamelCase							),
														    UperNetConvModule(lowerCamelCase       ,      lowerCamelCase       ,      kernel_size=1							),
														]
														for i, layer in enumerate(self.layers							):
																					self.add_module(str(lowerCamelCase							)       ,      lowerCamelCase							)






							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    torch.Tensor							)      ->       torch.Tensor:
														__snake_case						:     List[str]									=  input
														for layer in self.layers:
																					__snake_case						:     Tuple									=  layer(lowerCamelCase							)
														return hidden_state

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    Any       ,      lowerCamelCase			:    Tuple[int, ...]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    bool							)      ->       None:
														super().__init__()
														__snake_case						:     Dict									=  pool_scales
														__snake_case						:     List[str]									=  align_corners
														__snake_case						:     List[Any]									=  in_channels
														__snake_case						:     str									=  channels
														__snake_case						:     Optional[Any]									=  []
														for i, pool_scale in enumerate(lowerCamelCase							):
																					__snake_case						:     Union[str, Any]									=  UperNetPyramidPoolingBlock(pool_scale=lowerCamelCase       ,      in_channels=lowerCamelCase       ,      channels=lowerCamelCase							)
																					self.blocks.append(lowerCamelCase							)
																					self.add_module(str(lowerCamelCase							)       ,      lowerCamelCase							)






							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    torch.Tensor							)      ->       List[torch.Tensor]:
														__snake_case						:     Tuple									=  []
														for ppm in self.blocks:
																					__snake_case						:     Any									=  ppm(lowerCamelCase							)
																					__snake_case						:     List[Any]									=  nn.functional.interpolate(
																					    lowerCamelCase       ,      size=x.size()[2:]       ,      mode="bilinear"       ,      align_corners=self.align_corners							)
																					ppm_outs.append(lowerCamelCase							)
														return ppm_outs

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    int       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    str							)      ->       List[str]:
														super().__init__()

														__snake_case						:     Dict									=  config
														__snake_case						:     List[str]									=  config.pool_scales  # e.g. (1, 2, 3, 6)
														__snake_case						:     Tuple									=  in_channels
														__snake_case						:     str									=  config.hidden_size
														__snake_case						:     List[str]									=  False
														__snake_case						:     Any									=  nn.Convad(self.channels       ,      config.num_labels       ,      kernel_size=1							)

														# PSP Module
														__snake_case						:     Tuple									=  UperNetPyramidPoolingModule(
														    self.pool_scales       ,      self.in_channels[-1]       ,      self.channels       ,      align_corners=self.align_corners       ,      )
														__snake_case						:     List[str]									=  UperNetConvModule(
														    self.in_channels[-1] + len(self.pool_scales							) * self.channels       ,      self.channels       ,      kernel_size=3       ,      padding=1       ,      )
														# FPN Module
														__snake_case						:     List[Any]									=  nn.ModuleList()
														__snake_case						:     Dict									=  nn.ModuleList()
														for in_channels in self.in_channels[:-1]:  # skip the top layer
																					__snake_case						:     Union[str, Any]									=  UperNetConvModule(lowerCamelCase       ,      self.channels       ,      kernel_size=1							)
																					__snake_case						:     Optional[int]									=  UperNetConvModule(self.channels       ,      self.channels       ,      kernel_size=3       ,      padding=1							)
																					self.lateral_convs.append(lowerCamelCase							)
																					self.fpn_convs.append(lowerCamelCase							)

														__snake_case						:     int									=  UperNetConvModule(
														    len(self.in_channels							) * self.channels       ,      self.channels       ,      kernel_size=3       ,      padding=1       ,      )

							def        __snake_case   (				self			:    List[str]							)      ->       Optional[Any]:
														self.apply(self._init_weights							)

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    List[Any]							)      ->       str:
														if isinstance(lowerCamelCase       ,      nn.Convad							):
																					module.weight.data.normal_(mean=0.0       ,      std=self.config.initializer_range							)
																					if module.bias is not None:
																												module.bias.data.zero_()

