





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


SCREAMING_SNAKE_CASE_		     = logging.getLogger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    "summarization"
     __snake_case					:			List[str]				=    ["loss"]
     __snake_case					:			Optional[int]				=    ROUGE_KEYS
     __snake_case					:			List[Any]				=    "rouge2"






     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[Any]		,**lowerCamelCase__		:					List[Any] )  ->	str:




           '''simple docstring'''


           if hparams.sortish_sampler and hparams.gpus > 1:
                 SCREAMING_SNAKE_CASE	=		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__(lowerCamelCase__		,num_labels=lowerCamelCase__		,mode=self.mode		,**lowerCamelCase__ )
           use_task_specific_params(self.model		,"""summarization""" )
           save_git_info(self.hparams.output_dir )
           SCREAMING_SNAKE_CASE	=		Path(self.output_dir ) / """metrics.json"""
           SCREAMING_SNAKE_CASE	=		Path(self.output_dir ) / """hparams.pkl"""
           pickle_save(self.hparams		,self.hparams_save_path )
           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		defaultdict(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		self.config.model_type
           SCREAMING_SNAKE_CASE	=		self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

           SCREAMING_SNAKE_CASE	=		{
               """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() )

           SCREAMING_SNAKE_CASE	=		get_git_info()["""repo_sha"""]
           SCREAMING_SNAKE_CASE	=		hparams.num_workers
           SCREAMING_SNAKE_CASE	=		None  # default to config
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                 SCREAMING_SNAKE_CASE	=		self.decoder_start_token_id
           SCREAMING_SNAKE_CASE	=		(
               SeqaSeqDataset if hasattr(self.tokenizer		,"""prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
           )
           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		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:
                 SCREAMING_SNAKE_CASE	=		self.hparams.eval_max_gen_length
           else:
                 SCREAMING_SNAKE_CASE	=		self.model.config.max_length
           SCREAMING_SNAKE_CASE	=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Dict[str, torch.Tensor] )  ->	Dict[str, List[str]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{
               k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
           }
           save_json(lowerCamelCase__		,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""" )

           SCREAMING_SNAKE_CASE	=		True
           return readable_batch






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					str		,**lowerCamelCase__		:					Union[str, Any] )  ->	int:




           '''simple docstring'''


           return self.model(lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					List[int] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer.batch_decode(
               lowerCamelCase__		,skip_special_tokens=lowerCamelCase__		,clean_up_tokenization_spaces=lowerCamelCase__ )
           return lmap(str.strip		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					dict )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer.pad_token_id
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		batch["""input_ids"""], batch["""attention_mask"""]
           SCREAMING_SNAKE_CASE	=		batch["""labels"""]
           if isinstance(self.model		,lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		self.model._shift_right(lowerCamelCase__ )
           else:
                 SCREAMING_SNAKE_CASE	=		shift_tokens_right(lowerCamelCase__		,lowerCamelCase__ )
           if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                 SCREAMING_SNAKE_CASE	=		decoder_input_ids
                 self.save_readable_batch(lowerCamelCase__ )

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

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






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	int:




           '''simple docstring'''


           return self.tokenizer.pad_token_id






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Any )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self._step(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		dict(zip(self.loss_names		,lowerCamelCase__ ) )
           # tokens per batch
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].shape[0]
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].eq(self.pad ).sum()
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           return self._generative_step(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					List[str]="val" )  ->	Dict:




           '''simple docstring'''


           self.step_count += 1
           SCREAMING_SNAKE_CASE	=		{k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
           SCREAMING_SNAKE_CASE	=		losses["""loss"""]
           SCREAMING_SNAKE_CASE	=		{
               k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
           }
           SCREAMING_SNAKE_CASE	=		(
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           SCREAMING_SNAKE_CASE	=		torch.tensor(lowerCamelCase__ ).type_as(lowerCamelCase__ )
           generative_metrics.update({k: v.item() for k, v in losses.items()} )
           losses.update(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
           SCREAMING_SNAKE_CASE	=		self.step_count
           self.metrics[prefix].append(lowerCamelCase__ )  # callback writes this to self.metrics_save_path
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Union[str, Any] )  ->	Dict:




           '''simple docstring'''


           return calculate_rouge(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					dict )  ->	dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		time.time()

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[Any] )  ->	Any:




           '''simple docstring'''


           return self._generative_step(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Union[str, Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           return self.validation_epoch_end(lowerCamelCase__		,prefix="""test""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Tuple )  ->	SeqaSeqDataset:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.n_obs[type_path]
           SCREAMING_SNAKE_CASE	=		self.target_lens[type_path]
           SCREAMING_SNAKE_CASE	=		self.dataset_class(
               self.tokenizer		,type_path=lowerCamelCase__		,n_obs=lowerCamelCase__		,max_target_length=lowerCamelCase__		,**self.dataset_kwargs		,)
           return dataset






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					bool = False )  ->	DataLoader:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dataset(lowerCamelCase__ )

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

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	DataLoader:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dataloader("""train"""		,batch_size=self.hparams.train_batch_size		,shuffle=lowerCamelCase__ )
           return dataloader






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	DataLoader:




           '''simple docstring'''


           return self.get_dataloader("""val"""		,batch_size=self.hparams.eval_batch_size )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	DataLoader:




           '''simple docstring'''


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






     @staticmethod
     def        SCREAMING_SNAKE_CASE__						(							lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


           BaseTransformer.add_model_specific_args(lowerCamelCase__		,lowerCamelCase__ )
           add_generic_args(lowerCamelCase__		,lowerCamelCase__ )
           parser.add_argument(
               """--max_source_length"""		,default=1024		,type=lowerCamelCase__		,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=lowerCamelCase__		,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=lowerCamelCase__		,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=lowerCamelCase__		,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=lowerCamelCase__ )
           parser.add_argument("""--overwrite_output_dir"""		,action="""store_true"""		,default=lowerCamelCase__ )
           parser.add_argument("""--max_tokens_per_batch"""		,type=lowerCamelCase__		,default=lowerCamelCase__ )
           parser.add_argument("""--logger_name"""		,type=lowerCamelCase__		,choices=["""default""", """wandb""", """wandb_shared"""]		,default="""default""" )
           parser.add_argument("""--n_train"""		,type=lowerCamelCase__		,default=-1		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument("""--n_val"""		,type=lowerCamelCase__		,default=500		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument("""--n_test"""		,type=lowerCamelCase__		,default=-1		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument(
               """--task"""		,type=lowerCamelCase__		,default="""summarization"""		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument("""--label_smoothing"""		,type=lowerCamelCase__		,default=0.0		,required=lowerCamelCase__ )
           parser.add_argument("""--src_lang"""		,type=lowerCamelCase__		,default=""""""		,required=lowerCamelCase__ )
           parser.add_argument("""--tgt_lang"""		,type=lowerCamelCase__		,default=""""""		,required=lowerCamelCase__ )
           parser.add_argument("""--eval_beams"""		,type=lowerCamelCase__		,default=lowerCamelCase__		,required=lowerCamelCase__ )
           parser.add_argument(
               """--val_metric"""		,type=lowerCamelCase__		,default=lowerCamelCase__		,required=lowerCamelCase__		,choices=["""bleu""", """rouge2""", """loss""", None] )
           parser.add_argument("""--eval_max_gen_length"""		,type=lowerCamelCase__		,default=lowerCamelCase__		,help="""never generate more than n tokens""" )
           parser.add_argument("""--save_top_k"""		,type=lowerCamelCase__		,default=1		,required=lowerCamelCase__		,help="""How many checkpoints to save""" )
           parser.add_argument(
               """--early_stopping_patience"""		,type=lowerCamelCase__		,default=-1		,required=lowerCamelCase__		,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 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[Any]				=    "translation"
     __snake_case					:			Tuple				=    ["loss"]
     __snake_case					:			Union[str, Any]				=    ["bleu"]
     __snake_case					:			str				=    "bleu"






     def __init__(							self		:					str		,lowerCamelCase__		:					Optional[int]		,**lowerCamelCase__		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           super().__init__(lowerCamelCase__		,**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		hparams.src_lang
           SCREAMING_SNAKE_CASE	=		hparams.tgt_lang






