


"""simple docstring"""



import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__snake_case       =       logging.getLogger(__name__)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    int					)      ->		int:


    """simple docstring"""





    if os.path.exists(lowercase					):
        if os.path.exists(os.path.join(lowercase		,				"config.json"					)					) and os.path.isfile(
            os.path.join(lowercase		,				"config.json"					)					):
            os.remove(os.path.join(lowercase		,				"config.json"					)					)
        if os.path.exists(os.path.join(lowercase		,				"pytorch_model.bin"					)					) and os.path.isfile(
            os.path.join(lowercase		,				"pytorch_model.bin"					)					):
            os.remove(os.path.join(lowercase		,				"pytorch_model.bin"					)					)
    else:
        os.makedirs(lowercase					)
    model.save_pretrained(lowercase					)







def     __lowerCAmelCase		(		lowercase    :    Any		,				lowercase    :    Union[str, Any]=False					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case       : Union[str, Any]    =       2
    if unlogit:
        snake_case       : int    =       torch.pow(lowercase		,				lowercase					)
    snake_case       : Dict    =       p * torch.log(lowercase					)
    snake_case       : Any    =       0
    return -plogp.sum(dim=-1					)







def     __lowerCAmelCase		(		lowercase    :    List[Any]					)      ->		Dict:


    """simple docstring"""





    logger.info("lv, h >\t" + "\t".join(F'{x + 1}' for x in range(len(lowercase					)					)					)					)
    for row in range(len(lowercase					)					):
        if tensor.dtype != torch.long:
            logger.info(F'layer {row + 1}:\t' + "\t".join(F'{x:.5f}' for x in tensor[row].cpu().data					)					)
        else:
            logger.info(F'layer {row + 1}:\t' + "\t".join(F'{x:d}' for x in tensor[row].cpu().data					)					)







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    List[str]		,				lowercase    :    Optional[int]		,				lowercase    :    List[Any]=True		,				lowercase    :    List[str]=True		,				lowercase    :    str=None		,				lowercase    :    List[str]=False					)      ->		Optional[int]:


    """simple docstring"""





    snake_case   ,snake_case       : Optional[int]    =       model.config.num_hidden_layers, model.config.num_attention_heads
    snake_case       : Optional[int]    =       torch.zeros(lowercase		,				lowercase					).to(args.device					)
    snake_case       : int    =       torch.zeros(lowercase		,				lowercase					).to(args.device					)

    if head_mask is None:
        snake_case       : Any    =       torch.ones(lowercase		,				lowercase					).to(args.device					)

    head_mask.requires_grad_(requires_grad=lowercase					)
    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
    if actually_pruned:
        snake_case       : Tuple    =       None

    snake_case       : Tuple    =       0.0
    snake_case       : Union[str, Any]    =       0.0
    for step, inputs in enumerate(tqdm(lowercase		,				desc="Iteration"		,				disable=args.local_rank not in [-1, 0]					)					):
        snake_case       : int    =       tuple(t.to(args.device					) for t in inputs					)
        ((snake_case)   ,)       : Any    =       inputs

        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
        snake_case       : List[str]    =       model(lowercase		,				labels=lowercase		,				head_mask=lowercase					)
        #  (loss), lm_logits, presents, (all hidden_states), (attentions)
        snake_case   ,snake_case   ,snake_case       : Any    =       (
            outputs[0],
            outputs[1],
            outputs[-1],
        )  # Loss and logits are the first, attention the last
        loss.backward()  # Backpropagate to populate the gradients in the head mask
        total_loss += loss.detach().cpu().numpy()
        if compute_entropy:
            for layer, attn in enumerate(lowercase					):
                snake_case       : Optional[int]    =       entropy(attn.detach()		,				lowercase					)
                attn_entropy[layer] += masked_entropy.sum(-1					).sum(0					).sum(0					).detach()

        if compute_importance:
            head_importance += head_mask.grad.abs().detach()
        tot_tokens += torch.ones_like(lowercase					).float().detach().sum().data