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Tuple							)      ->       Optional[int]:
														__snake_case						:     str									=  inputs[-1]
														__snake_case						:     int									=  [x]
														psp_outs.extend(self.psp_modules(lowerCamelCase							)							)
														__snake_case						:     Tuple									=  torch.cat(lowerCamelCase       ,      dim=1							)
														__snake_case						:     Union[str, Any]									=  self.bottleneck(lowerCamelCase							)

														return output






							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    torch.Tensor							)      ->       torch.Tensor:
														# build laterals
														__snake_case						:     Any									=  [lateral_conv(encoder_hidden_states[i]							) for i, lateral_conv in enumerate(self.lateral_convs							)]

														laterals.append(self.psp_forward(lowerCamelCase							)							)

														# build top-down path
														__snake_case						:     Dict									=  len(lowerCamelCase							)
														for i in range(used_backbone_levels - 1       ,      0       ,      -1							):
																					__snake_case						:     Union[str, Any]									=  laterals[i - 1].shape[2:]
																					__snake_case						:     Optional[int]									=  laterals[i - 1] + nn.functional.interpolate(
																					    laterals[i]       ,      size=lowerCamelCase       ,      mode="bilinear"       ,      align_corners=self.align_corners							)

														# build outputs
														__snake_case						:     str									=  [self.fpn_convs[i](laterals[i]							) for i in range(used_backbone_levels - 1							)]
														# append psp feature
														fpn_outs.append(laterals[-1]							)

														for i in range(used_backbone_levels - 1       ,      0       ,      -1							):
																					__snake_case						:     Tuple									=  nn.functional.interpolate(
																					    fpn_outs[i]       ,      size=fpn_outs[0].shape[2:]       ,      mode="bilinear"       ,      align_corners=self.align_corners							)
														__snake_case						:     str									=  torch.cat(lowerCamelCase       ,      dim=1							)
														__snake_case						:     Optional[Any]									=  self.fpn_bottleneck(lowerCamelCase							)
														__snake_case						:     Tuple									=  self.classifier(lowerCamelCase							)

														return output

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    Any       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    int = 2       ,      lowerCamelCase			:    int = 3       ,      lowerCamelCase			:    Union[int, Tuple[int, int]] = 1							)      ->       None:
														super().__init__()

														__snake_case						:     List[Any]									=  config
														__snake_case						:     List[str]									=  config.auxiliary_in_channels
														__snake_case						:     List[Any]									=  config.auxiliary_channels
														__snake_case						:     Tuple									=  config.auxiliary_num_convs
														__snake_case						:     int									=  config.auxiliary_concat_input
														__snake_case						:     Optional[int]									=  in_index

														__snake_case						:     Tuple									=  (kernel_size // 2) * dilation
														__snake_case						:     Optional[int]									=  []
														convs.append(
														    UperNetConvModule(
														        self.in_channels       ,      self.channels       ,      kernel_size=lowerCamelCase       ,      padding=lowerCamelCase       ,      dilation=lowerCamelCase							)							)
														for i in range(self.num_convs - 1							):
																					convs.append(
																					    UperNetConvModule(
																					        self.channels       ,      self.channels       ,      kernel_size=lowerCamelCase       ,      padding=lowerCamelCase       ,      dilation=lowerCamelCase							)							)
														if self.num_convs == 0:
																					__snake_case						:     Union[str, Any]									=  nn.Identity()
														else:
																					__snake_case						:     Any									=  nn.Sequential(*lowerCamelCase							)
														if self.concat_input:
																					__snake_case						:     int									=  UperNetConvModule(
																					    self.in_channels + self.channels       ,      self.channels       ,      kernel_size=lowerCamelCase       ,      padding=kernel_size // 2							)

														__snake_case						:     Optional[Any]									=  nn.Convad(self.channels       ,      config.num_labels       ,      kernel_size=1							)

							def        __snake_case   (				self			:    Dict							)      ->       Optional[Any]:
														self.apply(self._init_weights							)