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple )  ->	dict:




           '''simple docstring'''


           return calculate_bleu(lowerCamelCase__		,lowerCamelCase__ )



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None			)				->     SummarizationModule:


      '''simple docstring'''


      Path(args.output_dir			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
      check_output_dir(_SCREAMING_SNAKE_CASE				,		expected_items=3			)

      if model is None:
            if "summarization" in args.task:
                  SCREAMING_SNAKE_CASE	=		SummarizationModule(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		TranslationModule(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		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"""			)
      ):
            SCREAMING_SNAKE_CASE	=		True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            SCREAMING_SNAKE_CASE	=		os.environ.get("""WANDB_PROJECT"""				,		_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		WandbLogger(name=model.output_dir.name				,		project=_SCREAMING_SNAKE_CASE			)

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

            SCREAMING_SNAKE_CASE	=		WandbLogger(name=model.output_dir.name				,		project=F"""hf_{dataset}"""			)

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

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

      SCREAMING_SNAKE_CASE	=		""""""
      SCREAMING_SNAKE_CASE	=		sorted(glob.glob(os.path.join(args.output_dir				,		"""*.ckpt"""			)				,		recursive=_SCREAMING_SNAKE_CASE			)			)
      if checkpoints:
            SCREAMING_SNAKE_CASE	=		checkpoints[-1]
            SCREAMING_SNAKE_CASE	=		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__":
      SCREAMING_SNAKE_CASE_		     = argparse.ArgumentParser()
      SCREAMING_SNAKE_CASE_		     = pl.Trainer.add_argparse_args(parser)
      SCREAMING_SNAKE_CASE_		     = SummarizationModule.add_model_specific_args(parser, os.getcwd())

      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      main(args)









from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFResNetForImageClassification, TFResNetModel
      from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Optional[Any]=32		,lowerCamelCase__		:					Any=3		,lowerCamelCase__		:					Any=10		,lowerCamelCase__		:					Any=[10, 20, 30, 40]		,lowerCamelCase__		:					Any=[1, 1, 2, 1]		,lowerCamelCase__		:					Union[str, Any]=True		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Union[str, Any]="relu"		,lowerCamelCase__		:					Dict=3		,lowerCamelCase__		:					Any=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		embeddings_size
           SCREAMING_SNAKE_CASE	=		hidden_sizes
           SCREAMING_SNAKE_CASE	=		depths
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		num_labels
           SCREAMING_SNAKE_CASE	=		scope
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

           SCREAMING_SNAKE_CASE	=		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE	=		ids_tensor([self.batch_size]		,self.num_labels )

           SCREAMING_SNAKE_CASE	=		self.get_config()

           return config, pixel_values, labels






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




           '''simple docstring'''


           return ResNetConfig(
               num_channels=self.num_channels		,embeddings_size=self.embeddings_size		,hidden_sizes=self.hidden_sizes		,depths=self.depths		,hidden_act=self.hidden_act		,num_labels=self.num_labels		,image_size=self.image_size		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.num_labels
           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,labels=lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_tf
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __snake_case					:			int				=    (
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			str				=    False






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






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




           '''simple docstring'''


           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           return






     @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		inspect.signature(model.call )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 SCREAMING_SNAKE_CASE	=		[*signature.parameters.keys()]

                 SCREAMING_SNAKE_CASE	=		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict ):
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__ ) )

                 SCREAMING_SNAKE_CASE	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE	=		self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__ )		,expected_num_stages + 1 )

                 # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:] )		,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,)

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE	=		["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       SCREAMING_SNAKE_CASE	=		layer_type
                       SCREAMING_SNAKE_CASE	=		True
                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

                       # check that output_hidden_states also work using config
                       del inputs_dict["output_hidden_states"]
                       SCREAMING_SNAKE_CASE	=		True

                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		TFResNetModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image



@require_tf
@require_vision
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
               if is_vision_available()
               else None
           )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-11.1069, -9.7877, -8.3777] )

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,lowerCamelCase__		,atol=1e-4 ) )


