






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


snake_case__   :						str													=   logging.getLogger(__name__)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """summarization"""
  A_						       = ["""loss"""]
  A_						       = ROUGE_KEYS
  A_						       = """rouge2"""




  def __init__(  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Union[str, Any]:
     if hparams.sortish_sampler and hparams.gpus > 1:
        UpperCamelCase_						=  False
     elif hparams.max_tokens_per_batch is not None:
        if hparams.gpus > 1:
           raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'			)
        if hparams.sortish_sampler:
           raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'			)

     super().__init__(_UpperCAmelCase		,		num_labels=_UpperCAmelCase		,		mode=self.mode		,		**_UpperCAmelCase			)
     use_task_specific_params(self.model		,		'summarization'			)
     save_git_info(self.hparams.output_dir			)
     UpperCamelCase_						=  Path(self.output_dir			) / 'metrics.json'
     UpperCamelCase_						=  Path(self.output_dir			) / 'hparams.pkl'
     pickle_save(self.hparams		,		self.hparams_save_path			)
     UpperCamelCase_						=  0
     UpperCamelCase_						=  defaultdict(_UpperCAmelCase			)
     UpperCamelCase_						=  self.config.model_type
     UpperCamelCase_						=  self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

     UpperCamelCase_						=  {
         "data_dir": self.hparams.data_dir,
         "max_source_length": self.hparams.max_source_length,
         "prefix": self.model.config.prefix or "",
     }
     UpperCamelCase_						=  {
         'train': self.hparams.n_train,
         'val': self.hparams.n_val,
         'test': self.hparams.n_test,
     }
     UpperCamelCase_						=  {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

     UpperCamelCase_						=  {
         'train': self.hparams.max_target_length,
         'val': self.hparams.val_max_target_length,
         'test': self.hparams.test_max_target_length,
     }
     assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
     assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
     if self.hparams.freeze_embeds:
        freeze_embeds(self.model			)
     if self.hparams.freeze_encoder:
        freeze_params(self.model.get_encoder()			)
        assert_all_frozen(self.model.get_encoder()			)

     UpperCamelCase_						=  get_git_info()['repo_sha']
     UpperCamelCase_						=  hparams.num_workers
     UpperCamelCase_						=  None  # default to config
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,		_UpperCAmelCase			):
        UpperCamelCase_						=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]
        UpperCamelCase_						=  self.decoder_start_token_id
     UpperCamelCase_						=  (
         SeqaSeqDataset if hasattr(self.tokenizer		,		'prepare_seq2seq_batch'			) else LegacySeqaSeqDataset
     )
     UpperCamelCase_						=  False
     UpperCamelCase_						=  self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
     if self.hparams.eval_max_gen_length is not None:
        UpperCamelCase_						=  self.hparams.eval_max_gen_length
     else:
        UpperCamelCase_						=  self.model.config.max_length
     UpperCamelCase_						=  self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict[str, List[str]]:
     UpperCamelCase_						=  {
         k: self.tokenizer.batch_decode(v.tolist()			) if 'mask' not in k else v.shape for k, v in batch.items()
     }
     save_json(_UpperCAmelCase		,		Path(self.output_dir			) / 'text_batch.json'			)
     save_json({k: v.tolist() for k, v in batch.items()}		,		Path(self.output_dir			) / 'tok_batch.json'			)

     UpperCamelCase_						=  True
     return readable_batch




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Union[str, Any]:
     return self.model(_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:
     UpperCamelCase_						=  self.tokenizer.batch_decode(
         _UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase		,		clean_up_tokenization_spaces=_UpperCAmelCase			)
     return lmap(str.strip		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  self.tokenizer.pad_token_id
     UpperCamelCase_		,   UpperCamelCase_						=  batch['input_ids'], batch['attention_mask']
     UpperCamelCase_						=  batch['labels']
     if isinstance(self.model		,		_UpperCAmelCase			):
        UpperCamelCase_						=  self.model._shift_right(_UpperCAmelCase			)
     else:
        UpperCamelCase_						=  shift_tokens_right(_UpperCAmelCase		,		_UpperCAmelCase			)
     if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
        UpperCamelCase_						=  decoder_input_ids
        self.save_readable_batch(_UpperCAmelCase			)

