from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather



@overload
def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	torch.Tensor:
  ...



@overload
def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Tuple[torch.Tensor, torch.Tensor]:
  ...



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Dict:
  __A							:   Optional[Any]	  =				aatype == rc.restype_order["G"]
  __A							:   List[str]	  =				rc.atom_order["CA"]
  __A							:   int	  =				rc.atom_order["CB"]
  __A							:   Any	  =				torch.where(
      is_gly[..., None].expand(*((-1,) * len(is_gly.shape    )) ,     3    ) ,     all_atom_positions[..., ca_idx, :] ,     all_atom_positions[..., cb_idx, :] ,     )

  if all_atom_masks is not None:
    __A							:   Tuple	  =				torch.where(
        _lowercase ,     all_atom_masks[..., ca_idx] ,     all_atom_masks[..., cb_idx] ,     )
    return pseudo_beta, pseudo_beta_mask
  else:
    return pseudo_beta



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	torch.Tensor:
  __A							:   str	  =				batched_gather(
      _lowercase ,     batch["residx_atom37_to_atom14"] ,     dim=-2 ,     no_batch_dims=len(atomaa.shape[:-2]    ) ,     )

  __A							:   List[str]	  =				atomaa_data * batch["atom37_atom_exists"][..., None]

  return atomaa_data



def    lowerCamelCase_   (  _lowercase    )							->	torch.Tensor:
  __A							:   Any	  =				template_feats["template_aatype"]
  __A							:   int	  =				template_feats["template_torsion_angles_sin_cos"]
  __A							:   Tuple	  =				template_feats["template_alt_torsion_angles_sin_cos"]
  __A							:   str	  =				template_feats["template_torsion_angles_mask"]
  __A							:   Optional[Any]	  =				torch.cat(
      [
          nn.functional.one_hot(_lowercase ,     22    ),
          torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2] ,     14    ),
          alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2] ,     14    ),
          torsion_angles_mask,
      ] ,     dim=-1 ,     )

  return template_angle_feat



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = False ,     _lowercase = 1E-20 ,     _lowercase = 1E8 ,     )							->	torch.Tensor:
  __A							:   Optional[Any]	  =				batch["template_pseudo_beta_mask"]
  __A							:   Optional[Any]	  =				template_mask[..., None] * template_mask[..., None, :]

  # Compute distogram (this seems to differ slightly from Alg. 5)
  __A							:   Tuple	  =				batch["template_pseudo_beta"]
  __A							:   List[Any]	  =				torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2 ,     dim=-1 ,     keepdim=_lowercase    )
  __A							:   List[str]	  =				torch.linspace(_lowercase ,     _lowercase ,     _lowercase ,     device=tpb.device    ) ** 2
  __A							:   Dict	  =				torch.cat([lower[1:], lower.new_tensor([inf]    )] ,     dim=-1    )
  __A							:   Union[str, Any]	  =				((dgram > lower) * (dgram < upper)).type(dgram.dtype    )

  __A							:   Tuple	  =				[dgram, template_mask_ad[..., None]]

  __A							:   torch.LongTensor	  =				nn.functional.one_hot(
      batch["template_aatype"] ,     rc.restype_num + 2 ,     )

  __A							:   Optional[int]	  =				batch["template_aatype"].shape[-1]
  to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2] ,     _lowercase ,     -1 ,     -1    )    )
  to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2] ,     -1 ,     _lowercase ,     -1    )    )

  __A							:   Tuple	  =				[rc.atom_order[a] for a in ["N", "CA", "C"]]
  __A							:   int	  =				Rigid.make_transform_from_reference(
      n_xyz=batch["template_all_atom_positions"][..., n, :] ,     ca_xyz=batch["template_all_atom_positions"][..., ca, :] ,     c_xyz=batch["template_all_atom_positions"][..., c, :] ,     eps=_lowercase ,     )
  __A							:   List[Any]	  =				rigids.get_trans()[..., None, :, :]
  __A							:   Dict	  =				rigids[..., None].invert_apply(_lowercase    )

  __A							:   Dict	  =				torch.rsqrt(eps + torch.sum(rigid_vec**2 ,     dim=-1    )    )

  __A							:   int	  =				batch["template_all_atom_mask"]
  __A							:   List[Any]	  =				t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
  __A							:   Optional[int]	  =				template_mask[..., None] * template_mask[..., None, :]

  __A							:   Optional[Any]	  =				inv_distance_scalar * template_mask_ad
  __A							:   int	  =				rigid_vec * inv_distance_scalar[..., None]

  if not use_unit_vector:
    __A							:   Tuple	  =				unit_vector * 0.0

  to_concat.extend(torch.unbind(unit_vector[..., None, :] ,     dim=-1    )    )
  to_concat.append(template_mask_ad[..., None]    )

  __A							:   Tuple	  =				torch.cat(_lowercase ,     dim=-1    )
  __A							:   Tuple	  =				act * template_mask_ad[..., None]

  return act



def    lowerCamelCase_   (  _lowercase    )							->	torch.Tensor:
  __A							:   torch.LongTensor	  =				nn.functional.one_hot(batch["extra_msa"] ,     23    )
  __A							:   Union[str, Any]	  =				[
      msa_ahot,
      batch["extra_has_deletion"].unsqueeze(-1    ),
      batch["extra_deletion_value"].unsqueeze(-1    ),
  ]
  return torch.cat(_lowercase ,     dim=-1    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     )							->	Rigid:
  # [*, N, 8, 4, 4]
  __A							:   Optional[int]	  =				rrgdf[aatype, ...]

