



"""simple docstring"""

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__					:						Optional[int]												=			16
lowercase__					:						Any												=			32




def __lowercase    (							_a	,    _a = 16	,    _a = "bert-base-cased"					):
    snake_case_				:  Tuple       =			AutoTokenizer.from_pretrained(_a					)
    snake_case_				:  List[Any]       =			load_dataset('''glue'''	,    '''mrpc'''					)

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

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

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

    def collate_fn(_a					):
        # 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(_a	,    padding='''max_length'''	,    max_length=128	,    return_tensors='''pt'''					)
        return tokenizer.pad(_a	,    padding='''longest'''	,    return_tensors='''pt'''					)

    # Instantiate dataloaders.
    snake_case_				:  List[Any]       =			DataLoader(
        tokenized_datasets['''train''']	,    shuffle=_a	,    collate_fn=_a	,    batch_size=_a					)
    snake_case_				:  Any       =			DataLoader(
        tokenized_datasets['''validation''']	,    shuffle=_a	,    collate_fn=_a	,    batch_size=_a					)

    return train_dataloader, eval_dataloader




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

    snake_case_				:  str       =			metric.compute()
    return eval_metric["accuracy"]




def __lowercase    (							_a	,    _a					):
    # Initialize accelerator
    snake_case_				:  List[str]       =			Accelerator()

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

    set_seed(_a					)
    snake_case_				:  Union[str, Any]       =			get_dataloaders(_a	,    _a	,    _a					)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_				:  str       =			AutoModelForSequenceClassification.from_pretrained(_a	,    return_dict=_a					)

    # Instantiate optimizer
    snake_case_				:  Optional[Any]       =			(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case_				:  Optional[int]       =			optimizer_cls(params=model.parameters()	,    lr=_a					)

    if accelerator.state.deepspeed_plugin is not None:
        snake_case_				:  Optional[int]       =			accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        snake_case_				:  Dict       =			1
    snake_case_				:  Tuple       =			(len(_a					) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case_				:  List[Any]       =			get_linear_schedule_with_warmup(
            optimizer=_a	,    num_warmup_steps=0	,    num_training_steps=_a	,    )
    else:
        snake_case_				:  Any       =			DummyScheduler(_a	,    total_num_steps=_a	,    warmup_num_steps=0					)

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

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

    if args.partial_train_epoch is not None:
        snake_case_				:  Dict       =			args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint					)
        snake_case_				:  Tuple       =			args.resume_from_checkpoint.split('''epoch_'''					)[1]
        snake_case_				:  Optional[int]       =			''''''
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        snake_case_				:  Tuple       =			int(_a					) + 1
        snake_case_				:  Optional[int]       =			evaluation_loop(_a	,    _a	,    _a	,    _a					)
        accelerator.print('''resumed checkpoint performance:'''	,    _a					)
        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:
            snake_case_				:  List[str]       =			json.load(_a					)
            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
    snake_case_				:  Tuple       =			{}
    for epoch in range(_a	,    _a					):
        model.train()
        for step, batch in enumerate(_a					):
            snake_case_				:  int       =			model(**_a					)
            snake_case_				:  Optional[int]       =			outputs.loss
            snake_case_				:  List[str]       =			loss / gradient_accumulation_steps
            accelerator.backward(_a					)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        snake_case_				:  int       =			f"epoch_{epoch}"
        snake_case_				:  Dict       =			os.path.join(args.output_dir	,    _a					)
        accelerator.save_state(_a					)
        snake_case_				:  Any       =			evaluation_loop(_a	,    _a	,    _a	,    _a					)
        snake_case_				:  Tuple       =			accuracy
        snake_case_				:  str       =			lr_scheduler.get_lr()[0]
        snake_case_				:  List[str]       =			optimizer.param_groups[0]['''lr''']
        snake_case_				:  List[Any]       =			epoch
        snake_case_				:  List[str]       =			overall_step
        accelerator.print(f"epoch {epoch}:"	,    _a					)