    # Normalize
    attn_entropy /= tot_tokens
    head_importance /= tot_tokens
    # Layerwise importance normalization
    if not args.dont_normalize_importance_by_layer:
        snake_case       : Any    =       2
        snake_case       : Optional[Any]    =       torch.pow(torch.pow(lowercase		,				lowercase					).sum(-1					)		,				1 / exponent					)
        head_importance /= norm_by_layer.unsqueeze(-1					) + 1e-2_0

    if not args.dont_normalize_global_importance:
        snake_case       : Union[str, Any]    =       (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

    # Print matrices
    if compute_entropy:
        logger.info("Attention entropies"					)
        print_ad_tensor(lowercase					)
    if compute_importance:
        logger.info("Head importance scores"					)
        print_ad_tensor(lowercase					)
    logger.info("Head ranked by importance scores"					)
    snake_case       : Optional[int]    =       torch.zeros(head_importance.numel()		,				dtype=torch.long		,				device=args.device					)
    snake_case       : Optional[Any]    =       torch.arange(
        head_importance.numel()		,				device=args.device					)
    snake_case       : Any    =       head_ranks.view_as(lowercase					)
    print_ad_tensor(lowercase					)
    return attn_entropy, head_importance, total_loss







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    Optional[Any]		,				lowercase    :    List[str]					)      ->		List[str]:


    """simple docstring"""





    snake_case   ,snake_case   ,snake_case       : Any    =       compute_heads_importance(lowercase		,				lowercase		,				lowercase		,				compute_entropy=lowercase					)
    snake_case       : str    =       1 / loss  # instead of downsteam score use the LM loss
    logger.info("Pruning: original score: %f, threshold: %f"		,				lowercase		,				original_score * args.masking_threshold					)

    snake_case       : Optional[int]    =       torch.ones_like(lowercase					)
    snake_case       : str    =       max(1		,				int(new_head_mask.numel() * args.masking_amount					)					)

    snake_case       : Dict    =       original_score
    while current_score >= original_score * args.masking_threshold:
        snake_case       : Dict    =       new_head_mask.clone().detach()  # save current head mask
        # heads from least important to most - keep only not-masked heads
        snake_case       : Tuple    =       float("Inf"					)
        snake_case       : Tuple    =       head_importance.view(-1					).sort()[1]

        if len(lowercase					) <= num_to_mask:
            print("BREAK BY num_to_mask"					)
            break

        # mask heads
        snake_case       : Union[str, Any]    =       current_heads_to_mask[:num_to_mask]
        logger.info("Heads to mask: %s"		,				str(current_heads_to_mask.tolist()					)					)
        snake_case       : Dict    =       new_head_mask.view(-1					)
        snake_case       : Tuple    =       0.0
        snake_case       : Optional[Any]    =       new_head_mask.view_as(lowercase					)
        snake_case       : str    =       new_head_mask.clone().detach()
        print_ad_tensor(lowercase					)

        # Compute metric and head importance again
        snake_case   ,snake_case   ,snake_case       : Optional[Any]    =       compute_heads_importance(
            lowercase		,				lowercase		,				lowercase		,				compute_entropy=lowercase		,				head_mask=lowercase					)
        snake_case       : Dict    =       1 / loss
        logger.info(
            "Masking: current score: %f, remaining heads %d (%.1f percents)"		,				lowercase		,				new_head_mask.sum()		,				new_head_mask.sum() / new_head_mask.numel() * 100		,				)

    logger.info("Final head mask"					)
    print_ad_tensor(lowercase					)
    np.save(os.path.join(args.output_dir		,				"head_mask.npy"					)		,				head_mask.detach().cpu().numpy()					)

    return head_mask







def     __lowerCAmelCase		(		lowercase    :    Optional[int]		,				lowercase    :    int		,				lowercase    :    Union[str, Any]		,				lowercase    :    Tuple					)      ->		Dict:


    """simple docstring"""





    snake_case       : Dict    =       datetime.now()
    snake_case   ,snake_case   ,snake_case       : List[str]    =       compute_heads_importance(
        lowercase		,				lowercase		,				lowercase		,				compute_entropy=lowercase		,				compute_importance=lowercase		,				head_mask=lowercase					)
    snake_case       : Dict    =       1 / loss
    snake_case       : str    =       datetime.now() - before_time

    snake_case       : Dict    =       sum(p.numel() for p in model.parameters()					)
    snake_case       : List[str]    =       {
        layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowercase					)					)
    }

    for k, v in heads_to_prune.items():
        if isinstance(lowercase		,				lowercase					):
            snake_case       : Dict    =       [
                v,
            ]

    assert sum(len(lowercase					) for h in heads_to_prune.values()					) == (1 - head_mask.long()).sum().item()
    model.prune_heads(lowercase					)
    snake_case       : Any    =       sum(p.numel() for p in model.parameters()					)

    snake_case       : Tuple    =       datetime.now()
    snake_case   ,snake_case   ,snake_case       : str    =       compute_heads_importance(
        lowercase		,				lowercase		,				lowercase		,				compute_entropy=lowercase		,				compute_importance=lowercase		,				head_mask=lowercase		,				actually_pruned=lowercase		,				)

    snake_case       : Union[str, Any]    =       1 / loss
    snake_case       : Tuple    =       datetime.now() - before_time

    logger.info(
        "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"		,				lowercase		,				lowercase		,				pruned_num_params / original_num_params * 100		,				)
    logger.info("Pruning: score with masking: %f score with pruning: %f"		,				lowercase		,				lowercase					)
    logger.info("Pruning: speed ratio (original timing / new timing): %f percents"		,				original_time / new_time * 100					)
    save_model(lowercase		,				args.output_dir					)







def     __lowerCAmelCase		(		)      ->		Optional[Any]:


    """simple docstring"""





    snake_case       : Union[str, Any]    =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--data_dir"		,				default=lowercase		,				type=lowercase		,				required=lowercase		,				help="The input data dir. Should contain the .tsv files (or other data files) for the task."		,				)
    parser.add_argument(
        "--model_name_or_path"		,				default=lowercase		,				type=lowercase		,				required=lowercase		,				help="Path to pretrained model or model identifier from huggingface.co/models"		,				)
    parser.add_argument(
        "--output_dir"		,				default=lowercase		,				type=lowercase		,				required=lowercase		,				help="The output directory where the model predictions and checkpoints will be written."		,				)

    # Other parameters
    parser.add_argument(
        "--config_name"		,				default=""		,				type=lowercase		,				help="Pretrained config name or path if not the same as model_name_or_path"		,				)
    parser.add_argument(
        "--tokenizer_name"		,				default=""		,				type=lowercase		,				help="Pretrained tokenizer name or path if not the same as model_name_or_path"		,				)
    parser.add_argument(
        "--cache_dir"		,				default=lowercase		,				type=lowercase		,				help="Where do you want to store the pre-trained models downloaded from s3"		,				)
    parser.add_argument(
        "--data_subset"		,				type=lowercase		,				default=-1		,				help="If > 0: limit the data to a subset of data_subset instances."					)
    parser.add_argument(
        "--overwrite_output_dir"		,				action="store_true"		,				help="Whether to overwrite data in output directory"					)
    parser.add_argument(
        "--overwrite_cache"		,				action="store_true"		,				help="Overwrite the cached training and evaluation sets"					)

    parser.add_argument(
        "--dont_normalize_importance_by_layer"		,				action="store_true"		,				help="Don't normalize importance score by layers"					)
    parser.add_argument(
        "--dont_normalize_global_importance"		,				action="store_true"		,				help="Don't normalize all importance scores between 0 and 1"		,				)

    parser.add_argument(
        "--try_masking"		,				action="store_true"		,				help="Whether to try to mask head until a threshold of accuracy."					)
    parser.add_argument(
        "--masking_threshold"		,				default=0.9		,				type=lowercase		,				help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."		,				)
    parser.add_argument(
        "--masking_amount"		,				default=0.1		,				type=lowercase		,				help="Amount to heads to masking at each masking step."					)
    parser.add_argument("--metric_name"		,				default="acc"		,				type=lowercase		,				help="Metric to use for head masking."					)

