



'''simple docstring'''





from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


lowercase_			=  getLogger(__name__)


lowercase_			=  4
lowercase_			=  7

lowercase_			=  {
    "float16": torch.floataa,
    "float32": torch.floataa,
}




def lowerCAmelCase  (__A):



     """simple docstring"""



     if isinstance(__A  ,     torch.Tensor):
          return image
     elif isinstance(__A  ,     PIL.Image.Image):
          _a     =  [image]

     if isinstance(image[0]  ,     PIL.Image.Image):
          _a  ,   _a     =  image[0].size
          _a  ,   _a     =  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

          _a     =  [np.array(i.resize((w, h)))[None, :] for i in image]
          _a     =  np.concatenate(__A  ,     axis=0)
          _a     =  np.array(__A).astype(np.floataa) / 2_55.0
          _a     =  image.transpose(0  ,     3  ,     1  ,     2)
          _a     =  2.0 * image - 1.0
          _a     =  torch.from_numpy(__A)
     elif isinstance(image[0]  ,     torch.Tensor):
          _a     =  torch.cat(__A  ,     dim=0)

     return image






class __A      (  A						):



 '''simple docstring'''







 def __init__(self			,       A			,       A			,       A			,       A			,       A			,       A			,       A = 350			,       )   ->					List[str]:


      """simple docstring"""



      super().__init__(
          vae=A			,       text_encoder=A			,       tokenizer=A			,       unet=A			,       low_res_scheduler=A			,       scheduler=A			,       safety_checker=A			,       feature_extractor=A			,       watermarker=A			,       max_noise_level=A			,       )



 def __call__(self			,       A			,       A			,       A = 75			,       A = 9.0			,       A = 20			,       A = None			,       A = 1			,       A = 0.0			,       A = None			,       A = None			,       A = None			,       A = None			,       A = "pil"			,       A = True			,       A = None			,       A = 1			,       )   ->					Optional[Any]:


      """simple docstring"""



      self.check_inputs(A			,       A			,       A			,       A	)

      # 2. Define call parameters
      if prompt is not None and isinstance(A			,       A	):
           _a     =  1
      elif prompt is not None and isinstance(A			,       A	):
           _a     =  len(A	)
      else:
           _a     =  prompt_embeds.shape[0]

      _a     =  self._execution_device
      # 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

      # 3. Encode input prompt
      _a     =  self._encode_prompt(
          A			,       A			,       A			,       A			,       A			,       prompt_embeds=A			,       negative_prompt_embeds=A			,       )

      _a     =  ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]

      # 4. Preprocess image
      _a     =  preprocess(A	)
      _a     =  image.cpu()

      # 5. set timesteps
      self.scheduler.set_timesteps(A			,       device=A	)
      _a     =  self.scheduler.timesteps

      # 5. Add noise to image
      _a     =  torch.tensor([noise_level]			,       dtype=torch.long			,       device=A	)
      _a     =  torch.randn(image.shape			,       generator=A			,       device=A			,       dtype=A	)
      _a     =  self.low_res_scheduler.add_noise(A			,       A			,       A	)

      _a     =  2 if do_classifier_free_guidance else 1
      _a     =  np.concatenate([image] * batch_multiplier * num_images_per_prompt	)
      _a     =  np.concatenate([noise_level] * image.shape[0]	)

      # 6. Prepare latent variables
      _a  ,   _a     =  image.shape[2:]
      _a     =  self.prepare_latents(
          batch_size * num_images_per_prompt			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       )

      # 7. Check that sizes of image and latents match
      _a     =  image.shape[1]
      if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
           raise ValueError(
               '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
               f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
               f''' `num_channels_image`: {num_channels_image} '''
               f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
               ''' `pipeline.unet` or your `image` input.'''	)

      # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      _a     =  self.prepare_extra_step_kwargs(A			,       A	)

      _a     =  next(
          (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')			,       '''tensor(float)'''	)
      _a     =  ORT_TO_NP_TYPE[timestep_dtype]

      # 9. Denoising loop
      _a     =  len(A	) - num_inference_steps * self.scheduler.order
      with self.progress_bar(total=A	) as progress_bar:
           for i, t in enumerate(A	):
                # expand the latents if we are doing classifier free guidance
                _a     =  np.concatenate([latents] * 2	) if do_classifier_free_guidance else latents