     UpperCamelCase_						=  self(_UpperCAmelCase		,		attention_mask=_UpperCAmelCase		,		decoder_input_ids=_UpperCAmelCase		,		use_cache=_UpperCAmelCase			)
     UpperCamelCase_						=  outputs['logits']
     if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        UpperCamelCase_						=  nn.CrossEntropyLoss(ignore_index=_UpperCAmelCase			)

        assert lm_logits.shape[-1] == self.vocab_size
        UpperCamelCase_						=  ce_loss_fct(lm_logits.view(-1		,		lm_logits.shape[-1]			)		,		tgt_ids.view(-1			)			)
     else:
        UpperCamelCase_						=  nn.functional.log_softmax(_UpperCAmelCase		,		dim=-1			)
        UpperCamelCase_		,   UpperCamelCase_						=  label_smoothed_nll_loss(
            _UpperCAmelCase		,		_UpperCAmelCase		,		self.hparams.label_smoothing		,		ignore_index=_UpperCAmelCase			)
     return (loss,)




  @property
  def  _UpperCAmelCase  (  self			)      ->			int:
     return self.tokenizer.pad_token_id




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  self._step(_UpperCAmelCase			)

     UpperCamelCase_						=  dict(zip(self.loss_names		,		_UpperCAmelCase			)			)
     # tokens per batch
     UpperCamelCase_						=  batch['input_ids'].ne(self.pad			).sum() + batch['labels'].ne(self.pad			).sum()
     UpperCamelCase_						=  batch['input_ids'].shape[0]
     UpperCamelCase_						=  batch['input_ids'].eq(self.pad			).sum()
     UpperCamelCase_						=  batch['input_ids'].eq(self.pad			).float().mean()
     # TODO(SS): make a wandb summary metric for this
     return {"loss": loss_tensors[0], "log": logs}




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     return self._generative_step(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase="val"			)      ->			Dict:
     self.step_count += 1
     UpperCamelCase_						=  {k: torch.stack([x[k] for x in outputs]			).mean() for k in self.loss_names}
     UpperCamelCase_						=  losses['loss']
     UpperCamelCase_						=  {
         k: np.array([x[k] for x in outputs]			).mean() for k in self.metric_names + ['gen_time', 'gen_len']
     }
     UpperCamelCase_						=  (
         generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
     )
     UpperCamelCase_						=  torch.tensor(_UpperCAmelCase			).type_as(_UpperCAmelCase			)
     generative_metrics.update({k: v.item() for k, v in losses.items()}			)
     losses.update(_UpperCAmelCase			)
     UpperCamelCase_						=  {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
     UpperCamelCase_						=  self.step_count
     self.metrics[prefix].append(_UpperCAmelCase			)  # callback writes this to self.metrics_save_path
     UpperCamelCase_						=  flatten_list([x['preds'] for x in outputs]			)
     return {
         "log": all_metrics,
         "preds": preds,
         f"""{prefix}_loss""": loss,
         f"""{prefix}_{self.val_metric}""": metric_tensor,
     }




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     return calculate_rouge(_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			dict:
     UpperCamelCase_						=  time.time()

     # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
     UpperCamelCase_						=  self.model.generate(
         batch['input_ids']		,		attention_mask=batch['attention_mask']		,		use_cache=_UpperCAmelCase		,		decoder_start_token_id=self.decoder_start_token_id		,		num_beams=self.eval_beams		,		max_length=self.eval_max_length		,		)
     UpperCamelCase_						=  (time.time() - ta) / batch['input_ids'].shape[0]
     UpperCamelCase_						=  self.ids_to_clean_text(_UpperCAmelCase			)
     UpperCamelCase_						=  self.ids_to_clean_text(batch['labels']			)
     UpperCamelCase_						=  self._step(_UpperCAmelCase			)
     UpperCamelCase_						=  dict(zip(self.loss_names		,		_UpperCAmelCase			)			)
     UpperCamelCase_						=  self.calc_generative_metrics(_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  np.mean(lmap(_UpperCAmelCase		,		_UpperCAmelCase			)			)
     base_metrics.update(gen_time=_UpperCAmelCase		,		gen_len=_UpperCAmelCase		,		preds=_UpperCAmelCase		,		target=_UpperCAmelCase		,		**_UpperCAmelCase			)
     return base_metrics