        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(_a	,    _a					)




def __lowercase    (							):
    snake_case_				:  int       =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''					)
    parser.add_argument(
        '''--model_name_or_path'''	,    type=_a	,    default='''bert-base-cased'''	,    help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,    required=_a	,    )
    parser.add_argument(
        '''--output_dir'''	,    type=_a	,    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=_a	,    default=_a	,    help='''If the training should continue from a checkpoint folder.'''	,    )
    parser.add_argument(
        '''--partial_train_epoch'''	,    type=_a	,    default=_a	,    help='''If passed, the training will stop after this number of epochs.'''	,    )
    parser.add_argument(
        '''--num_epochs'''	,    type=_a	,    default=2	,    help='''Number of train epochs.'''	,    )
    snake_case_				:  Any       =			parser.parse_args()
    snake_case_				:  Dict       =			{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

    training_function(_a	,    _a					)


if __name__ == "__main__":
     main()










"""simple docstring"""

import tensorflow as tf

from ...tf_utils import shape_list




class  _UpperCAmelCase (    tf.keras.layers.Layer):

 def __init__(  self	: int						,  lowercase_	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: Tuple						,  lowercase_	: List[Any]						,  lowercase_	: Tuple=1						,  lowercase_	: List[str]=False						,  **lowercase_	: Optional[Any]       ):
     super().__init__(**lowercase_       )

     snake_case_				:  int       =			vocab_size
     snake_case_				:  Union[str, Any]       =			d_embed
     snake_case_				:  Optional[int]       =			d_proj

     snake_case_				:  int       =			cutoffs + [vocab_size]
     snake_case_				:  Optional[int]       =			[0] + self.cutoffs
     snake_case_				:  List[str]       =			div_val

     snake_case_				:  int       =			self.cutoffs[0]
     snake_case_				:  Optional[Any]       =			len(self.cutoffs       ) - 1
     snake_case_				:  Any       =			self.shortlist_size + self.n_clusters
     snake_case_				:  Dict       =			keep_order

     snake_case_				:  Tuple       =			[]
     snake_case_				:  Optional[Any]       =			[]

 def 		_snake_case      (  self	: Dict						,  lowercase_	: int       ):
     if self.n_clusters > 0:
         snake_case_				:  Tuple       =			self.add_weight(
             shape=(self.n_clusters, self.d_embed)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name='''cluster_weight'''       )
         snake_case_				:  List[str]       =			self.add_weight(
             shape=(self.n_clusters,)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name='''cluster_bias'''       )

     if self.div_val == 1:
         for i in range(len(self.cutoffs       )       ):
             if self.d_proj != self.d_embed:
                 snake_case_				:  List[Any]       =			self.add_weight(
                     shape=(self.d_embed, self.d_proj)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_projs_._{i}"						,  )
                 self.out_projs.append(lowercase_       )
             else:
                 self.out_projs.append(lowercase_       )
             snake_case_				:  Any       =			self.add_weight(
                 shape=(self.vocab_size, self.d_embed)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._weight"						,  )
             snake_case_				:  Any       =			self.add_weight(
                 shape=(self.vocab_size,)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._bias"						,  )
             self.out_layers.append((weight, bias)       )
     else:
         for i in range(len(self.cutoffs       )       ):
             snake_case_,			snake_case_				:  str       =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
             snake_case_				:  List[str]       =			self.d_embed // (self.div_val**i)

             snake_case_				:  Optional[Any]       =			self.add_weight(
                 shape=(d_emb_i, self.d_proj)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_projs_._{i}"       )
             self.out_projs.append(lowercase_       )
             snake_case_				:  Any       =			self.add_weight(
                 shape=(r_idx - l_idx, d_emb_i)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._weight"						,  )
             snake_case_				:  List[Any]       =			self.add_weight(
                 shape=(r_idx - l_idx,)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._bias"						,  )
             self.out_layers.append((weight, bias)       )
     super().build(lowercase_       )

 @staticmethod
 def 		_snake_case      (  lowercase_	: Dict						,  lowercase_	: Union[str, Any]						,  lowercase_	: Optional[int]						,  lowercase_	: str=None       ):
     snake_case_				:  str       =			x
     if proj is not None:
         snake_case_				:  str       =			tf.einsum('''ibd,ed->ibe'''						,  lowercase_						,  lowercase_       )
     return tf.einsum('''ibd,nd->ibn'''						,  lowercase_						,  lowercase_       ) + b

