




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__UpperCamelCase   :			Any          = logging.get_logger(__name__)  # pylint: disable=invalid-name

class 						__lowerCAmelCase      (      _a					):
							def __init__(		self		:Union[str, Any]						, __magic_name__		:AutoencoderKL						, __magic_name__		:CLIPTextModel						, __magic_name__		:CLIPTokenizer						, __magic_name__		:UNetaDConditionModel						, __magic_name__		:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						, __magic_name__		:StableDiffusionSafetyChecker						, __magic_name__		:CLIPImageProcessor						, ):







								'''simple docstring'''







								super().__init__()
								self.register_modules(
								    vae=__magic_name__						, text_encoder=__magic_name__						, tokenizer=__magic_name__						, unet=__magic_name__						, scheduler=__magic_name__						, safety_checker=__magic_name__						, feature_extractor=__magic_name__						, )
							def  lowerCamelCase__				(		self		:str						, __magic_name__		:Optional[Union[str, int]] = "auto"							):







								'''simple docstring'''







								if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									a									=				self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__magic_name__							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								self.enable_attention_slicing(__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:Optional[int]						, __magic_name__		:Union[str, List[str]]						, __magic_name__		:int = 512						, __magic_name__		:int = 512						, __magic_name__		:int = 50						, __magic_name__		:float = 7.5						, __magic_name__		:Optional[Union[str, List[str]]] = None						, __magic_name__		:Optional[int] = 1						, __magic_name__		:float = 0.0						, __magic_name__		:Optional[torch.Generator] = None						, __magic_name__		:Optional[torch.FloatTensor] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, __magic_name__		:Optional[Callable[[int, int, torch.FloatTensor], None]] = None						, __magic_name__		:int = 1						, __magic_name__		:Optional[torch.FloatTensor] = None						, **__magic_name__		:int						, ):







								'''simple docstring'''







								if isinstance(__magic_name__						, __magic_name__							):
									a									=				1
								elif isinstance(__magic_name__						, __magic_name__							):
									a									=				len(__magic_name__							)
								else:
									raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__magic_name__							)}'							)

								if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'							)

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(__magic_name__						, __magic_name__							) or callback_steps <= 0)
								):
									raise ValueError(
									    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
									    F' {type(__magic_name__							)}.'							)

								# get prompt text embeddings
								a									=				self.tokenizer(
								    __magic_name__						, padding="""max_length"""						, max_length=self.tokenizer.model_max_length						, return_tensors="""pt"""						, )
								a									=				text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									a									=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
									a									=				text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
									a									=				self.text_encoder(text_input_ids.to(self.device							)							)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								a							,					a							,					a									=				text_embeddings.shape
								a									=				text_embeddings.repeat(1						, __magic_name__						, 1							)
								a									=				text_embeddings.view(bs_embed * num_images_per_prompt						, __magic_name__						, -1							)

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								a									=				guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									a									=				42
									if negative_prompt is None:
										a									=				[""""""]
									elif type(__magic_name__							) is not type(__magic_name__							):
										raise TypeError(
										    F'`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__							)} !='
										    F' {type(__magic_name__							)}.'							)
									elif isinstance(__magic_name__						, __magic_name__							):
										a									=				[negative_prompt]
									elif batch_size != len(__magic_name__							):
										raise ValueError(
										    F'`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__							)}, but `prompt`:'
										    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
										    """ the batch size of `prompt`."""							)
									else:
										a									=				negative_prompt

									a									=				text_input_ids.shape[-1]
									a									=				self.tokenizer(
									    __magic_name__						, padding="""max_length"""						, max_length=__magic_name__						, truncation=__magic_name__						, return_tensors="""pt"""						, )
									a									=				self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									a									=				uncond_embeddings.shape[1]
									a									=				uncond_embeddings.repeat(__magic_name__						, __magic_name__						, 1							)
									a									=				uncond_embeddings.view(batch_size * num_images_per_prompt						, __magic_name__						, -1							)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									a									=				torch.cat([uncond_embeddings, text_embeddings]							)

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								a									=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								a									=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								a									=				text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not exist on mps
										a									=				torch.randn(
										    __magic_name__						, generator=__magic_name__						, device="""cpu"""						, dtype=__magic_name__							).to(self.device							)
										a									=				torch.randn(__magic_name__						, generator=__magic_name__						, device="""cpu"""						, dtype=__magic_name__							).to(
										    self.device							)
									else:
										a									=				torch.randn(
										    __magic_name__						, generator=__magic_name__						, device=self.device						, dtype=__magic_name__							)
										a									=				torch.randn(__magic_name__						, generator=__magic_name__						, device=self.device						, dtype=__magic_name__							)
								else:
									if latents_reference.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
									a									=				latents_reference.to(self.device							)
									a									=				latents.to(self.device							)

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								a									=				(latents_shape[3] - latents_shape_reference[3]) // 2
								a									=				(latents_shape[2] - latents_shape_reference[2]) // 2
								a									=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								a									=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								a									=				0 if dx < 0 else dx
								a									=				0 if dy < 0 else dy
								a									=				max(-dx						, 0							)
								a									=				max(-dy						, 0							)
								# import pdb
								# pdb.set_trace()
								a									=				latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(__magic_name__							)