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			str:
     return self._generative_step(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:
     return self.validation_epoch_end(_UpperCAmelCase		,		prefix='test'			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			SeqaSeqDataset:
     UpperCamelCase_						=  self.n_obs[type_path]
     UpperCamelCase_						=  self.target_lens[type_path]
     UpperCamelCase_						=  self.dataset_class(
         self.tokenizer		,		type_path=_UpperCAmelCase		,		n_obs=_UpperCAmelCase		,		max_target_length=_UpperCAmelCase		,		**self.dataset_kwargs		,		)
     return dataset




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = False			)      ->			DataLoader:
     UpperCamelCase_						=  self.get_dataset(_UpperCAmelCase			)

     if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        UpperCamelCase_						=  dataset.make_sortish_sampler(_UpperCAmelCase		,		distributed=self.hparams.gpus > 1			)
        return DataLoader(
            _UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		collate_fn=dataset.collate_fn		,		shuffle=_UpperCAmelCase		,		num_workers=self.num_workers		,		sampler=_UpperCAmelCase		,		)

     elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
        UpperCamelCase_						=  dataset.make_dynamic_sampler(
            self.hparams.max_tokens_per_batch		,		distributed=self.hparams.gpus > 1			)
        return DataLoader(
            _UpperCAmelCase		,		batch_sampler=_UpperCAmelCase		,		collate_fn=dataset.collate_fn		,		num_workers=self.num_workers		,		)
     else:
        return DataLoader(
            _UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		collate_fn=dataset.collate_fn		,		shuffle=_UpperCAmelCase		,		num_workers=self.num_workers		,		sampler=_UpperCAmelCase		,		)




  def  _UpperCAmelCase  (  self			)      ->			DataLoader:
     UpperCamelCase_						=  self.get_dataloader('train'		,		batch_size=self.hparams.train_batch_size		,		shuffle=_UpperCAmelCase			)
     return dataloader




  def  _UpperCAmelCase  (  self			)      ->			DataLoader:
     return self.get_dataloader('val'		,		batch_size=self.hparams.eval_batch_size			)




  def  _UpperCAmelCase  (  self			)      ->			DataLoader:
     return self.get_dataloader('test'		,		batch_size=self.hparams.eval_batch_size			)




  @staticmethod
  def  _UpperCAmelCase  (  _UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     BaseTransformer.add_model_specific_args(_UpperCAmelCase		,		_UpperCAmelCase			)
     add_generic_args(_UpperCAmelCase		,		_UpperCAmelCase			)
     parser.add_argument(
         '--max_source_length'		,		default=1024		,		type=_UpperCAmelCase		,		help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )		,		)
     parser.add_argument(
         '--max_target_length'		,		default=56		,		type=_UpperCAmelCase		,		help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )		,		)
     parser.add_argument(
         '--val_max_target_length'		,		default=142		,		type=_UpperCAmelCase		,		help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )		,		)
     parser.add_argument(
         '--test_max_target_length'		,		default=142		,		type=_UpperCAmelCase		,		help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )		,		)
     parser.add_argument('--freeze_encoder'		,		action='store_true'			)
     parser.add_argument('--freeze_embeds'		,		action='store_true'			)
     parser.add_argument('--sortish_sampler'		,		action='store_true'		,		default=_UpperCAmelCase			)
     parser.add_argument('--overwrite_output_dir'		,		action='store_true'		,		default=_UpperCAmelCase			)
     parser.add_argument('--max_tokens_per_batch'		,		type=_UpperCAmelCase		,		default=_UpperCAmelCase			)
     parser.add_argument('--logger_name'		,		type=_UpperCAmelCase		,		choices=['default', 'wandb', 'wandb_shared']		,		default='default'			)
     parser.add_argument('--n_train'		,		type=_UpperCAmelCase		,		default=-1		,		required=_UpperCAmelCase		,		help='# examples. -1 means use all.'			)
     parser.add_argument('--n_val'		,		type=_UpperCAmelCase		,		default=500		,		required=_UpperCAmelCase		,		help='# examples. -1 means use all.'			)
     parser.add_argument('--n_test'		,		type=_UpperCAmelCase		,		default=-1		,		required=_UpperCAmelCase		,		help='# examples. -1 means use all.'			)
     parser.add_argument(
         '--task'		,		type=_UpperCAmelCase		,		default='summarization'		,		required=_UpperCAmelCase		,		help='# examples. -1 means use all.'			)
     parser.add_argument('--label_smoothing'		,		type=_UpperCAmelCase		,		default=0.0		,		required=_UpperCAmelCase			)
     parser.add_argument('--src_lang'		,		type=_UpperCAmelCase		,		default=''		,		required=_UpperCAmelCase			)
     parser.add_argument('--tgt_lang'		,		type=_UpperCAmelCase		,		default=''		,		required=_UpperCAmelCase			)
     parser.add_argument('--eval_beams'		,		type=_UpperCAmelCase		,		default=_UpperCAmelCase		,		required=_UpperCAmelCase			)
     parser.add_argument(
         '--val_metric'		,		type=_UpperCAmelCase		,		default=_UpperCAmelCase		,		required=_UpperCAmelCase		,		choices=['bleu', 'rouge2', 'loss', None]			)
     parser.add_argument('--eval_max_gen_length'		,		type=_UpperCAmelCase		,		default=_UpperCAmelCase		,		help='never generate more than n tokens'			)
     parser.add_argument('--save_top_k'		,		type=_UpperCAmelCase		,		default=1		,		required=_UpperCAmelCase		,		help='How many checkpoints to save'			)
     parser.add_argument(
         '--early_stopping_patience'		,		type=_UpperCAmelCase		,		default=-1		,		required=_UpperCAmelCase		,		help=(
             '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
             ' val_check_interval will effect it.'
         )		,		)
     return parser