                # concat latents, mask, masked_image_latents in the channel dimension
                _a     =  self.scheduler.scale_model_input(A			,       A	)
                _a     =  np.concatenate([latent_model_input, image]			,       axis=1	)

                # timestep to tensor
                _a     =  np.array([t]			,       dtype=A	)

                # predict the noise residual
                _a     =  self.unet(
                    sample=A			,       timestep=A			,       encoder_hidden_states=A			,       class_labels=noise_level.astype(np.intaa	)			,       )[0]

                # perform guidance
                if do_classifier_free_guidance:
                     _a  ,   _a     =  np.split(A			,       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(
                    torch.from_numpy(A	)			,       A			,       A			,       **A	).prev_sample

                # call the callback, if provided
                if i == len(A	) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                          callback(A			,       A			,       A	)

        # 10. Post-processing
      _a     =  self.decode_latents(latents.float()	)

      # 11. Convert to PIL
      if output_type == "pil":
           _a     =  self.numpy_to_pil(A	)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  1 / 0.08333 * latents
      _a     =  self.vae(latent_sample=A	)[0]
      _a     =  np.clip(image / 2 + 0.5			,       0			,       1	)
      _a     =  image.transpose((0, 2, 3, 1)	)
      return image



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A = None			,       A = None			,       )   ->					Union[str, Any]:


      """simple docstring"""



      if prompt is not None and isinstance(A			,       A	):
           _a     =  1
      elif prompt is not None and isinstance(A			,       A	):
           _a     =  len(A	)
      else:
           _a     =  prompt_embeds.shape[0]

      if prompt_embeds is None:
           _a     =  self.tokenizer(
               A			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=A			,       return_tensors='''pt'''			,       )
           _a     =  text_inputs.input_ids
           _a     =  self.tokenizer(A			,       padding='''longest'''			,       return_tensors='''pt'''	).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
               A			,       A	):
                _a     =  self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]	)
                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}'''	)

           # no positional arguments to text_encoder
           _a     =  self.text_encoder(
               input_ids=text_input_ids.int().to(A	)			,       )
           _a     =  prompt_embeds[0]

      _a  ,   _a  ,   _a     =  prompt_embeds.shape
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      _a     =  prompt_embeds.repeat(1			,       A	)
      _a     =  prompt_embeds.reshape(bs_embed * num_images_per_prompt			,       A			,       -1	)

      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
           _a     =  42
           if negative_prompt is None:
                _a     =  [''''''] * batch_size
           elif type(A	) is not type(A	):
                raise TypeError(
                    f'''`negative_prompt` should be the same type to `prompt`, but got {type(A	)} !='''
                    f''' {type(A	)}.'''	)
           elif isinstance(A			,       A	):
                _a     =  [negative_prompt]
           elif batch_size != len(A	):
                raise ValueError(
                    f'''`negative_prompt`: {negative_prompt} has batch size {len(A	)}, 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(
               A			,       padding='''max_length'''			,       max_length=A			,       truncation=A			,       return_tensors='''pt'''			,       )

           # if hasattr(uncond_input, "attention_mask"):
           #     attention_mask = uncond_input.attention_mask.to(device)
           # else:
           #     attention_mask = None

           _a     =  self.text_encoder(
               input_ids=uncond_input.input_ids.int().to(A	)			,       )
           _a     =  uncond_embeddings[0]

      if do_classifier_free_guidance:
           _a     =  uncond_embeddings.shape[1]
           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           _a     =  uncond_embeddings.repeat(1			,       A	)
           _a     =  uncond_embeddings.reshape(batch_size * num_images_per_prompt			,       A			,       -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     =  np.concatenate([uncond_embeddings, prompt_embeds]	)

      return prompt_embeds








'''simple docstring'''





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase_			=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase_			=  {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase_			=  {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase_			=  sorted(arg_to_scheduler.keys())
lowercase_			=  "{" + ", ".join(arg_to_scheduler_choices) + "}"






class __A      (  pl.LightningModule						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=None			,       A="base"			,       A=None			,       A=None			,       A=None			,       **A			,       )   ->					int:


      """simple docstring"""



      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(A	)
      _a     =  0
      _a     =  Path(self.hparams.output_dir	)
      _a     =  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           _a     =  AutoConfig.from_pretrained(
               self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,       **({'''num_labels''': num_labels} if num_labels is not None else {})			,       cache_dir=A			,       **A			,       )
      else:
           _a     =  config

