





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

SCREAMING_SNAKE_CASE_		     = imread(r"""digital_image_processing/image_data/lena_small.jpg""")
SCREAMING_SNAKE_CASE_		     = cvtColor(img, COLOR_BGR2GRAY)



def      __lowercase     (   )				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		cn.convert_to_negative(_SCREAMING_SNAKE_CASE			)
      # assert negative_img array for at least one True
      assert negative_img.any()



def      __lowercase     (   )				->     Tuple:


      '''simple docstring'''


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""			) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(_SCREAMING_SNAKE_CASE				,		1_10			)			).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""			)



def      __lowercase     (   )				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		canny.gen_gaussian_kernel(9				,		sigma=1.4			)
      # Assert ambiguous array
      assert resp.all()



def      __lowercase     (   )				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		imread("""digital_image_processing/image_data/lena_small.jpg"""				,		0			)
      # assert ambiguous array for all == True
      assert canny_img.all()
      SCREAMING_SNAKE_CASE	=		canny.canny(_SCREAMING_SNAKE_CASE			)
      # assert canny array for at least one True
      assert canny_array.any()



def      __lowercase     (   )				->     Tuple:


      '''simple docstring'''


      assert gg.gaussian_filter(_SCREAMING_SNAKE_CASE				,		5				,		sigma=0.9			).all()



def      __lowercase     (   )				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
      SCREAMING_SNAKE_CASE	=		conv.img_convolve(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			).astype(_SCREAMING_SNAKE_CASE			)
      assert res.any()



def      __lowercase     (   )				->     Tuple:


      '''simple docstring'''


      assert med.median_filter(_SCREAMING_SNAKE_CASE				,		3			).any()



def      __lowercase     (   )				->     List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		sob.sobel_filter(_SCREAMING_SNAKE_CASE			)
      assert grad.any() and theta.any()



def      __lowercase     (   )				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		sp.make_sepia(_SCREAMING_SNAKE_CASE				,		20			)
      assert sepia.all()



def      __lowercase     (   _SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		bs.Burkes(imread(_SCREAMING_SNAKE_CASE				,		1			)				,		1_20			)
      burkes.process()
      assert burkes.output_img.any()



def      __lowercase     (   _SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"				,		)				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		rs.NearestNeighbour(imread(_SCREAMING_SNAKE_CASE				,		1			)				,		4_00				,		2_00			)
      nn.process()
      assert nn.output.any()



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      SCREAMING_SNAKE_CASE	=		imread(_SCREAMING_SNAKE_CASE				,		0			)

      # Test for get_neighbors_pixel function() return not None
      SCREAMING_SNAKE_CASE	=		0
      SCREAMING_SNAKE_CASE	=		0
      SCREAMING_SNAKE_CASE	=		image[x_coordinate][y_coordinate]

      SCREAMING_SNAKE_CASE	=		lbp.get_neighbors_pixel(
          _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      SCREAMING_SNAKE_CASE	=		np.zeros((image.shape[0], image.shape[1])			)

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0				,		image.shape[0]			):
            for j in range(0				,		image.shape[1]			):
                  SCREAMING_SNAKE_CASE	=		lbp.local_binary_value(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)

      assert lbp_image.any()









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)