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """translation"""
  A_						       = ["""loss"""]
  A_						       = ["""bleu"""]
  A_						       = """bleu"""




  def __init__(  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Optional[int]:
     super().__init__(_UpperCAmelCase		,		**_UpperCAmelCase			)
     UpperCamelCase_						=  hparams.src_lang
     UpperCamelCase_						=  hparams.tgt_lang




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			dict:
     return calculate_bleu(_UpperCAmelCase		,		_UpperCAmelCase			)






def      _snake_case     (__lowercase     ,   __lowercase=None):
   Path(args.output_dir).mkdir(exist_ok=__lowercase)
   check_output_dir(__lowercase     ,   expected_items=3)

   if model is None:
      if "summarization" in args.task:
         UpperCamelCase_						=  SummarizationModule(__lowercase)
      else:
         UpperCamelCase_						=  TranslationModule(__lowercase)
   UpperCamelCase_						=  Path(args.data_dir).name
   if (
       args.logger_name == "default"
       or args.fast_dev_run
       or str(args.output_dir).startswith('/tmp')
       or str(args.output_dir).startswith('/var')
   ):
      UpperCamelCase_						=  True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      UpperCamelCase_						=  os.environ.get('WANDB_PROJECT'     ,   __lowercase)
      UpperCamelCase_						=  WandbLogger(name=model.output_dir.name     ,   project=__lowercase)

   elif args.logger_name == "wandb_shared":
      from pytorch_lightning.loggers import WandbLogger

      UpperCamelCase_						=  WandbLogger(name=model.output_dir.name     ,   project=f"""hf_{dataset}""")

   if args.early_stopping_patience >= 0:
      UpperCamelCase_						=  get_early_stopping_callback(model.val_metric     ,   args.early_stopping_patience)
   else:
      UpperCamelCase_						=  False

   UpperCamelCase_						=  args.val_metric == 'loss'
   UpperCamelCase_						=  generic_train(
       __lowercase     ,   __lowercase     ,   logging_callback=SeqaSeqLoggingCallback()     ,   checkpoint_callback=get_checkpoint_callback(
           args.output_dir     ,   model.val_metric     ,   args.save_top_k     ,   __lowercase)     ,   early_stopping_callback=__lowercase     ,   logger=__lowercase     ,   )
   pickle_save(model.hparams     ,   model.output_dir / 'hparams.pkl')
   if not args.do_predict:
      return model

   UpperCamelCase_						=  ''
   UpperCamelCase_						=  sorted(glob.glob(os.path.join(args.output_dir     ,   '*.ckpt')     ,   recursive=__lowercase))
   if checkpoints:
      UpperCamelCase_						=  checkpoints[-1]
      UpperCamelCase_						=  checkpoints[-1]
   trainer.logger.log_hyperparams(model.hparams)