    parser.add_argument(
        "--max_seq_length"		,				default=128		,				type=lowercase		,				help=(
            "The maximum total input sequence length after WordPiece tokenization. \n"
            "Sequences longer than this will be truncated, sequences shorter padded."
        )		,				)
    parser.add_argument("--batch_size"		,				default=1		,				type=lowercase		,				help="Batch size."					)

    parser.add_argument("--seed"		,				type=lowercase		,				default=42					)
    parser.add_argument("--local_rank"		,				type=lowercase		,				default=-1		,				help="local_rank for distributed training on gpus"					)
    parser.add_argument("--no_cuda"		,				action="store_true"		,				help="Whether not to use CUDA when available"					)
    parser.add_argument("--server_ip"		,				type=lowercase		,				default=""		,				help="Can be used for distant debugging."					)
    parser.add_argument("--server_port"		,				type=lowercase		,				default=""		,				help="Can be used for distant debugging."					)
    snake_case       : List[str]    =       parser.parse_args()

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("Waiting for debugger attach"					)
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,				redirect_output=lowercase					)
        ptvsd.wait_for_attach()

    # Setup devices and distributed training
    if args.local_rank == -1 or args.no_cuda:
        snake_case       : Optional[int]    =       torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"					)
        snake_case       : Dict    =       0 if args.no_cuda else torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank					)
        snake_case       : Optional[Any]    =       torch.device("cuda"		,				args.local_rank					)
        snake_case       : Optional[int]    =       1
        torch.distributed.init_process_group(backend="nccl"					)  # Initializes the distributed backend

    # Setup logging
    logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN					)
    logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device		,				args.n_gpu		,				bool(args.local_rank != -1					)					)					)

    snake_case       : Any    =       GPTaLMHeadModel.from_pretrained(args.model_name_or_path					)

    # Distributed and parallel training
    model.to(args.device					)
    if args.local_rank != -1:
        snake_case       : Optional[Any]    =       nn.parallel.DistributedDataParallel(
            lowercase		,				device_ids=[args.local_rank]		,				output_device=args.local_rank		,				find_unused_parameters=lowercase					)
    elif args.n_gpu > 1:
        snake_case       : List[str]    =       nn.DataParallel(lowercase					)

    # Print/save training arguments
    os.makedirs(args.output_dir		,				exist_ok=lowercase					)
    torch.save(lowercase		,				os.path.join(args.output_dir		,				"run_args.bin"					)					)
    logger.info("Training/evaluation parameters %s"		,				lowercase					)

    # Prepare dataset
    snake_case       : str    =       np.concatenate(
        [
            np.loadtxt(args.data_dir		,				dtype=np.intaa					),
        ]					)
    snake_case       : Union[str, Any]    =       (torch.from_numpy(lowercase					),)
    snake_case       : Tuple    =       TensorDataset(*lowercase					)
    snake_case       : List[Any]    =       RandomSampler(lowercase					)
    snake_case       : int    =       DataLoader(lowercase		,				sampler=lowercase		,				batch_size=args.batch_size					)

    # Compute head entropy and importance score
    compute_heads_importance(lowercase		,				lowercase		,				lowercase					)

    # Try head masking (set heads to zero until the score goes under a threshole)
    # and head pruning (remove masked heads and see the effect on the network)
    if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
        snake_case       : Tuple    =       mask_heads(lowercase		,				lowercase		,				lowercase					)
        prune_heads(lowercase		,				lowercase		,				lowercase		,				lowercase					)


if __name__ == "__main__":
  main()









"""simple docstring"""



