





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
      from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					str )  ->	Optional[int]:




           '''simple docstring'''


           for model_result in results.values():
                 for batch_size, sequence_length in zip(model_result["""bs"""]		,model_result["""ss"""] ):
                       SCREAMING_SNAKE_CASE	=		model_result["""result"""][batch_size][sequence_length]
                       self.assertIsNotNone(lowerCamelCase__ )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sgugger/tiny-distilbert-classification"""
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,only_pretrain_model=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,torchscript=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






     @unittest.skipIf(torch_device == """cpu"""		,"""Cant do half precision""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,fpaa=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		AutoConfig.from_pretrained(lowerCamelCase__ )
           # set architectures equal to `None`
           SCREAMING_SNAKE_CASE	=		None
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__		,configs=[config] )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result )
           self.check_results_dict_not_empty(results.memory_train_result )






     @unittest.skipIf(torch_device == """cpu"""		,"""Can\'t do half precision""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,fpaa=lowerCamelCase__		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result )
           self.check_results_dict_not_empty(results.memory_train_result )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		AutoConfig.from_pretrained(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__		,configs=[config] )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tinier_bart"""
           SCREAMING_SNAKE_CASE	=		AutoConfig.from_pretrained(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__		,configs=[config] )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result )
           self.check_results_dict_not_empty(results.memory_inference_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           SCREAMING_SNAKE_CASE	=		AutoConfig.from_pretrained(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__		,configs=[config] )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result )
           self.check_results_dict_not_empty(results.memory_train_result )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tinier_bart"""
           SCREAMING_SNAKE_CASE	=		AutoConfig.from_pretrained(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__		,configs=[config] )
           SCREAMING_SNAKE_CASE	=		benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result )
           self.check_results_dict_not_empty(results.memory_train_result )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""
           with tempfile.TemporaryDirectory() as tmp_dir:
                 SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
                     models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,save_to_csv=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,inference_time_csv_file=os.path.join(lowerCamelCase__		,"""inf_time.csv""" )		,train_memory_csv_file=os.path.join(lowerCamelCase__		,"""train_mem.csv""" )		,inference_memory_csv_file=os.path.join(lowerCamelCase__		,"""inf_mem.csv""" )		,train_time_csv_file=os.path.join(lowerCamelCase__		,"""train_time.csv""" )		,env_info_csv_file=os.path.join(lowerCamelCase__		,"""env.csv""" )		,multi_process=lowerCamelCase__		,)
                 SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
                 benchmark.run()
                 self.assertTrue(Path(os.path.join(lowerCamelCase__		,"""inf_time.csv""" ) ).exists() )
                 self.assertTrue(Path(os.path.join(lowerCamelCase__		,"""train_time.csv""" ) ).exists() )
                 self.assertTrue(Path(os.path.join(lowerCamelCase__		,"""inf_mem.csv""" ) ).exists() )
                 self.assertTrue(Path(os.path.join(lowerCamelCase__		,"""train_mem.csv""" ) ).exists() )
                 self.assertTrue(Path(os.path.join(lowerCamelCase__		,"""env.csv""" ) ).exists() )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""sshleifer/tiny-gpt2"""

           def _check_summary_is_not_empty(lowerCamelCase__		:					Optional[Any] ):
                 self.assertTrue(hasattr(lowerCamelCase__		,"""sequential""" ) )
                 self.assertTrue(hasattr(lowerCamelCase__		,"""cumulative""" ) )
                 self.assertTrue(hasattr(lowerCamelCase__		,"""current""" ) )
                 self.assertTrue(hasattr(lowerCamelCase__		,"""total""" ) )

           with tempfile.TemporaryDirectory() as tmp_dir:
                 SCREAMING_SNAKE_CASE	=		PyTorchBenchmarkArguments(
                     models=[MODEL_ID]		,training=lowerCamelCase__		,inference=lowerCamelCase__		,sequence_lengths=[8]		,batch_sizes=[1]		,log_filename=os.path.join(lowerCamelCase__		,"""log.txt""" )		,log_print=lowerCamelCase__		,trace_memory_line_by_line=lowerCamelCase__		,multi_process=lowerCamelCase__		,)
                 SCREAMING_SNAKE_CASE	=		PyTorchBenchmark(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		benchmark.run()
                 _check_summary_is_not_empty(result.inference_summary )
                 _check_summary_is_not_empty(result.train_summary )
                 self.assertTrue(Path(os.path.join(lowerCamelCase__		,"""log.txt""" ) ).exists() )









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)


