"""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
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





      """simple docstring"""






      return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



    """simple docstring"""

    @staticmethod
    def 		snake_case (				lowerCAmelCase    ):






          """simple docstring"""

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

          train_parser.add_argument(
              '--train_data'		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
          train_parser.add_argument(
              '--column_label'		,    type=lowerCAmelCase		,    default=0		,    help='Column of the dataset csv file with example labels.'    )
          train_parser.add_argument(
              '--column_text'		,    type=lowerCAmelCase		,    default=1		,    help='Column of the dataset csv file with example texts.'    )
          train_parser.add_argument(
              '--column_id'		,    type=lowerCAmelCase		,    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=lowerCAmelCase		,    default=''		,    help='path to validation dataset.'    )
          train_parser.add_argument(
              '--validation_split'		,    type=lowerCAmelCase		,    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=lowerCAmelCase		,    default='./'		,    help='path to saved the trained model.'    )

          train_parser.add_argument(
              '--task'		,    type=lowerCAmelCase		,    default='text_classification'		,    help='Task to train the model on.'    )
          train_parser.add_argument(
              '--model'		,    type=lowerCAmelCase		,    default='bert-base-uncased'		,    help='Model\'s name or path to stored model.'    )
          train_parser.add_argument('--train_batch_size'		,    type=lowerCAmelCase		,    default=32		,    help='Batch size for training.'    )
          train_parser.add_argument('--valid_batch_size'		,    type=lowerCAmelCase		,    default=64		,    help='Batch size for validation.'    )
          train_parser.add_argument('--learning_rate'		,    type=lowerCAmelCase		,    default=3E-5		,    help='Learning rate.'    )
          train_parser.add_argument('--adam_epsilon'		,    type=lowerCAmelCase		,    default=1E-08		,    help='Epsilon for Adam optimizer.'    )
          train_parser.set_defaults(func=lowerCAmelCase    )

    def __init__(				self		,    lowerCAmelCase    ):






          """simple docstring"""

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

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

          os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
          snake_case								=			args.output

          snake_case								=			args.column_label
          snake_case								=			args.column_text
          snake_case								=			args.column_id

          self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
          if args.task == "text_classification":
                snake_case								=			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}"""    )
          snake_case								=			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		,    )
          snake_case								=			None
          if args.validation_data:
                self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
                snake_case								=			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		,    )

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

    def 		snake_case (				self    ):






          """simple docstring"""

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

    def 		snake_case (				self    ):






          """simple docstring"""

          raise NotImplementedError

    def 		snake_case (				self    ):






          """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    )



"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
												snake_case								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.floataa ,			device=protein['aatype'].device ,			)
						snake_case								=			protein['aatype'].to(torch.long					)

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			restype_atomaa_mask[protein_aatype]

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, np.ndarray]:





						"""simple docstring"""






						snake_case								=			tree_map(lambda _UpperCamelCase					: torch.tensor(_UpperCamelCase ,			device=batch['aatype'].device					) ,			_UpperCamelCase ,			np.ndarray					)
						snake_case								=			tensor_tree_map(lambda _UpperCamelCase					: np.array(_UpperCamelCase					) ,			make_atomaa_masks(_UpperCamelCase					)					)
						return out


