


"""simple docstring"""



import pickle

import numpy as np
from matplotlib import pyplot as plt







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=0.2		,			UpperCamelCase__=0.2		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					bp_numa
       snake_case					:   Dict         =					bp_numa
       snake_case					:   Tuple         =					bp_numa
       snake_case					:   Optional[Any]         =					conva_get[:2]
       snake_case					:   Optional[Any]         =					conva_get[2]
       snake_case					:   List[str]         =					size_pa
       snake_case					:   Dict         =					rate_w
       snake_case					:   Any         =					rate_t
       snake_case					:   Dict         =					[
           np.mat(-1 * np.random.rand(self.conva[0]		,			self.conva[0]		) + 0.5		)
           for i in range(self.conva[1]		)
       ]
       snake_case					:   Tuple         =					np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa		) + 0.5		)
       snake_case					:   List[Any]         =					np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa		) + 0.5		)
       snake_case					:   str         =					-2 * np.random.rand(self.conva[1]		) + 1
       snake_case					:   List[Any]         =					-2 * np.random.rand(self.num_bpa		) + 1
       snake_case					:   Optional[int]         =					-2 * np.random.rand(self.num_bpa		) + 1







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       snake_case					:   Any         =					{
           "num_bp1": self.num_bpa,
           "num_bp2": self.num_bpa,
           "num_bp3": self.num_bpa,
           "conv1": self.conva,
           "step_conv1": self.step_conva,
           "size_pooling1": self.size_poolinga,
           "rate_weight": self.rate_weight,
           "rate_thre": self.rate_thre,
           "w_conv1": self.w_conva,
           "wkj": self.wkj,
           "vji": self.vji,
           "thre_conv1": self.thre_conva,
           "thre_bp2": self.thre_bpa,
           "thre_bp3": self.thre_bpa,
       }
       with open(UpperCamelCase__		,			"wb"		) as f:
           pickle.dump(UpperCamelCase__		,			UpperCamelCase__		)

       print(F'Model saved： {save_path}'		)







   @classmethod
   def 				lowerCamelCase	(			cls		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       with open(UpperCamelCase__		,			"rb"		) as f:
           snake_case					:   Tuple         =					pickle.load(UpperCamelCase__		)  # noqa: S301

       snake_case					:   Optional[Any]         =					model_dic.get("conv1"		)
       conv_get.append(model_dic.get("step_conv1"		)		)
       snake_case					:   Any         =					model_dic.get("size_pooling1"		)
       snake_case					:   Optional[int]         =					model_dic.get("num_bp1"		)
       snake_case					:   Any         =					model_dic.get("num_bp2"		)
       snake_case					:   Tuple         =					model_dic.get("num_bp3"		)
       snake_case					:   List[str]         =					model_dic.get("rate_weight"		)
       snake_case					:   Tuple         =					model_dic.get("rate_thre"		)
       # create model instance
       snake_case					:   Optional[Any]         =					CNN(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)
       # modify model parameter
       snake_case					:   Dict         =					model_dic.get("w_conv1"		)
       snake_case					:   Union[str, Any]         =					model_dic.get("wkj"		)
       snake_case					:   Tuple         =					model_dic.get("vji"		)
       snake_case					:   int         =					model_dic.get("thre_conv1"		)
       snake_case					:   Optional[int]         =					model_dic.get("thre_bp2"		)
       snake_case					:   List[str]         =					model_dic.get("thre_bp3"		)
       return conv_ins







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return 1 / (1 + np.exp(-1 * x		))







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       return round(UpperCamelCase__		,			3		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       snake_case					:   int         =					convs[0]
       snake_case					:   Any         =					convs[1]
       snake_case					:   Optional[int]         =					np.shape(UpperCamelCase__		)[0]
       # get the data slice of original image data, data_focus
       snake_case					:   Any         =					[]
       for i_focus in range(0		,			size_data - size_conv + 1		,			UpperCamelCase__		):
           for j_focus in range(0		,			size_data - size_conv + 1		,			UpperCamelCase__		):
               snake_case					:   Optional[int]         =					data[
                   i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
               ]
               data_focus.append(UpperCamelCase__		)
        # calculate the feature map of every single kernel, and saved as list of matrix
       snake_case					:   List[Any]         =					[]
       snake_case					:   Tuple         =					int((size_data - size_conv) / conv_step + 1		)
       for i_map in range(UpperCamelCase__		):
           snake_case					:   List[Any]         =					[]
           for i_focus in range(len(UpperCamelCase__		)		):
               snake_case					:   Dict         =					(
                   np.sum(np.multiply(data_focus[i_focus]		,			w_convs[i_map]		)		)
                   - thre_convs[i_map]
               )
               featuremap.append(self.sig(UpperCamelCase__		)		)
           snake_case					:   Union[str, Any]         =					np.asmatrix(UpperCamelCase__		).reshape(
               UpperCamelCase__		,			UpperCamelCase__		)
           data_featuremap.append(UpperCamelCase__		)