   # test() without a model tests using the best checkpoint automatically
   trainer.test()
   return model


if __name__ == "__main__":
 snake_case__   :						Union[str, Any]													=   argparse.ArgumentParser()
 snake_case__   :						Dict													=   pl.Trainer.add_argparse_args(parser)
 snake_case__   :						List[str]													=   SummarizationModule.add_model_specific_args(parser, os.getcwd())

 snake_case__   :						Optional[Any]													=   parser.parse_args()

 main(args)







"""simple docstring"""




import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


lowerCAmelCase__				=					logging.get_logger(__name__)

# General docstring
lowerCAmelCase__				=					'PoolFormerConfig'

# Base docstring
lowerCAmelCase__				=					'sail/poolformer_s12'
lowerCAmelCase__				=					[1, 512, 7, 7]

# Image classification docstring
lowerCAmelCase__				=					'sail/poolformer_s12'
lowerCAmelCase__				=					'tabby, tabby cat'

lowerCAmelCase__				=					[
    'sail/poolformer_s12',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]



def 							_lowerCamelCase			(			__a,				__a = 0.0,				__a = False     ):
		if drop_prob == 0.0 or not training:
				return input
		SCREAMING_SNAKE_CASE_    			=			1 - drop_prob
		SCREAMING_SNAKE_CASE_    			=			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
		SCREAMING_SNAKE_CASE_    			=			keep_prob + torch.rand(__a,				dtype=input.dtype,				device=input.device     )
		random_tensor.floor_()  # binarize
		SCREAMING_SNAKE_CASE_    			=			input.div(__a     ) * random_tensor
		return output





class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			drop_prob

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return drop_path(SCREAMING_SNAKE_CASE_							,							self.drop_prob							,							self.training     )







							def _lowercase       (self     ):

									"""simple docstring"""







									return "p={}".format(self.drop_prob     )







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			patch_size if isinstance(SCREAMING_SNAKE_CASE_							,							collections.abc.Iterable     ) else (patch_size, patch_size)
									SCREAMING_SNAKE_CASE_    			=			stride if isinstance(SCREAMING_SNAKE_CASE_							,							collections.abc.Iterable     ) else (stride, stride)
									SCREAMING_SNAKE_CASE_    			=			padding if isinstance(SCREAMING_SNAKE_CASE_							,							collections.abc.Iterable     ) else (padding, padding)

									SCREAMING_SNAKE_CASE_    			=			nn.Convad(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							kernel_size=SCREAMING_SNAKE_CASE_							,							stride=SCREAMING_SNAKE_CASE_							,							padding=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			norm_layer(SCREAMING_SNAKE_CASE_     ) if norm_layer else nn.Identity()







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.projection(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.norm(SCREAMING_SNAKE_CASE_     )
									return embeddings







class 			snake_case  (				nn.GroupNorm ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(1							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			nn.AvgPoolad(SCREAMING_SNAKE_CASE_							,							stride=1							,							padding=pool_size // 2							,							count_include_pad=SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.pool(SCREAMING_SNAKE_CASE_     ) - hidden_states







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			nn.Convad(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							1     )
									SCREAMING_SNAKE_CASE_    			=			nn.Convad(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							1     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerDropPath(SCREAMING_SNAKE_CASE_     )
									if isinstance(config.hidden_act							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			ACTaFN[config.hidden_act]
									else:
											SCREAMING_SNAKE_CASE_    			=			config.hidden_act







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.conva(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.act_fn(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.drop(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.conva(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.drop(SCREAMING_SNAKE_CASE_     )

									return hidden_states







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			PoolFormerPooling(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerOutput(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE_     )

									# Useful for training neural nets
									SCREAMING_SNAKE_CASE_    			=			PoolFormerDropPath(SCREAMING_SNAKE_CASE_     ) if drop_path > 0.0 else nn.Identity()
									SCREAMING_SNAKE_CASE_    			=			config.use_layer_scale
									if config.use_layer_scale:
											SCREAMING_SNAKE_CASE_    			=			nn.Parameter(
											    config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE_)     )							,							requires_grad=SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			nn.Parameter(
											    config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE_)     )							,							requires_grad=SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if self.use_layer_scale:
											SCREAMING_SNAKE_CASE_    			=			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE_     )     )
											SCREAMING_SNAKE_CASE_    			=			self.layer_scale_a.unsqueeze(-1     ).unsqueeze(-1     ) * pooling_output
											# First residual connection
											SCREAMING_SNAKE_CASE_    			=			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			()