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__snake_case       =       logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__snake_case       =       {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__snake_case       =       {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__snake_case       =       sorted(arg_to_scheduler.keys())
__snake_case       =       """{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class _lowerCAmelCase							( pl.LightningModule      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__="base"		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      **UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(UpperCamelCase__		)
       snake_case       : Tuple    =       0
       snake_case       : List[str]    =       Path(self.hparams.output_dir		)
       snake_case       : Any    =       self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
           snake_case       : int    =       AutoConfig.from_pretrained(
               self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,      **({"num_labels": num_labels} if num_labels is not None else {})		,      cache_dir=UpperCamelCase__		,      **UpperCamelCase__		,      )
       else:
           snake_case       : PretrainedConfig    =       config

       snake_case       : Any    =       ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
       for p in extra_model_params:
           if getattr(self.hparams		,      UpperCamelCase__		,      UpperCamelCase__		):
               assert hasattr(self.config		,      UpperCamelCase__		), F'model config doesn\'t have a `{p}` attribute'
               setattr(self.config		,      UpperCamelCase__		,      getattr(self.hparams		,      UpperCamelCase__		)		)

       if tokenizer is None:
           snake_case       : Dict    =       AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,      cache_dir=UpperCamelCase__		,      )
       else:
           snake_case       : PreTrainedTokenizer    =       tokenizer
       snake_case       : List[str]    =       MODEL_MODES[mode]
       if model is None:
           snake_case       : Union[str, Any]    =       self.model_type.from_pretrained(
               self.hparams.model_name_or_path		,      from_tf=bool(".ckpt" in self.hparams.model_name_or_path		)		,      config=self.config		,      cache_dir=UpperCamelCase__		,      )
       else:
           snake_case       : List[Any]    =       model







   def 					lowerCamelCase						(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[Any]    =       self.model_type.from_pretrained(*UpperCamelCase__		,      **UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Dict    =       arg_to_scheduler[self.hparams.lr_scheduler]
       snake_case       : Any    =       get_schedule_func(
           self.opt		,      num_warmup_steps=self.hparams.warmup_steps		,      num_training_steps=self.total_steps()		)
       snake_case       : List[str]    =       {"scheduler": scheduler, "interval": "step", "frequency": 1}
       return scheduler







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

       '''simple docstring'''




       snake_case       : Dict    =       self.model
       snake_case       : Tuple    =       ["bias", "LayerNorm.weight"]
       snake_case       : Any    =       [
           {
               "params": [
                   p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)
               ],  # check this named paramters
               "weight_decay": self.hparams.weight_decay,
           },
           {
               "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)],
               "weight_decay": 0.0,
           },
       ]
       if self.hparams.adafactor:
           snake_case       : Optional[int]    =       Adafactor(
               UpperCamelCase__		,      lr=self.hparams.learning_rate		,      scale_parameter=UpperCamelCase__		,      relative_step=UpperCamelCase__		)

       else:
           snake_case       : Optional[Any]    =       AdamW(
               UpperCamelCase__		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon		)
       snake_case       : Any    =       optimizer

       snake_case       : List[Any]    =       self.get_lr_scheduler()

       return [optimizer], [scheduler]







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

       '''simple docstring'''




       return self.validation_step(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.validation_end(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : List[str]    =       max(1		,      self.hparams.gpus		)  # TODO: consider num_tpu_cores
       snake_case       : List[Any]    =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







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

       '''simple docstring'''




       if stage == "test":
           snake_case       : Dict    =       len(self.test_dataloader().dataset		)
       else:
           snake_case       : str    =       self.get_dataloader("train"		,      self.hparams.train_batch_size		,      shuffle=UpperCamelCase__		)
           snake_case       : Dict    =       len(self.train_dataloader().dataset		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = False		)      ->						int:

       '''simple docstring'''




       raise NotImplementedError("You must implement this for your task"		)







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

       '''simple docstring'''




       return self.train_loader







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       return self.get_dataloader("dev"		,      self.hparams.eval_batch_size		,      shuffle=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.get_dataloader("test"		,      self.hparams.eval_batch_size		,      shuffle=UpperCamelCase__		)







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

       '''simple docstring'''




       return os.path.join(
           self.hparams.data_dir		,      "cached_{}_{}_{}".format(
               UpperCamelCase__		,      list(filter(UpperCamelCase__		,      self.hparams.model_name_or_path.split("/"		)		)		).pop()		,      str(self.hparams.max_seq_length		)		,      )		,      )