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								a									=				self.scheduler.timesteps.to(self.device							)

								# scale the initial noise by the standard deviation required by the scheduler
								a									=				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a									=				"""eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
								a									=				{}
								if accepts_eta:
									a									=				eta

								for i, t in enumerate(self.progress_bar(__magic_name__							)							):
									# expand the latents if we are doing classifier free guidance
									a									=				torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
									a									=				self.scheduler.scale_model_input(__magic_name__						, __magic_name__							)

									# predict the noise residual
									a									=				self.unet(__magic_name__						, __magic_name__						, encoder_hidden_states=__magic_name__							).sample

									# perform guidance
									if do_classifier_free_guidance:
										a							,					a									=				noise_pred.chunk(2							)
										a									=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									a									=				self.scheduler.step(__magic_name__						, __magic_name__						, __magic_name__						, **__magic_name__							).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
										callback(__magic_name__						, __magic_name__						, __magic_name__							)

								a									=				1 / 0.18215 * latents
								a									=				self.vae.decode(__magic_name__							).sample

								a									=				(image / 2 + 0.5).clamp(0						, 1							)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a									=				image.cpu().permute(0						, 2						, 3						, 1							).float().numpy()

								if self.safety_checker is not None:
									a									=				self.feature_extractor(self.numpy_to_pil(__magic_name__							)						, return_tensors="""pt"""							).to(
									    self.device							)
									a							,					a									=				self.safety_checker(
									    images=__magic_name__						, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
								else:
									a									=				None

								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=__magic_name__						, nsfw_content_detected=__magic_name__							)









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 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		int					='''summarization'''
       __lowerCAmelCase    :		str					=['''loss''']
       __lowerCAmelCase    :		Dict					=ROUGE_KEYS
       __lowerCAmelCase    :		str					='''rouge2'''





       def __init__(				self	:Dict,      snake_case	:List[Any],      **snake_case	:Tuple):



          """simple docstring"""

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

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

          _lowercase              ={
              '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())

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





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Dict[str, torch.Tensor]):



          """simple docstring"""

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

          _lowercase              =True
          return readable_batch





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[str],      **snake_case	:List[Any]):



          """simple docstring"""

          return self.model(snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:List[int]):



          """simple docstring"""

          _lowercase              =self.tokenizer.batch_decode(
              snake_case,      skip_special_tokens=snake_case,      clean_up_tokenization_spaces=snake_case)
          return lmap(str.strip,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:dict):



          """simple docstring"""

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

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

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





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.tokenizer.pad_token_id





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Dict,      snake_case	:str):



          """simple docstring"""

          _lowercase              =self._step(snake_case)

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





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Dict,      snake_case	:List[Any]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:List[str],      snake_case	:str="val"):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:List[str]):



          """simple docstring"""

          return calculate_rouge(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:dict):



          """simple docstring"""

          _lowercase              =time.time()

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





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:str,      snake_case	:Optional[int]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:List[str]):



          """simple docstring"""

          return self.validation_epoch_end(snake_case,      prefix='test')





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:str):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:str,      snake_case	:str,      snake_case	:int,      snake_case	:bool = False):



          """simple docstring"""

          _lowercase              =self.get_dataset(snake_case)

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

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





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.get_dataloader('train',      batch_size=self.hparams.train_batch_size,      shuffle=snake_case)
          return dataloader





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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





       @staticmethod
       def 				UpperCamelCase__						(				snake_case	:Dict,      snake_case	:List[str]):



          """simple docstring"""

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


       __lowerCAmelCase    :		Union[str, Any]					='''translation'''
       __lowerCAmelCase    :		Any					=['''loss''']
       __lowerCAmelCase    :		Optional[int]					=['''bleu''']
       __lowerCAmelCase    :		Any					='''bleu'''





       def __init__(				self	:str,      snake_case	:Union[str, Any],      **snake_case	:Any):



          """simple docstring"""

          super().__init__(snake_case,      **snake_case)
          _lowercase              =hparams.src_lang
          _lowercase              =hparams.tgt_lang





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any],      snake_case	:Any):



          """simple docstring"""

          return calculate_bleu(snake_case,      snake_case)





def        _snake_case   (_snake_case				:       Dict      ,    _snake_case				:       int=None)					->	SummarizationModule:
   Path(args.output_dir).mkdir(exist_ok=_snake_case)
   check_output_dir(_snake_case      ,    expected_items=3)

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

      _lowercase              =os.environ.get('WANDB_PROJECT'      ,    _snake_case)
      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=_snake_case)

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

      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=f'''hf_{dataset}''')

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

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

   _lowercase              =''
   _lowercase              =sorted(glob.glob(os.path.join(args.output_dir      ,    '*.ckpt')      ,    recursive=_snake_case))
   if checkpoints:
      _lowercase              =checkpoints[-1]
      _lowercase              =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)