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    Tuple							)      ->       Optional[int]:
														if isinstance(lowerCamelCase       ,      nn.Convad							):
																					module.weight.data.normal_(mean=0.0       ,      std=self.config.initializer_range							)
																					if module.bias is not None:
																												module.bias.data.zero_()






							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    torch.Tensor							)      ->       torch.Tensor:
														# just take the relevant feature maps
														__snake_case						:     List[str]									=  encoder_hidden_states[self.in_index]
														__snake_case						:     Optional[Any]									=  self.convs(lowerCamelCase							)
														if self.concat_input:
																					__snake_case						:     Optional[Any]									=  self.conv_cat(torch.cat([hidden_states, output]       ,      dim=1							)							)
														__snake_case						:     Union[str, Any]									=  self.classifier(lowerCamelCase							)
														return output

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    UperNetConfig
							__UpperCAmelCase       :				int	    =    "pixel_values"
							__UpperCAmelCase       :				str	    =    True

							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    List[Any]							)      ->       List[Any]:
														if isinstance(lowerCamelCase       ,      lowerCamelCase							):
																					module.backbone.init_weights()
																					module.decode_head.init_weights()
																					module.auxiliary_head.init_weights()

							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[str]:
														self.backbone.init_weights()
														self.decode_head.init_weights()
														self.auxiliary_head.init_weights()






							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    Optional[Any]=False							)      ->       Dict:
														if isinstance(lowerCamelCase       ,      lowerCamelCase							):
																					__snake_case						:     Union[str, Any]									=  value
_snake_case	:						Dict         =							R"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_snake_case	:						Tuple         =							R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"

@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."	,							_lowerCAmelCase	,							)
class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def __init__(				self			:    Dict       ,      lowerCamelCase			:    int							)      ->       Optional[int]:
														super().__init__(lowerCamelCase							)

														__snake_case						:     Any									=  AutoBackbone.from_config(config.backbone_config							)

														# Semantic segmentation head(s)
														__snake_case						:     Union[str, Any]									=  UperNetHead(lowerCamelCase       ,      in_channels=self.backbone.channels							)
														__snake_case						:     Any									=  UperNetFCNHead(lowerCamelCase							) if config.use_auxiliary_head else None

														# Initialize weights and apply final processing
														self.post_init()






							@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"							)							)
							@replace_return_docstrings(output_type=lowerCamelCase       ,      config_class=_CONFIG_FOR_DOC							)
							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[torch.Tensor] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      lowerCamelCase			:    Optional[torch.Tensor] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      )      ->       Union[tuple, SemanticSegmenterOutput]:
														__snake_case						:     Any									=  return_dict if return_dict is not None else self.config.use_return_dict
														__snake_case						:     int									=  (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														__snake_case						:     str									=  output_attentions if output_attentions is not None else self.config.output_attentions

														__snake_case						:     Tuple									=  self.backbone.forward_with_filtered_kwargs(
														    lowerCamelCase       ,      output_hidden_states=lowerCamelCase       ,      output_attentions=lowerCamelCase							)
														__snake_case						:     List[Any]									=  outputs.feature_maps

														__snake_case						:     List[Any]									=  self.decode_head(lowerCamelCase							)
														__snake_case						:     List[str]									=  nn.functional.interpolate(lowerCamelCase       ,      size=pixel_values.shape[2:]       ,      mode="bilinear"       ,      align_corners=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  None
														if self.auxiliary_head is not None:
																					__snake_case						:     Dict									=  self.auxiliary_head(lowerCamelCase							)
																					__snake_case						:     Dict									=  nn.functional.interpolate(
																					    lowerCamelCase       ,      size=pixel_values.shape[2:]       ,      mode="bilinear"       ,      align_corners=lowerCamelCase							)