   @pl.utilities.rank_zero_only
   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       snake_case       : str    =       self.output_dir.joinpath("best_tfmr"		)
       snake_case       : int    =       self.step_count
       self.model.save_pretrained(UpperCamelCase__		)
       self.tokenizer.save_pretrained(UpperCamelCase__		)







   @staticmethod
   def 					lowerCamelCase						(      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       parser.add_argument(
           "--model_name_or_path"		,      default=UpperCamelCase__		,      type=UpperCamelCase__		,      required=UpperCamelCase__		,      help="Path to pretrained model or model identifier from huggingface.co/models"		,      )
       parser.add_argument(
           "--config_name"		,      default=""		,      type=UpperCamelCase__		,      help="Pretrained config name or path if not the same as model_name"		)
       parser.add_argument(
           "--tokenizer_name"		,      default=UpperCamelCase__		,      type=UpperCamelCase__		,      help="Pretrained tokenizer name or path if not the same as model_name"		,      )
       parser.add_argument(
           "--cache_dir"		,      default=str(Path(UpperCamelCase__		).parent / "test_run" / "cache"		)		,      type=UpperCamelCase__		,      help="Where do you want to store the pre-trained models downloaded from huggingface.co"		,      )
       parser.add_argument(
           "--encoder_layerdrop"		,      type=UpperCamelCase__		,      help="Encoder layer dropout probability (Optional). Goes into model.config"		,      )
       parser.add_argument(
           "--decoder_layerdrop"		,      type=UpperCamelCase__		,      help="Decoder layer dropout probability (Optional). Goes into model.config"		,      )
       parser.add_argument(
           "--dropout"		,      type=UpperCamelCase__		,      help="Dropout probability (Optional). Goes into model.config"		,      )
       parser.add_argument(
           "--attention_dropout"		,      type=UpperCamelCase__		,      help="Attention dropout probability (Optional). Goes into model.config"		,      )
       parser.add_argument("--learning_rate"		,      default=5e-5		,      type=UpperCamelCase__		,      help="The initial learning rate for Adam."		)
       parser.add_argument(
           "--lr_scheduler"		,      default="linear"		,      choices=UpperCamelCase__		,      metavar=UpperCamelCase__		,      type=UpperCamelCase__		,      help="Learning rate scheduler"		,      )
       parser.add_argument("--weight_decay"		,      default=0.0		,      type=UpperCamelCase__		,      help="Weight decay if we apply some."		)
       parser.add_argument("--adam_epsilon"		,      default=1e-8		,      type=UpperCamelCase__		,      help="Epsilon for Adam optimizer."		)
       parser.add_argument("--warmup_steps"		,      default=0		,      type=UpperCamelCase__		,      help="Linear warmup over warmup_steps."		)
       parser.add_argument("--num_workers"		,      default=4		,      type=UpperCamelCase__		,      help="kwarg passed to DataLoader"		)
       parser.add_argument("--num_train_epochs"		,      dest="max_epochs"		,      default=3		,      type=UpperCamelCase__		)
       parser.add_argument("--train_batch_size"		,      default=32		,      type=UpperCamelCase__		)
       parser.add_argument("--eval_batch_size"		,      default=32		,      type=UpperCamelCase__		)
       parser.add_argument("--adafactor"		,      action="store_true"		)







class _lowerCAmelCase							( pl.Callback      ):







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

       '''simple docstring'''




       if (
           trainer.is_global_zero and trainer.global_rank == 0
       ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class _lowerCAmelCase							( pl.Callback      ):







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

       '''simple docstring'''




       for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
               print(UpperCamelCase__		)







class _lowerCAmelCase							( pl.Callback      ):







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

       '''simple docstring'''




       snake_case       : List[str]    =       trainer.lr_schedulers[0]["scheduler"]
       snake_case       : str    =       {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
       pl_module.logger.log_metrics(UpperCamelCase__		)







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

       '''simple docstring'''




       rank_zero_info("***** Validation results *****"		)
       snake_case       : List[str]    =       trainer.callback_metrics
       # Log results
       for key in sorted(UpperCamelCase__		):
           if key not in ["log", "progress_bar"]:
               rank_zero_info("{} = {}\n".format(UpperCamelCase__		,      str(metrics[key]		)		)		)







