

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
       from fairscale.optim import OSS


snake_case_			  = logging.get_logger(__name__)

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,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   a=None						,   a=None						,   *a						,   **a):
         super().__init__(*a						,   **a)

         if config is None:
              assert isinstance(self.model						,   a), (
                  "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                  f""" {self.model.__class__}"""
              )
              lowercase__    :   int      =       self.model.config
         else:
              lowercase__    :   Union[str, Any]      =       config

         lowercase__    :   Optional[int]      =       data_args
         lowercase__    :   Tuple      =       self.config.tgt_vocab_size if isinstance(self.config						,   a) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
              assert self.config.pad_token_id is not None, (
                  "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                  " calculation or doing label smoothing."
              )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
              logger.warning(
                  f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
                  ' padding..')

         if self.args.label_smoothing == 0:
              lowercase__    :   Any      =       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
         else:
              # dynamically import label_smoothed_nll_loss
              from utils import label_smoothed_nll_loss

              lowercase__    :   str      =       label_smoothed_nll_loss





    def       snake_case_				(      self						,   a):
         if self.optimizer is None:
              lowercase__    :   str      =       ['bias', 'LayerNorm.weight']
              lowercase__    :   int      =       [
                  {
                      'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
                      'weight_decay': self.args.weight_decay,
                  },
                  {
                      'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
                      'weight_decay': 0.0,
                  },
              ]
              lowercase__    :   Optional[int]      =       Adafactor if self.args.adafactor else AdamW
              if self.args.adafactor:
                   lowercase__    :   List[str]      =       Adafactor
                   lowercase__    :   List[Any]      =       {'scale_parameter': False, 'relative_step': False}
              else:
                   lowercase__    :   Tuple      =       AdamW
                   lowercase__    :   str      =       {
                       'betas': (self.args.adam_betaa, self.args.adam_betaa),
                       'eps': self.args.adam_epsilon,
                   }
              lowercase__    :   Dict      =       self.args.learning_rate
              if self.sharded_ddp:
                   lowercase__    :   int      =       OSS(
                       params=a						,   optim=a						,   **a						,   )
              else:
                   lowercase__    :   Optional[Any]      =       optimizer_cls(a						,   **a)

         if self.lr_scheduler is None:
              lowercase__    :   Any      =       self._get_lr_scheduler(a)
         else:  # ignoring --lr_scheduler
              logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.')





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
              lowercase__    :   Union[str, Any]      =       schedule_func(self.optimizer)
         elif self.args.lr_scheduler == "constant_w_warmup":
              lowercase__    :   Union[str, Any]      =       schedule_func(self.optimizer						,   num_warmup_steps=self.args.warmup_steps)
         else:
              lowercase__    :   Dict      =       schedule_func(
                  self.optimizer						,   num_warmup_steps=self.args.warmup_steps						,   num_training_steps=a)
         return scheduler





    def       snake_case_				(      self):
         if isinstance(self.train_dataset						,   torch.utils.data.IterableDataset):
              return None
         elif is_torch_tpu_available():
              return get_tpu_sampler(self.train_dataset)
         else:
              if self.args.sortish_sampler:
                   self.train_dataset.make_sortish_sampler(
                       self.args.per_device_train_batch_size						,   distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)						,   )

              return (
                  RandomSampler(self.train_dataset)
                  if self.args.local_rank == -1
                  else DistributedSampler(self.train_dataset)
              )





    def       snake_case_				(      self						,   a						,   a						,   a):
         if self.args.label_smoothing == 0:
              if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                   # force training to ignore pad token
                   lowercase__    :   Optional[Any]      =       model(**a						,   use_cache=a)[0]
                   lowercase__    :   str      =       self.loss_fn(logits.view(-1						,   logits.shape[-1])						,   labels.view(-1))
              else:
                   # compute usual loss via models
                   lowercase__      , lowercase__    :   Optional[int]      =       model(**a						,   labels=a						,   use_cache=a)[:2]
         else:
              # compute label smoothed loss
              lowercase__    :   Dict      =       model(**a						,   use_cache=a)[0]
              lowercase__    :   Union[str, Any]      =       torch.nn.functional.log_softmax(a						,   dim=-1)
              lowercase__      , lowercase__    :   Any      =       self.loss_fn(a						,   a						,   self.args.label_smoothing						,   ignore_index=self.config.pad_token_id)
         return loss, logits





    def       snake_case_				(      self						,   a						,   a):
         lowercase__    :   Any      =       inputs.pop('labels')
         lowercase__      , lowercase__    :   Optional[Any]      =       self._compute_loss(a						,   a						,   a)
         return loss





    def       snake_case_				(      self						,   a						,   a						,   a						,   a = None						,   ):
         lowercase__    :   List[Any]      =       self._prepare_inputs(a)