														__snake_case						:     int									=  None
														if labels is not None:
																					if self.config.num_labels == 1:
																												raise ValueError("The number of labels should be greater than one"							)
																					else:
																												# compute weighted loss
																												__snake_case						:     Any									=  CrossEntropyLoss(ignore_index=self.config.loss_ignore_index							)
																												__snake_case						:     Union[str, Any]									=  loss_fct(lowerCamelCase       ,      lowerCamelCase							)
																												__snake_case						:     Optional[Any]									=  loss_fct(lowerCamelCase       ,      lowerCamelCase							)
																												__snake_case						:     Optional[Any]									=  main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

														if not return_dict:
																					if output_hidden_states:
																												__snake_case						:     Any									=  (logits,) + outputs[1:]
																					else:
																												__snake_case						:     str									=  (logits,) + outputs[2:]
																					return ((loss,) + output) if loss is not None else output

														return SemanticSegmenterOutput(
														    loss=lowerCamelCase       ,      logits=lowerCamelCase       ,      hidden_states=outputs.hidden_states       ,      attentions=outputs.attentions       ,      )








import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase		:  str			       =16
_lowercase		:  int			       =32


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__ = 16							,lowerCAmelCase__ = "bert-base-cased"						):
		lowerCamelCase_	:     Any 						=      AutoTokenizer.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     Tuple 						=      load_dataset('glue'							,'mrpc'						)

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

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

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

		def collate_fn(lowerCAmelCase__						):
				# 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(lowerCAmelCase__							,padding='max_length'							,max_length=1_28							,return_tensors='pt'						)
				return tokenizer.pad(lowerCAmelCase__							,padding='longest'							,return_tensors='pt'						)

		# Instantiate dataloaders.
		lowerCamelCase_	:     int 						=      DataLoader(
		    tokenized_datasets['train']							,shuffle=lowerCAmelCase__							,collate_fn=lowerCAmelCase__							,batch_size=lowerCAmelCase__						)
		lowerCamelCase_	:     str 						=      DataLoader(
		    tokenized_datasets['validation']							,shuffle=lowerCAmelCase__							,collate_fn=lowerCAmelCase__							,batch_size=lowerCAmelCase__						)

		return train_dataloader, eval_dataloader


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		model.eval()
		lowerCamelCase_	:     List[str] 						=      0
		for step, batch in enumerate(lowerCAmelCase__						):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device						)
				with torch.no_grad():
						lowerCamelCase_	:     Dict 						=      model(**lowerCAmelCase__						)
				lowerCamelCase_	:     Any 						=      outputs.logits.argmax(dim=-1						)
				# It is slightly faster to call this once, than multiple times
				lowerCamelCase_						,      lowerCamelCase_	:     Tuple 						=      accelerator.gather(
				    (predictions, batch['labels'])						)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(lowerCAmelCase__						) - 1:
								lowerCamelCase_	:     Union[str, Any] 						=      predictions[: len(eval_dataloader.dataset						) - samples_seen]
								lowerCamelCase_	:     List[str] 						=      references[: len(eval_dataloader.dataset						) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=lowerCAmelCase__							,references=lowerCAmelCase__							,)

		lowerCamelCase_	:     Tuple 						=      metric.compute()
		return eval_metric["accuracy"]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		# Initialize accelerator
		lowerCamelCase_	:     List[Any] 						=      Accelerator()

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

		set_seed(lowerCAmelCase__						)
		lowerCamelCase_						,      lowerCamelCase_	:     List[Any] 						=      get_dataloaders(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCamelCase_	:     List[Any] 						=      AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__							,return_dict=lowerCAmelCase__						)

		# Instantiate optimizer
		lowerCamelCase_	:     List[Any] 						=      (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCamelCase_	:     Union[str, Any] 						=      optimizer_cls(params=model.parameters()							,lr=lowerCAmelCase__						)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCamelCase_	:     Tuple 						=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				lowerCamelCase_	:     Tuple 						=      1
		lowerCamelCase_	:     str 						=      (len(lowerCAmelCase__						) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCamelCase_	:     Tuple 						=      get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase__							,num_warmup_steps=0							,num_training_steps=lowerCAmelCase__							,)
		else:
				lowerCamelCase_	:     Optional[int] 						=      DummyScheduler(lowerCAmelCase__							,total_num_steps=lowerCAmelCase__							,warmup_num_steps=0						)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      accelerator.prepare(
		    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