  # [*, N, 8] transformations, i.e.
  #   One [*, N, 8, 3, 3] rotation matrix and
  #   One [*, N, 8, 3]    translation matrix
  __A							:   Dict	  =				r.from_tensor_axa(_lowercase    )

  __A							:   Dict	  =				alpha.new_zeros((*((1,) * len(alpha.shape[:-1]    )), 2)    )
  __A							:   str	  =				1

  # [*, N, 8, 2]
  __A							:   Optional[Any]	  =				torch.cat([bb_rot.expand(*alpha.shape[:-2] ,     -1 ,     -1    ), alpha] ,     dim=-2    )

  # [*, N, 8, 3, 3]
  # Produces rotation matrices of the form:
  # [
  #   [1, 0  , 0  ],
  #   [0, a_2,-a_1],
  #   [0, a_1, a_2]
  # ]
  # This follows the original code rather than the supplement, which uses
  # different indices.

  __A							:   str	  =				alpha.new_zeros(default_r.get_rots().get_rot_mats().shape    )
  __A							:   Optional[Any]	  =				1
  __A							:   Dict	  =				alpha[..., 1]
  __A							:   int	  =				-alpha[..., 0]
  __A							:   List[Any]	  =				alpha

  __A							:   Any	  =				default_r.compose(Rigid(Rotation(rot_mats=_lowercase    ) ,     _lowercase    )    )

  __A							:   Optional[Any]	  =				all_frames[..., 5]
  __A							:   Optional[Any]	  =				all_frames[..., 6]
  __A							:   List[Any]	  =				all_frames[..., 7]

  __A							:   Optional[Any]	  =				all_frames[..., 4]
  __A							:   int	  =				chia_frame_to_bb.compose(_lowercase    )
  __A							:   Optional[Any]	  =				chia_frame_to_bb.compose(_lowercase    )
  __A							:   int	  =				chia_frame_to_bb.compose(_lowercase    )

  __A							:   List[Any]	  =				Rigid.cat(
      [
          all_frames[..., :5],
          chia_frame_to_bb.unsqueeze(-1    ),
          chia_frame_to_bb.unsqueeze(-1    ),
          chia_frame_to_bb.unsqueeze(-1    ),
      ] ,     dim=-1 ,     )

  __A							:   int	  =				r[..., None].compose(_lowercase    )

  return all_frames_to_global





def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     )							->	torch.Tensor:
  # [*, N, 14]
  __A							:   str	  =				group_idx[aatype, ...]

  # [*, N, 14, 8]
  __A							:   torch.LongTensor	  =				nn.functional.one_hot(
      _lowercase ,     num_classes=default_frames.shape[-3] ,     )

  # [*, N, 14, 8]
  __A							:   Tuple	  =				r[..., None, :] * group_mask_one_hot

  # [*, N, 14]
  __A							:   Union[str, Any]	  =				t_atoms_to_global.map_tensor_fn(lambda _lowercase    : torch.sum(_lowercase ,     dim=-1    )    )

  # [*, N, 14, 1]
  __A							:   Union[str, Any]	  =				atom_mask[aatype, ...].unsqueeze(-1    )

  # [*, N, 14, 3]
  __A							:   Optional[Any]	  =				lit_positions[aatype, ...]
  __A							:   Union[str, Any]	  =				t_atoms_to_global.apply(_lowercase    )
  __A							:   Union[str, Any]	  =				pred_positions * atom_mask

  return pred_positions






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


UpperCamelCase      			=     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase      			=     {
    '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
UpperCamelCase      			=     {
    '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
}
UpperCamelCase      			=     sorted(arg_to_scheduler.keys())
UpperCamelCase      			=     '{' + ', '.join(arg_to_scheduler_choices) + '}'


class _a       (	pl.LightningModule ):

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=None							,			__UpperCAmelCase="base"							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			**__UpperCAmelCase							,			):
			super().__init__()
			# TODO: move to self.save_hyperparameters()
			# self.save_hyperparameters()
			# can also expand arguments into trainer signature for easier reading

			self.save_hyperparameters(__UpperCAmelCase   )
			__A							:   str	  =				0
			__A							:   Dict	  =				Path(self.hparams.output_dir   )
			__A							:   Any	  =				self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					__A							:   Union[str, Any]	  =				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=__UpperCAmelCase							,			**__UpperCAmelCase							,			)
			else:
					__A							:   PretrainedConfig	  =				config