											SCREAMING_SNAKE_CASE_    			=			self.output(self.after_norm(SCREAMING_SNAKE_CASE_     )     )
											SCREAMING_SNAKE_CASE_    			=			self.layer_scale_a.unsqueeze(-1     ).unsqueeze(-1     ) * layer_output
											# Second residual connection
											SCREAMING_SNAKE_CASE_    			=			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE_     )

											SCREAMING_SNAKE_CASE_    			=			(output,) + outputs
											return outputs

									else:
											SCREAMING_SNAKE_CASE_    			=			self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE_     )     )     )
											# First residual connection
											SCREAMING_SNAKE_CASE_    			=			pooling_output + hidden_states
											SCREAMING_SNAKE_CASE_    			=			()

											# Second residual connection inside the PoolFormerOutput block
											SCREAMING_SNAKE_CASE_    			=			self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE_     )     )     )
											SCREAMING_SNAKE_CASE_    			=			hidden_states + layer_output

											SCREAMING_SNAKE_CASE_    			=			(output,) + outputs
											return outputs







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			config
									# stochastic depth decay rule
									SCREAMING_SNAKE_CASE_    			=			[x.item() for x in torch.linspace(0							,							config.drop_path_rate							,							sum(config.depths     )     )]

									# patch embeddings
									SCREAMING_SNAKE_CASE_    			=			[]
									for i in range(config.num_encoder_blocks     ):
											embeddings.append(
											    PoolFormerEmbeddings(
											        patch_size=config.patch_sizes[i]							,							stride=config.strides[i]							,							padding=config.padding[i]							,							num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]							,							hidden_size=config.hidden_sizes[i]							,							)     )
									SCREAMING_SNAKE_CASE_    			=			nn.ModuleList(SCREAMING_SNAKE_CASE_     )

									# Transformer blocks
									SCREAMING_SNAKE_CASE_    			=			[]
									SCREAMING_SNAKE_CASE_    			=			0
									for i in range(config.num_encoder_blocks     ):
											# each block consists of layers
											SCREAMING_SNAKE_CASE_    			=			[]
											if i != 0:
													cur += config.depths[i - 1]
											for j in range(config.depths[i]     ):
													layers.append(
													    PoolFormerLayer(
													        SCREAMING_SNAKE_CASE_							,							num_channels=config.hidden_sizes[i]							,							pool_size=config.pool_size							,							hidden_size=config.hidden_sizes[i]							,							intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio     )							,							drop_path=dpr[cur + j]							,							)     )
											blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE_     )     )

									SCREAMING_SNAKE_CASE_    			=			nn.ModuleList(SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			() if output_hidden_states else None

									SCREAMING_SNAKE_CASE_    			=			pixel_values
									for idx, layers in enumerate(zip(self.patch_embeddings							,							self.block     )     ):
											SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			layers
											# Get patch embeddings from hidden_states
											SCREAMING_SNAKE_CASE_    			=			embedding_layer(SCREAMING_SNAKE_CASE_     )
											# Send the embeddings through the blocks
											for _, blk in enumerate(SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			blk(SCREAMING_SNAKE_CASE_     )
													SCREAMING_SNAKE_CASE_    			=			layer_outputs[0]

											if output_hidden_states:
													SCREAMING_SNAKE_CASE_    			=			all_hidden_states + (hidden_states,)

									if not return_dict:
											return tuple(v for v in [hidden_states, all_hidden_states] if v is not None     )

									return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_							,							hidden_states=SCREAMING_SNAKE_CASE_     )







class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   PoolFormerConfig
							UpperCAmelCase__      =   '''poolformer'''
							UpperCAmelCase__      =   '''pixel_values'''
							UpperCAmelCase__      =   True

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							(nn.Linear, nn.Convad)     ):
											module.weight.data.normal_(mean=0.0							,							std=self.config.initializer_range     )
											if module.bias is not None:
													module.bias.data.zero_()
									elif isinstance(SCREAMING_SNAKE_CASE_							,							nn.LayerNorm     ):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False     ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			value







lowerCAmelCase__				=					r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowerCAmelCase__				=					r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n'