       # expanding the data slice to One dimenssion
       snake_case					:   Optional[int]         =					[]
       for each_focus in data_focus:
           focusa_list.extend(self.Expand_Mat(UpperCamelCase__		)		)
       snake_case					:   List[str]         =					np.asarray(UpperCamelCase__		)
       return focus_list, data_featuremap







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__="average_pool"		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					len(featuremaps[0]		)
       snake_case					:   List[str]         =					int(size_map / size_pooling		)
       snake_case					:   List[str]         =					[]
       for i_map in range(len(UpperCamelCase__		)		):
           snake_case					:   Tuple         =					featuremaps[i_map]
           snake_case					:   Union[str, Any]         =					[]
           for i_focus in range(0		,			UpperCamelCase__		,			UpperCamelCase__		):
               for j_focus in range(0		,			UpperCamelCase__		,			UpperCamelCase__		):
                   snake_case					:   Optional[Any]         =					feature_map[
                       i_focus : i_focus + size_pooling,
                       j_focus : j_focus + size_pooling,
                   ]
                   if pooling_type == "average_pool":
                       # average pooling
                       map_pooled.append(np.average(UpperCamelCase__		)		)
                   elif pooling_type == "max_pooling":
                       # max pooling
                       map_pooled.append(np.max(UpperCamelCase__		)		)
           snake_case					:   Optional[int]         =					np.asmatrix(UpperCamelCase__		).reshape(UpperCamelCase__		,			UpperCamelCase__		)
           featuremap_pooled.append(UpperCamelCase__		)
       return featuremap_pooled







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   List[str]         =					[]
       for i in range(len(UpperCamelCase__		)		):
           snake_case					:   str         =					np.shape(data[i]		)
           snake_case					:   Optional[Any]         =					data[i].reshape(1		,			shapes[0] * shapes[1]		)
           snake_case					:   Any         =					data_listed.getA().tolist()[0]
           data_expanded.extend(UpperCamelCase__		)
       snake_case					:   int         =					np.asarray(UpperCamelCase__		)
       return data_expanded







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   int         =					np.asarray(UpperCamelCase__		)
       snake_case					:   str         =					np.shape(UpperCamelCase__		)
       snake_case					:   List[str]         =					data_mat.reshape(1		,			shapes[0] * shapes[1]		)
       return data_expanded







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[]
       snake_case					:   Tuple         =					0
       for i_map in range(UpperCamelCase__		):
           snake_case					:   Any         =					np.ones((size_map, size_map)		)
           for i in range(0		,			UpperCamelCase__		,			UpperCamelCase__		):
               for j in range(0		,			UpperCamelCase__		,			UpperCamelCase__		):
                   snake_case					:   Optional[int]         =					pd_pool[
                       i_pool
                   ]
                   snake_case					:   List[Any]         =					i_pool + 1
           snake_case					:   int         =					np.multiply(
               UpperCamelCase__		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])		)		)
           pd_all.append(UpperCamelCase__		)
       return pd_all







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=bool		)			->	Tuple:

       '''simple docstring'''




       print("----------------------Start Training-------------------------"		)
       print((" - - Shape: Train_Data  ", np.shape(UpperCamelCase__		))		)
       print((" - - Shape: Teach_Data  ", np.shape(UpperCamelCase__		))		)
       snake_case					:   Union[str, Any]         =					0
       snake_case					:   Union[str, Any]         =					[]
       snake_case					:   int         =					1_0000
       while rp < n_repeat and mse >= error_accuracy:
           snake_case					:   Optional[Any]         =					0
           print(F'-------------Learning Time {rp}--------------'		)
           for p in range(len(UpperCamelCase__		)		):
               # print('------------Learning Image: %d--------------'%p)
               snake_case					:   Optional[int]         =					np.asmatrix(datas_train[p]		)
               snake_case					:   Union[str, Any]         =					np.asarray(datas_teach[p]		)
               snake_case      ,snake_case					:   Any         =					self.convolute(
                   UpperCamelCase__		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
               snake_case					:   int         =					self.pooling(UpperCamelCase__		,			self.size_poolinga		)
               snake_case					:   List[Any]         =					np.shape(UpperCamelCase__		)
               snake_case					:   List[str]         =					self._expand(UpperCamelCase__		)
               snake_case					:   Tuple         =					data_bp_input