      _a     =  ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
           if getattr(self.hparams			,       A			,       A	):
                assert hasattr(self.config			,       A	), f'''model config doesn\'t have a `{p}` attribute'''
                setattr(self.config			,       A			,       getattr(self.hparams			,       A	)	)

      if tokenizer is None:
           _a     =  AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,       cache_dir=A			,       )
      else:
           _a     =  tokenizer
      _a     =  MODEL_MODES[mode]
      if model is None:
           _a     =  self.model_type.from_pretrained(
               self.hparams.model_name_or_path			,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path	)			,       config=self.config			,       cache_dir=A			,       )
      else:
           _a     =  model



 def  a__  (self			,       *A			,       **A	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model_type.from_pretrained(*A			,       **A	)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  arg_to_scheduler[self.hparams.lr_scheduler]
      _a     =  get_schedule_func(
          self.opt			,       num_warmup_steps=self.hparams.warmup_steps			,       num_training_steps=self.total_steps()	)
      _a     =  {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler



 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model
      _a     =  ['''bias''', '''LayerNorm.weight''']
      _a     =  [
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
              '''weight_decay''': self.hparams.weight_decay,
          },
          {
              '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
              '''weight_decay''': 0.0,
          },
      ]
      if self.hparams.adafactor:
           _a     =  Adafactor(
               A			,       lr=self.hparams.learning_rate			,       scale_parameter=A			,       relative_step=A	)

      else:
           _a     =  AdamW(
               A			,       lr=self.hparams.learning_rate			,       eps=self.hparams.adam_epsilon	)
      _a     =  optimizer

      _a     =  self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      return self.validation_step(A			,       A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      return self.validation_end(A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a     =  max(1			,       self.hparams.gpus	)  # TODO: consider num_tpu_cores
      _a     =  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



 def  a__  (self			,       A	)   ->					int:


      """simple docstring"""



      if stage == "test":
           _a     =  len(self.test_dataloader().dataset	)
      else:
           _a     =  self.get_dataloader('''train'''			,       self.hparams.train_batch_size			,       shuffle=A	)
           _a     =  len(self.train_dataloader().dataset	)



 def  a__  (self			,       A			,       A			,       A = False	)   ->					Optional[int]:


      """simple docstring"""



      raise NotImplementedError('''You must implement this for your task'''	)



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


      """simple docstring"""



      return self.train_loader



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      return self.get_dataloader('''dev'''			,       self.hparams.eval_batch_size			,       shuffle=A	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



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



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


      """simple docstring"""



      return os.path.join(
          self.hparams.data_dir			,       '''cached_{}_{}_{}'''.format(
              A			,       list(filter(A			,       self.hparams.model_name_or_path.split('''/'''	)	)	).pop()			,       str(self.hparams.max_seq_length	)			,       )			,       )



 @pl.utilities.rank_zero_only
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.output_dir.joinpath('''best_tfmr'''	)
      _a     =  self.step_count
      self.model.save_pretrained(A	)
      self.tokenizer.save_pretrained(A	)



 @staticmethod
 def  a__  (A			,       A	)   ->					str:


      """simple docstring"""



      parser.add_argument(
          '''--model_name_or_path'''			,       default=A			,       type=A			,       required=A			,       help='''Path to pretrained model or model identifier from huggingface.co/models'''			,       )
      parser.add_argument(
          '''--config_name'''			,       default=''''''			,       type=A			,       help='''Pretrained config name or path if not the same as model_name'''	)
      parser.add_argument(
          '''--tokenizer_name'''			,       default=A			,       type=A			,       help='''Pretrained tokenizer name or path if not the same as model_name'''			,       )
      parser.add_argument(
          '''--cache_dir'''			,       default=''''''			,       type=A			,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''			,       )
      parser.add_argument(
          '''--encoder_layerdrop'''			,       type=A			,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--decoder_layerdrop'''			,       type=A			,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--dropout'''			,       type=A			,       help='''Dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--attention_dropout'''			,       type=A			,       help='''Attention dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument('''--learning_rate'''			,       default=5E-5			,       type=A			,       help='''The initial learning rate for Adam.'''	)
      parser.add_argument(
          '''--lr_scheduler'''			,       default='''linear'''			,       choices=A			,       metavar=A			,       type=A			,       help='''Learning rate scheduler'''			,       )
      parser.add_argument('''--weight_decay'''			,       default=0.0			,       type=A			,       help='''Weight decay if we apply some.'''	)
      parser.add_argument('''--adam_epsilon'''			,       default=1E-8			,       type=A			,       help='''Epsilon for Adam optimizer.'''	)
      parser.add_argument('''--warmup_steps'''			,       default=0			,       type=A			,       help='''Linear warmup over warmup_steps.'''	)
      parser.add_argument('''--num_workers'''			,       default=4			,       type=A			,       help='''kwarg passed to DataLoader'''	)
      parser.add_argument('''--num_train_epochs'''			,       dest='''max_epochs'''			,       default=3			,       type=A	)
      parser.add_argument('''--train_batch_size'''			,       default=32			,       type=A	)
      parser.add_argument('''--eval_batch_size'''			,       default=32			,       type=A	)
      parser.add_argument('''--adafactor'''			,       action='''store_true'''	)