 @staticmethod
 def 		_snake_case      (  lowercase_	: Tuple						,  lowercase_	: Union[str, Any]       ):
     snake_case_				:  Optional[int]       =			shape_list(lowercase_       )
     snake_case_				:  Any       =			tf.range(lp_size[0]						,  dtype=target.dtype       )
     snake_case_				:  Optional[int]       =			tf.stack([r, target]						,  1       )
     return tf.gather_nd(lowercase_						,  lowercase_       )






 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: int						,  lowercase_	: Tuple=True						,  lowercase_	: Tuple=False       ):
     snake_case_				:  Optional[int]       =			0
     if self.n_clusters == 0:
         snake_case_				:  List[str]       =			self._logit(lowercase_						,  self.out_layers[0][0]						,  self.out_layers[0][1]						,  self.out_projs[0]       )
         if target is not None:
             snake_case_				:  List[Any]       =			tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowercase_						,  logits=lowercase_       )
         snake_case_				:  List[Any]       =			tf.nn.log_softmax(lowercase_						,  axis=-1       )
     else:
         snake_case_				:  Optional[int]       =			shape_list(lowercase_       )
         snake_case_				:  Any       =			[]
         snake_case_				:  List[str]       =			tf.zeros(hidden_sizes[:2]       )
         for i in range(len(self.cutoffs       )       ):
             snake_case_,			snake_case_				:  Union[str, Any]       =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
             if target is not None:
                 snake_case_				:  Optional[Any]       =			(target >= l_idx) & (target < r_idx)
                 snake_case_				:  Any       =			tf.where(lowercase_       )
                 snake_case_				:  List[Any]       =			tf.boolean_mask(lowercase_						,  lowercase_       ) - l_idx

             if self.div_val == 1:
                 snake_case_				:  Optional[int]       =			self.out_layers[0][0][l_idx:r_idx]
                 snake_case_				:  List[Any]       =			self.out_layers[0][1][l_idx:r_idx]
             else:
                 snake_case_				:  Dict       =			self.out_layers[i][0]
                 snake_case_				:  str       =			self.out_layers[i][1]

             if i == 0:
                 snake_case_				:  Tuple       =			tf.concat([cur_W, self.cluster_weight]						,  0       )
                 snake_case_				:  List[str]       =			tf.concat([cur_b, self.cluster_bias]						,  0       )

                 snake_case_				:  List[Any]       =			self._logit(lowercase_						,  lowercase_						,  lowercase_						,  self.out_projs[0]       )
                 snake_case_				:  Union[str, Any]       =			tf.nn.log_softmax(lowercase_       )
                 out.append(head_logprob[..., : self.cutoffs[0]]       )
                 if target is not None:
                     snake_case_				:  int       =			tf.boolean_mask(lowercase_						,  lowercase_       )
                     snake_case_				:  str       =			self._gather_logprob(lowercase_						,  lowercase_       )
             else:
                 snake_case_				:  Union[str, Any]       =			self._logit(lowercase_						,  lowercase_						,  lowercase_						,  self.out_projs[i]       )
                 snake_case_				:  List[str]       =			tf.nn.log_softmax(lowercase_       )
                 snake_case_				:  Dict       =			self.cutoffs[0] + i - 1  # No probability for the head cluster
                 snake_case_				:  Union[str, Any]       =			head_logprob[..., cluster_prob_idx, None] + tail_logprob
                 out.append(lowercase_       )
                 if target is not None:
                     snake_case_				:  Optional[int]       =			tf.boolean_mask(lowercase_						,  lowercase_       )
                     snake_case_				:  Dict       =			tf.boolean_mask(lowercase_						,  lowercase_       )
                     snake_case_				:  Union[str, Any]       =			self._gather_logprob(lowercase_						,  lowercase_       )
                     cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
             if target is not None:
                 loss += tf.scatter_nd(lowercase_						,  -cur_logprob						,  shape_list(lowercase_       )       )
         snake_case_				:  Union[str, Any]       =			tf.concat(lowercase_						,  axis=-1       )

     if target is not None:
         if return_mean:
             snake_case_				:  Dict       =			tf.reduce_mean(lowercase_       )
         # Add the training-time loss value to the layer using `self.add_loss()`.
         self.add_loss(lowercase_       )

         # Log the loss as a metric (we could log arbitrary metrics,
         # including different metrics for training and inference.
         self.add_metric(lowercase_						,  name=self.name						,  aggregation='''mean''' if return_mean else ''''''       )

     return out