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

       '''simple docstring'''




       rank_zero_info("***** Test results *****"		)
       snake_case       : Dict    =       trainer.callback_metrics
       # Log and save results to file
       snake_case       : Union[str, Any]    =       os.path.join(pl_module.hparams.output_dir		,      "test_results.txt"		)
       with open(UpperCamelCase__		,      "w"		) as writer:
           for key in sorted(UpperCamelCase__		):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info("{} = {}\n".format(UpperCamelCase__		,      str(metrics[key]		)		)		)
                   writer.write("{} = {}\n".format(UpperCamelCase__		,      str(metrics[key]		)		)		)







def     __lowerCAmelCase		(		lowercase    :    Any		,				lowercase    :    str					)      ->		None:


    """simple docstring"""





    parser.add_argument(
        "--output_dir"		,				default=str(Path(lowercase					).parent / "test_run" / "model_checkpoints"					)		,				type=lowercase		,				help="The output directory where the model predictions and checkpoints will be written."		,				)
    parser.add_argument(
        "--fp16"		,				action="store_true"		,				help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"		,				)

    parser.add_argument(
        "--fp16_opt_level"		,				type=lowercase		,				default="O2"		,				help=(
            "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
            "See details at https://nvidia.github.io/apex/amp.html"
        )		,				)
    parser.add_argument("--n_tpu_cores"		,				dest="tpu_cores"		,				type=lowercase					)
    parser.add_argument("--max_grad_norm"		,				dest="gradient_clip_val"		,				default=1.0		,				type=lowercase		,				help="Max gradient norm"					)
    parser.add_argument("--do_train"		,				action="store_true"		,				help="Whether to run training."					)
    parser.add_argument("--do_predict"		,				action="store_true"		,				help="Whether to run predictions on the test set."					)
    parser.add_argument(
        "--gradient_accumulation_steps"		,				dest="accumulate_grad_batches"		,				type=lowercase		,				default=1		,				help="Number of updates steps to accumulate before performing a backward/update pass."		,				)
    parser.add_argument("--seed"		,				type=lowercase		,				default=42		,				help="random seed for initialization"					)
    parser.add_argument(
        "--data_dir"		,				default=str(Path(lowercase					).parent / "test_run" / "dummy-train-data"					)		,				type=lowercase		,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,				)







def     __lowerCAmelCase		(		lowercase    :    BaseTransformer		,				lowercase    :    argparse.Namespace		,				lowercase    :    Any=None		,				lowercase    :    List[str]=True		,				lowercase    :    List[Any]=[]		,				lowercase    :    Any=None		,				lowercase    :    Optional[int]=None		,				**lowercase    :    List[Any]		,				)      ->		Tuple:


    """simple docstring"""





    pl.seed_everything(args.seed					)

    # init model
    snake_case       : Optional[int]    =       Path(model.hparams.output_dir					)
    odir.mkdir(exist_ok=lowercase					)

    # add custom checkpoints
    if checkpoint_callback is None:
        snake_case       : int    =       pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir		,				prefix="checkpoint"		,				monitor="val_loss"		,				mode="min"		,				save_top_k=1					)
    if early_stopping_callback:
        extra_callbacks.append(lowercase					)
    if logging_callback is None:
        snake_case       : Tuple    =       LoggingCallback()

    snake_case       : str    =       {}

    if args.fpaa:
        snake_case       : Union[str, Any]    =       16

    if args.gpus > 1:
        snake_case       : List[str]    =       "auto"
        snake_case       : int    =       "ddp"

    snake_case       : Dict    =       args.accumulate_grad_batches
    snake_case       : Tuple    =       None
    snake_case       : Any    =       "auto"

    snake_case       : int    =       pl.Trainer.from_argparse_args(
        lowercase		,				weights_summary=lowercase		,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,				logger=lowercase		,				val_check_interval=1		,				num_sanity_val_steps=2		,				**lowercase		,				)

    if args.do_train:
        trainer.fit(lowercase					)

    else:
        print("RAG modeling tests with new set functions successfuly executed!"					)
    return trainer