		# We need to keep track of how many total steps we have iterated over
		lowerCamelCase_	:     List[str] 						=      0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCamelCase_	:     Any 						=      0
		lowerCamelCase_	:     Tuple 						=      evaluate.load('glue'							,'mrpc'						)
		lowerCamelCase_	:     List[Any] 						=      num_epochs

		if args.partial_train_epoch is not None:
				lowerCamelCase_	:     Any 						=      args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint						)
				lowerCamelCase_	:     List[Any] 						=      args.resume_from_checkpoint.split('epoch_'						)[1]
				lowerCamelCase_	:     str 						=      ''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCamelCase_	:     List[str] 						=      int(lowerCAmelCase__						) + 1
				lowerCamelCase_	:     Optional[Any] 						=      evaluation_loop(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
				accelerator.print('resumed checkpoint performance:'							,lowerCAmelCase__						)
				accelerator.print('resumed checkpoint\'s scheduler\'s lr:'							,lr_scheduler.get_lr()[0]						)
				accelerator.print('resumed optimizers\'s lr:'							,optimizer.param_groups[0]['lr']						)
				with open(os.path.join(args.output_dir							,F"state_{starting_epoch-1}.json"						)							,'r'						) as f:
						lowerCamelCase_	:     Union[str, Any] 						=      json.load(lowerCAmelCase__						)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		lowerCamelCase_	:     str 						=      {}
		for epoch in range(lowerCAmelCase__							,lowerCAmelCase__						):
				model.train()
				for step, batch in enumerate(lowerCAmelCase__						):
						lowerCamelCase_	:     List[Any] 						=      model(**lowerCAmelCase__						)
						lowerCamelCase_	:     Tuple 						=      outputs.loss
						lowerCamelCase_	:     str 						=      loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase__						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				lowerCamelCase_	:     int 						=      F"epoch_{epoch}"
				lowerCamelCase_	:     Optional[int] 						=      os.path.join(args.output_dir							,lowerCAmelCase__						)
				accelerator.save_state(lowerCAmelCase__						)
				lowerCamelCase_	:     Optional[Any] 						=      evaluation_loop(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
				lowerCamelCase_	:     List[str] 						=      accuracy
				lowerCamelCase_	:     List[str] 						=      lr_scheduler.get_lr()[0]
				lowerCamelCase_	:     str 						=      optimizer.param_groups[0]['lr']
				lowerCamelCase_	:     Any 						=      epoch
				lowerCamelCase_	:     Union[str, Any] 						=      overall_step
				accelerator.print(F"epoch {epoch}:"							,lowerCAmelCase__						)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir							,F"state_{epoch}.json"						)							,'w'						) as f:
								json.dump(lowerCAmelCase__							,lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'						)
		parser.add_argument(
		    '--model_name_or_path'							,type=lowerCAmelCase__							,default='bert-base-cased'							,help='Path to pretrained model or model identifier from huggingface.co/models.'							,required=lowerCAmelCase__							,)
		parser.add_argument(
		    '--output_dir'							,type=lowerCAmelCase__							,default='.'							,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'							,)
		parser.add_argument(
		    '--resume_from_checkpoint'							,type=lowerCAmelCase__							,default=lowerCAmelCase__							,help='If the training should continue from a checkpoint folder.'							,)
		parser.add_argument(
		    '--partial_train_epoch'							,type=lowerCAmelCase__							,default=lowerCAmelCase__							,help='If passed, the training will stop after this number of epochs.'							,)
		parser.add_argument(
		    '--num_epochs'							,type=lowerCAmelCase__							,default=2							,help='Number of train epochs.'							,)
		lowerCamelCase_	:     Optional[Any] 						=      parser.parse_args()
		lowerCamelCase_	:     List[Any] 						=      {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(lowerCAmelCase__							,lowerCAmelCase__						)


if __name__ == "__main__":
		main()