               snake_case					:   Tuple         =					np.dot(UpperCamelCase__		,			self.vji.T		) - self.thre_bpa
               snake_case					:   Tuple         =					self.sig(UpperCamelCase__		)
               snake_case					:   Tuple         =					np.dot(UpperCamelCase__		,			self.wkj.T		) - self.thre_bpa
               snake_case					:   Optional[Any]         =					self.sig(UpperCamelCase__		)

               # --------------Model Leaning ------------------------
               # calculate error and gradient---------------
               snake_case					:   Union[str, Any]         =					np.multiply(
                   (data_teach - bp_outa)		,			np.multiply(UpperCamelCase__		,			(1 - bp_outa)		)		)
               snake_case					:   Dict         =					np.multiply(
                   np.dot(UpperCamelCase__		,			self.wkj		)		,			np.multiply(UpperCamelCase__		,			(1 - bp_outa)		)		)
               snake_case					:   Tuple         =					np.dot(UpperCamelCase__		,			self.vji		)

               snake_case					:   str         =					pd_i_all / (self.size_poolinga * self.size_poolinga)
               snake_case					:   str         =					pd_conva_pooled.T.getA().tolist()
               snake_case					:   str         =					self._calculate_gradient_from_pool(
                   UpperCamelCase__		,			UpperCamelCase__		,			shape_featuremapa[0]		,			shape_featuremapa[1]		,			self.size_poolinga		,			)
               # weight and threshold learning process---------
               # convolution layer
               for k_conv in range(self.conva[1]		):
                   snake_case					:   int         =					self._expand_mat(pd_conva_all[k_conv]		)
                   snake_case					:   List[Any]         =					self.rate_weight * np.dot(UpperCamelCase__		,			UpperCamelCase__		)

                   snake_case					:   Tuple         =					self.w_conva[k_conv] + delta_w.reshape(
                       (self.conva[0], self.conva[0])		)

                   snake_case					:   Any         =					(
                       self.thre_conva[k_conv]
                       - np.sum(pd_conva_all[k_conv]		) * self.rate_thre
                   )
               # all connected layer
               snake_case					:   Any         =					self.wkj + pd_k_all.T * bp_outa * self.rate_weight
               snake_case					:   int         =					self.vji + pd_j_all.T * bp_outa * self.rate_weight
               snake_case					:   List[Any]         =					self.thre_bpa - pd_k_all * self.rate_thre
               snake_case					:   Union[str, Any]         =					self.thre_bpa - pd_j_all * self.rate_thre
               # calculate the sum error of all single image
               snake_case					:   Dict         =					np.sum(abs(data_teach - bp_outa		)		)
               error_count += errors
               # print('   ----Teach      ',data_teach)
               # print('   ----BP_output  ',bp_out3)
           snake_case					:   Dict         =					rp + 1
           snake_case					:   Optional[int]         =					error_count / patterns
           all_mse.append(UpperCamelCase__		)

       def draw_error():
           snake_case					:   Optional[int]         =					[error_accuracy for i in range(int(n_repeat * 1.2		)		)]
           plt.plot(UpperCamelCase__		,			"+-"		)
           plt.plot(UpperCamelCase__		,			"r--"		)
           plt.xlabel("Learning Times"		)
           plt.ylabel("All_mse"		)
           plt.grid(UpperCamelCase__		,			alpha=0.5		)
           plt.show()

       print("------------------Training Complished---------------------"		)
       print((" - - Training epoch: ", rp, F'     - - Mse: {mse:.6f}')		)
       if draw_e:
           draw_error()
       return mse