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					str:


      """simple docstring"""



      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      _a     =  trainer.lr_schedulers[0]['''scheduler''']
      _a     =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
      pl_module.logger.log_metrics(A	)



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      rank_zero_info('''***** Validation results *****'''	)
      _a     =  trainer.callback_metrics
      # Log results
      for key in sorted(A	):
           if key not in ["log", "progress_bar"]:
                rank_zero_info('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)



 def  a__  (self			,       A			,       A	)   ->					Dict:


      """simple docstring"""



      rank_zero_info('''***** Test results *****'''	)
      _a     =  trainer.callback_metrics
      # Log and save results to file
      _a     =  os.path.join(pl_module.hparams.output_dir			,       '''test_results.txt'''	)
      with open(A			,       '''w'''	) as writer:
           for key in sorted(A	):
                if key not in ["log", "progress_bar"]:
                     rank_zero_info('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)
                     writer.write('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)





def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     parser.add_argument(
         '''--output_dir'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     help='''The output directory where the model predictions and checkpoints will be written.'''  ,     )
     parser.add_argument(
         '''--fp16'''  ,     action='''store_true'''  ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''  ,     )

     parser.add_argument(
         '''--fp16_opt_level'''  ,     type=__A  ,     default='''O2'''  ,     help=(
             '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
             '''See details at https://nvidia.github.io/apex/amp.html'''
         )  ,     )
     parser.add_argument('''--n_tpu_cores'''  ,     dest='''tpu_cores'''  ,     type=__A)
     parser.add_argument('''--max_grad_norm'''  ,     dest='''gradient_clip_val'''  ,     default=1.0  ,     type=__A  ,     help='''Max gradient norm''')
     parser.add_argument('''--do_train'''  ,     action='''store_true'''  ,     help='''Whether to run training.''')
     parser.add_argument('''--do_predict'''  ,     action='''store_true'''  ,     help='''Whether to run predictions on the test set.''')
     parser.add_argument(
         '''--gradient_accumulation_steps'''  ,     dest='''accumulate_grad_batches'''  ,     type=__A  ,     default=1  ,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,     )
     parser.add_argument('''--seed'''  ,     type=__A  ,     default=42  ,     help='''random seed for initialization''')
     parser.add_argument(
         '''--data_dir'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,     )




def lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=True  ,     __A=None  ,     __A=[]  ,     __A=None  ,     __A=None  ,     **__A  ,     ):



     """simple docstring"""



     pl.seed_everything(args.seed)

     # init model
     _a     =  Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__A)

     # add custom checkpoints
     if checkpoint_callback is None:
          _a     =  pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir  ,     prefix='''checkpoint'''  ,     monitor='''val_loss'''  ,     mode='''min'''  ,     save_top_k=1)
     if early_stopping_callback:
          extra_callbacks.append(__A)
     if logging_callback is None:
          _a     =  LoggingCallback()

     _a     =  {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _a     =  16
          # train_params["amp_level"] = args.fp16_opt_level

     if args.gpus > 1:
          _a     =  '''auto'''  # "ddp"
          _a     =  '''ddp'''

     _a     =  args.accumulate_grad_batches
     _a     =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _a     =  '''auto'''

     _a     =  pl.Trainer.from_argparse_args(
         __A  ,     weights_summary=__A  ,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,     logger=__A  ,     **__A  ,     )

     if args.do_train:
          trainer.fit(__A)

     return trainer



