


'''simple docstring'''



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							str=False				)	->						Tuple:
     __lowerCamelCase  :      Optional[Any]														=						OmegaConf.load(UpperCAmelCase_				)
     if display:
          print(yaml.dump(OmegaConf.to_container(UpperCAmelCase_				)				)				)
     return config



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Dict=None ,       UpperCAmelCase_       :							List[Any]=None				)	->						Dict:
     if conf_path is None:
          __lowerCamelCase  :      Union[str, Any]														=						'./model_checkpoints/vqgan_only.yaml'
     __lowerCamelCase  :      Dict														=						load_config(UpperCAmelCase_ ,       display=UpperCAmelCase_				)
     __lowerCamelCase  :      Union[str, Any]														=						VQModel(**config.model.params				)
     if ckpt_path is None:
          __lowerCamelCase  :      Optional[int]														=						'./model_checkpoints/vqgan_only.pt'
     __lowerCamelCase  :      Optional[int]														=						torch.load(UpperCAmelCase_ ,       map_location=UpperCAmelCase_				)
     if ".ckpt" in ckpt_path:
          __lowerCamelCase  :      List[Any]														=						sd['state_dict']
     model.load_state_dict(UpperCAmelCase_ ,       strict=UpperCAmelCase_				)
     model.to(UpperCAmelCase_				)
     del sd
     return model



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Tuple				)	->						Optional[int]:
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						model.encode(UpperCAmelCase_				)
     print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'				)
     __lowerCamelCase  :      Optional[int]														=						model.decode(UpperCAmelCase_				)
     return xrec



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Dict=False				)	->						Optional[int]:
     __lowerCamelCase							,			__lowerCamelCase  :      Any														=						string.rsplit('.' ,       1				)
     if reload:
          __lowerCamelCase  :      Optional[Any]														=						importlib.import_module(UpperCAmelCase_				)
          importlib.reload(UpperCAmelCase_				)
     return getattr(importlib.import_module(UpperCAmelCase_ ,       package=UpperCAmelCase_				) ,       cls				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						Optional[int]:
     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params' ,       {}				)				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							List[Any]=True ,       UpperCAmelCase_       :							int=True				)	->						Dict:
     __lowerCamelCase  :      Optional[Any]														=						instantiate_from_config(UpperCAmelCase_				)
     if sd is not None:
          model.load_state_dict(UpperCAmelCase_				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							List[str]				)	->						Any:
     # load the specified checkpoint
     if ckpt:
          __lowerCamelCase  :      int														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)
          __lowerCamelCase  :      Union[str, Any]														=						pl_sd['global_step']
          print(F'loaded model from global step {global_step}.'				)
     else:
          __lowerCamelCase  :      Any														=						{'state_dict': None}
          __lowerCamelCase  :      str														=						None
     __lowerCamelCase  :      str														=						load_model_from_config(config.model ,       pl_sd['state_dict'] ,       gpu=UpperCAmelCase_ ,       eval_mode=UpperCAmelCase_				)['model']
     return model, global_step








"""simple docstring"""




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a		:       Optional[Any]     						=       False
a		:       str     						=       False



def     __magic_name__		(					UpperCamelCase     :		Namespace     )     ->						Optional[int]:
    return TrainCommand(UpperCamelCase     )







class     lowercase(_lowercase       ):



     @staticmethod
     def 			lowercase__  (							__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			parser.add_parser('train'     ,				help='CLI tool to train a model on a task.'   )

         train_parser.add_argument(
             '--train_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				required=__SCREAMING_SNAKE_CASE     ,				help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,				)
         train_parser.add_argument(
             '--column_label'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0     ,				help='Column of the dataset csv file with example labels.'   )
         train_parser.add_argument(
             '--column_text'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1     ,				help='Column of the dataset csv file with example texts.'   )
         train_parser.add_argument(
             '--column_id'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=2     ,				help='Column of the dataset csv file with example ids.'   )
         train_parser.add_argument(
             '--skip_first_row'     ,				action='store_true'     ,				help='Skip the first row of the csv file (headers).'   )

         train_parser.add_argument('--validation_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=''     ,				help='path to validation dataset.'   )
         train_parser.add_argument(
             '--validation_split'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0.1     ,				help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'     ,				)

         train_parser.add_argument('--output'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='./'     ,				help='path to saved the trained model.'   )

         train_parser.add_argument(
             '--task'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='text_classification'     ,				help='Task to train the model on.'   )
         train_parser.add_argument(
             '--model'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='bert-base-uncased'     ,				help='Model\'s name or path to stored model.'   )
         train_parser.add_argument('--train_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3_2     ,				help='Batch size for training.'   )
         train_parser.add_argument('--valid_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=6_4     ,				help='Batch size for validation.'   )
         train_parser.add_argument('--learning_rate'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3e-5     ,				help='Learning rate.'   )
         train_parser.add_argument('--adam_epsilon'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1e-08     ,				help='Epsilon for Adam optimizer.'   )
         train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE   )



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







         """simple docstring"""



         a__		=			logging.get_logger('transformers-cli/training'   )

         a__		=			'tf' if is_tf_available() else 'torch'

         os.makedirs(args.output     ,				exist_ok=__SCREAMING_SNAKE_CASE   )
         a__		=			args.output

         a__		=			args.column_label
         a__		=			args.column_text
         a__		=			args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'   )
         if args.task == "text_classification":
             a__		=			TextClassificationPipeline.from_pretrained(args.model   )
         elif args.task == "token_classification":
             raise NotImplementedError
         elif args.task == "question_answering":
             raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'   )
         a__		=			Processor.create_from_csv(
             args.train_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)
         a__		=			None
         if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'   )
             a__		=			Processor.create_from_csv(
                 args.validation_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)

         a__		=			args.validation_split
         a__		=			args.train_batch_size
         a__		=			args.valid_batch_size
         a__		=			args.learning_rate
         a__		=			args.adam_epsilon



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         if self.framework == "tf":
             return self.run_tf()
         return self.run_torch()



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         raise NotImplementedError




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         self.pipeline.fit(
             self.train_dataset     ,				validation_data=self.valid_dataset     ,				validation_split=self.validation_split     ,				learning_rate=self.learning_rate     ,				adam_epsilon=self.adam_epsilon     ,				train_batch_size=self.train_batch_size     ,				valid_batch_size=self.valid_batch_size     ,				)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output   )