         lowercase__    :   str      =       {
             'max_length': self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
              lowercase__    :   List[Any]      =       self.model.generate(
                  inputs['input_ids']						,   attention_mask=inputs['attention_mask']						,   **a						,   )
              # in case the batch is shorter than max length, the output should be padded
              if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   lowercase__    :   Tuple      =       self._pad_tensors_to_max_len(a						,   gen_kwargs['max_length'])

         lowercase__    :   Dict      =       inputs.pop('labels')
         with torch.no_grad():
              # compute loss on predict data
              lowercase__      , lowercase__    :   str      =       self._compute_loss(a						,   a						,   a)

         lowercase__    :   Dict      =       loss.mean().detach()
         if self.args.prediction_loss_only:
              return (loss, None, None)

         lowercase__    :   Optional[int]      =       generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
              lowercase__    :   List[Any]      =       self._pad_tensors_to_max_len(a						,   gen_kwargs['max_length'])

         return (loss, logits, labels)





    def       snake_case_				(      self						,   a						,   a):
         # If PAD token is not defined at least EOS token has to be defined
         lowercase__    :   str      =       self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
              raise ValueError(
                  'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
                  f""" padded to `max_length`={max_length}""")

         lowercase__    :   Any      =       pad_token_id * torch.ones(
             (tensor.shape[0], max_length)						,   dtype=tensor.dtype						,   device=tensor.device)
         lowercase__    :   List[Any]      =       tensor
         return padded_tensor




import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


snake_case_			  = logging.get_logger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       set()
     lowercase__    :   List[str]      =       []

     def parse_line(SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):
          for line in fp:
               if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[int]      =       line.decode('UTF-8'			)
               if "warnings summary (final)" in line:
                    continue
               # This means we are outside the body of a warning
               elif not line.startswith(' '			):
                    # process a single warning and move it to `selected_warnings`.
                    if len(SCREAMING_SNAKE_CASE_			) > 0:
                         lowercase__    :   Optional[Any]      =       '\n'.join(SCREAMING_SNAKE_CASE_			)
                         # Only keep the warnings specified in `targets`
                         if any(f""": {x}: """ in warning for x in targets			):
                              selected_warnings.add(SCREAMING_SNAKE_CASE_			)
                         buffer.clear()
                    continue
               else:
                    lowercase__    :   Optional[Any]      =       line.strip()
                    buffer.append(SCREAMING_SNAKE_CASE_			)

     if from_gh:
          for filename in os.listdir(SCREAMING_SNAKE_CASE_			):
               lowercase__    :   int      =       os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
               if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(SCREAMING_SNAKE_CASE_			) as fp:
                         parse_line(SCREAMING_SNAKE_CASE_			)
     else:
          try:
               with zipfile.ZipFile(SCREAMING_SNAKE_CASE_			) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(SCREAMING_SNAKE_CASE_			) as fp:
                                   parse_line(SCREAMING_SNAKE_CASE_			)
          except Exception:
               logger.warning(
                   f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""			)

     return selected_warnings

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       set()

     lowercase__    :   List[str]      =       [os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			) for p in os.listdir(SCREAMING_SNAKE_CASE_			) if (p.endswith('.zip'			) or from_gh)]
     for p in paths:
          selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)			)

     return selected_warnings


if __name__ == "__main__":

       def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



            '''simple docstring'''





            return values.split(','			)

       snake_case_			  = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
       parser.add_argument(
           '''--output_dir''',
           type=str,
           required=True,
           help='''Where to store the downloaded artifacts and other result files.''',
       )
       parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
       # optional parameters
       parser.add_argument(
           '''--targets''',
           default='''DeprecationWarning,UserWarning,FutureWarning''',
           type=list_str,
           help='''Comma-separated list of target warning(s) which we want to extract.''',
       )
       parser.add_argument(
           '''--from_gh''',
           action='''store_true''',
           help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
       )

       snake_case_			  = parser.parse_args()

       snake_case_			  = args.from_gh
       if from_gh:
              # The artifacts have to be downloaded using `actions/download-artifact@v3`
              pass
       else:
              os.makedirs(args.output_dir, exist_ok=True)

              # get download links
              snake_case_			  = get_artifacts_links(args.workflow_run_id, token=args.token)
              with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
                     json.dump(artifacts, fp, ensure_ascii=False, indent=4)

              # download artifacts
              for idx, (name, url) in enumerate(artifacts.items()):
                     print(name)
                     print(url)
                     print('''=''' * 80)
                     download_artifact(name, url, args.output_dir, args.token)
                     # Be gentle to GitHub
                     time.sleep(1)

    # extract warnings from artifacts
       snake_case_			  = extract_warnings(args.output_dir, args.targets)
       snake_case_			  = sorted(selected_warnings)
       with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
              json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