@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''							,      __lowercase							,      )
class 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			config

									SCREAMING_SNAKE_CASE_    			=			PoolFormerEncoder(SCREAMING_SNAKE_CASE_     )

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

							def _lowercase       (self     ):

									"""simple docstring"""







									return self.embeddings.patch_embeddings







							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_     )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC							,							output_type=SCREAMING_SNAKE_CASE_							,							config_class=_CONFIG_FOR_DOC							,							modality='''vision'''							,							expected_output=_EXPECTED_OUTPUT_SHAPE							,							)
							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_    			=			return_dict if return_dict is not None else self.config.use_return_dict

									if pixel_values is None:
											raise ValueError('''You have to specify pixel_values'''     )

									SCREAMING_SNAKE_CASE_    			=			self.encoder(
									    SCREAMING_SNAKE_CASE_							,							output_hidden_states=SCREAMING_SNAKE_CASE_							,							return_dict=SCREAMING_SNAKE_CASE_							,							)
									SCREAMING_SNAKE_CASE_    			=			encoder_outputs[0]

									if not return_dict:
											return (sequence_output, None) + encoder_outputs[1:]

									return BaseModelOutputWithNoAttention(
									    last_hidden_state=SCREAMING_SNAKE_CASE_							,							hidden_states=encoder_outputs.hidden_states							,							)







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			nn.Linear(config.hidden_size							,							config.hidden_size     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.dense(SCREAMING_SNAKE_CASE_     )
									return output







@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''							,      __lowercase							,      )
class 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			config.num_labels
									SCREAMING_SNAKE_CASE_    			=			PoolFormerModel(SCREAMING_SNAKE_CASE_     )

									# Final norm
									SCREAMING_SNAKE_CASE_    			=			PoolFormerGroupNorm(config.hidden_sizes[-1]     )
									# Classifier head
									SCREAMING_SNAKE_CASE_    			=			(
									    nn.Linear(config.hidden_sizes[-1]							,							config.num_labels     ) if config.num_labels > 0 else nn.Identity()
									)

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







							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_     )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT							,							output_type=SCREAMING_SNAKE_CASE_							,							config_class=_CONFIG_FOR_DOC							,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,							)
							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_    			=			self.poolformer(
									    SCREAMING_SNAKE_CASE_							,							output_hidden_states=SCREAMING_SNAKE_CASE_							,							return_dict=SCREAMING_SNAKE_CASE_							,							)

									SCREAMING_SNAKE_CASE_    			=			outputs[0]

									SCREAMING_SNAKE_CASE_    			=			self.classifier(self.norm(SCREAMING_SNAKE_CASE_     ).mean([-2, -1]     )     )

									SCREAMING_SNAKE_CASE_    			=			None
									if labels is not None:
											if self.config.problem_type is None:
													if self.num_labels == 1:
															SCREAMING_SNAKE_CASE_    			=			'''regression'''
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															SCREAMING_SNAKE_CASE_    			=			'''single_label_classification'''
													else:
															SCREAMING_SNAKE_CASE_    			=			'''multi_label_classification'''

											if self.config.problem_type == "regression":
													SCREAMING_SNAKE_CASE_    			=			MSELoss()
													if self.num_labels == 1:
															SCREAMING_SNAKE_CASE_    			=			loss_fct(logits.squeeze()							,							labels.squeeze()     )
													else:
															SCREAMING_SNAKE_CASE_    			=			loss_fct(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
											elif self.config.problem_type == "single_label_classification":
													SCREAMING_SNAKE_CASE_    			=			CrossEntropyLoss()
													SCREAMING_SNAKE_CASE_    			=			loss_fct(logits.view(-1							,							self.num_labels     )							,							labels.view(-1     )     )
											elif self.config.problem_type == "multi_label_classification":
													SCREAMING_SNAKE_CASE_    			=			BCEWithLogitsLoss()
													SCREAMING_SNAKE_CASE_    			=			loss_fct(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									if not return_dict:
											SCREAMING_SNAKE_CASE_    			=			(logits,) + outputs[2:]
											return ((loss,) + output) if loss is not None else output

									return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE_							,							logits=SCREAMING_SNAKE_CASE_							,							hidden_states=outputs.hidden_states     )