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   int         =					[]
       print("-------------------Start Testing-------------------------"		)
       print((" - - Shape: Test_Data  ", np.shape(UpperCamelCase__		))		)
       for p in range(len(UpperCamelCase__		)		):
           snake_case					:   List[Any]         =					np.asmatrix(datas_test[p]		)
           snake_case      ,snake_case					:   Tuple         =					self.convolute(
               UpperCamelCase__		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
           snake_case					:   Any         =					self.pooling(UpperCamelCase__		,			self.size_poolinga		)
           snake_case					:   int         =					self._expand(UpperCamelCase__		)

           snake_case					:   Optional[Any]         =					data_bp_input
           snake_case					:   List[str]         =					bp_outa * self.vji.T - self.thre_bpa
           snake_case					:   Any         =					self.sig(UpperCamelCase__		)
           snake_case					:   Dict         =					bp_outa * self.wkj.T - self.thre_bpa
           snake_case					:   Optional[Any]         =					self.sig(UpperCamelCase__		)
           produce_out.extend(bp_outa.getA().tolist()		)
       snake_case					:   Dict         =					[list(map(self.do_round		,			UpperCamelCase__		)		) for each in produce_out]
       return np.asarray(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Dict         =					np.asmatrix(UpperCamelCase__		)
       snake_case      ,snake_case					:   str         =					self.convolute(
           UpperCamelCase__		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
       snake_case					:   int         =					self.pooling(UpperCamelCase__		,			self.size_poolinga		)

       return data_conveda, data_pooleda


if __name__ == "__main__":
  pass









"""simple docstring"""



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   int         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
    snake_case					:   str         =					load_dataset("glue"		,							"mrpc"				)

    def tokenize_function(lowercase						:						Tuple				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[str]         =					tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowercase		,							max_length=lowercase				)
        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():
        snake_case					:   Any         =					datasets.map(
            lowercase		,							batched=lowercase		,							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
    snake_case					:   Tuple         =					tokenized_datasets.rename_column("label"		,							"labels"				)

    def collate_fn(lowercase						:						str				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case					:   Any         =					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":
            snake_case					:   str         =					16
        elif accelerator.mixed_precision != "no":
            snake_case					:   List[Any]         =					8
        else:
            snake_case					:   Union[str, Any]         =					None

        return tokenizer.pad(
            lowercase		,							padding="longest"		,							max_length=lowercase		,							pad_to_multiple_of=lowercase		,							return_tensors="pt"		,							)

    # Instantiate dataloaders.
    snake_case					:   Any         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   List[str]         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

    return train_dataloader, eval_dataloader


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

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						int				)			->		Tuple:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Union[str, Any]         =					2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        snake_case					:   str         =					Accelerator(
            cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with="all"		,							project_dir=args.project_dir				)
    else:
        snake_case					:   Optional[int]         =					Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision				)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case					:   str         =					config["lr"]
    snake_case					:   Dict         =					int(config["num_epochs"]				)
    snake_case					:   int         =					int(config["seed"]				)
    snake_case					:   Tuple         =					int(config["batch_size"]				)
    set_seed(lowercase				)

    snake_case      ,snake_case					:   List[Any]         =					get_dataloaders(lowercase		,							lowercase				)
    snake_case					:   Optional[Any]         =					evaluate.load("glue"		,							"mrpc"				)

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

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case					:   List[Any]         =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=lowercase				)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    snake_case					:   str         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Optional[Any]         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps		,							)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Union[str, Any]         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        snake_case					:   str         =					os.path.split(lowercase				)[-1].split("."				)[0]
        accelerator.init_trackers(lowercase		,							lowercase				)

    # Now we train the model
    for epoch in range(lowercase				):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            snake_case					:   Any         =					0
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            snake_case					:   Dict         =					model(**lowercase				)
            snake_case					:   int         =					outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            snake_case					:   Union[str, Any]         =					loss / gradient_accumulation_steps
            accelerator.backward(lowercase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
            batch.to(accelerator.device				)
            with torch.no_grad():
                snake_case					:   Optional[int]         =					model(**lowercase				)
            snake_case					:   Tuple         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   List[str]         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

        snake_case					:   Optional[Any]         =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,							lowercase				)

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(lowercase				),
                    "epoch": epoch,
                }		,							step=lowercase		,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()







def 						__lowerCAmelCase		(		)			->		str:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					argparse.ArgumentParser(description="Simple example of training script."				)
    parser.add_argument(
        "--mixed_precision"		,							type=lowercase		,							default=lowercase		,							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(
        "--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(
        "--project_dir"		,							type=lowercase		,							default="logs"		,							help="Location on where to store experiment tracking logs` and relevent project information"		,							)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   List[str]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()