			__A							:   Any	  =				("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
			for p in extra_model_params:
					if getattr(self.hparams							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
							assert hasattr(self.config							,			__UpperCAmelCase   ), F"model config doesn't have a `{p}` attribute"
							setattr(self.config							,			__UpperCAmelCase							,			getattr(self.hparams							,			__UpperCAmelCase   )   )

			if tokenizer is None:
					__A							:   List[Any]	  =				AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,			cache_dir=__UpperCAmelCase							,			)
			else:
					__A							:   PreTrainedTokenizer	  =				tokenizer
			__A							:   Union[str, Any]	  =				MODEL_MODES[mode]
			if model is None:
					__A							:   Tuple	  =				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=__UpperCAmelCase							,			)
			else:
					__A							:   Tuple	  =				model

	def 					__UpperCAmelCase(       self							,			*__UpperCAmelCase							,			**__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				self.model_type.from_pretrained(*__UpperCAmelCase							,			**__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				arg_to_scheduler[self.hparams.lr_scheduler]
			__A							:   Union[str, Any]	  =				get_schedule_func(
			    self.opt							,			num_warmup_steps=self.hparams.warmup_steps							,			num_training_steps=self.total_steps()   )
			__A							:   Tuple	  =				{"scheduler": scheduler, "interval": "step", "frequency": 1}
			return scheduler

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				self.model
			__A							:   Optional[int]	  =				["bias", "LayerNorm.weight"]
			__A							:   str	  =				[
			    {
			        "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							:   Optional[int]	  =				Adafactor(
					    __UpperCAmelCase							,			lr=self.hparams.learning_rate							,			scale_parameter=__UpperCAmelCase							,			relative_step=__UpperCAmelCase   )

			else:
					__A							:   Tuple	  =				AdamW(
					    __UpperCAmelCase							,			lr=self.hparams.learning_rate							,			eps=self.hparams.adam_epsilon   )
			__A							:   Optional[Any]	  =				optimizer

			__A							:   str	  =				self.get_lr_scheduler()

			return [optimizer], [scheduler]

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			return self.validation_step(__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			return self.validation_end(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				max(1							,			self.hparams.gpus   )  # TODO: consider num_tpu_cores
			__A							:   Optional[int]	  =				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
			return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			if mode == "test":
					__A							:   List[str]	  =				len(self.test_dataloader().dataset   )
			else:
					__A							:   List[str]	  =				self.get_dataloader("train"							,			self.hparams.train_batch_size							,			shuffle=__UpperCAmelCase   )
					__A							:   str	  =				len(self.train_dataloader().dataset   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase = False   ):
			raise NotImplementedError("You must implement this for your task"   )

	def 					__UpperCAmelCase(       self   ):
			return self.train_loader

	def 					__UpperCAmelCase(       self   ):
			return self.get_dataloader("dev"							,			self.hparams.eval_batch_size							,			shuffle=__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			return self.get_dataloader("test"							,			self.hparams.eval_batch_size							,			shuffle=__UpperCAmelCase   )

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

	@pl.utilities.rank_zero_only
	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				self.output_dir.joinpath("best_tfmr"   )
			__A							:   Union[str, Any]	  =				self.step_count
			self.model.save_pretrained(__UpperCAmelCase   )
			self.tokenizer.save_pretrained(__UpperCAmelCase   )







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


class _a       (	pl.Callback ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				trainer.lr_schedulers[0]["scheduler"]
			__A							:   Optional[Any]	  =				{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
			pl_module.logger.log_metrics(__UpperCAmelCase   )

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







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



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	None:
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    "--output_dir" ,     default=_lowercase ,     type=_lowercase ,     required=_lowercase ,     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=_lowercase ,     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=_lowercase    )
		parser.add_argument("--max_grad_norm" ,     dest="gradient_clip_val" ,     default=1.0 ,     type=_lowercase ,     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=_lowercase ,     default=1 ,     help="Number of updates steps to accumulate before performing a backward/update pass." ,     )
		parser.add_argument("--seed" ,     type=_lowercase ,     default=42 ,     help="random seed for initialization"    )
		parser.add_argument(
		    "--data_dir" ,     default=_lowercase ,     type=_lowercase ,     required=_lowercase ,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,     )





def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase=None ,     _lowercase=True ,     _lowercase=[] ,     _lowercase=None ,     _lowercase=None ,     **_lowercase ,     )							->	Union[str, Any]:
		pl.seed_everything(args.seed    )

		# init model
		__A							:   int	  =				Path(model.hparams.output_dir    )
		odir.mkdir(exist_ok=_lowercase    )

		# add custom checkpoints
		if checkpoint_callback is None:
				__A							:   Optional[int]	  =				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(_lowercase    )
		if logging_callback is None:
				__A							:   int	  =				LoggingCallback()

		__A							:   List[str]	  =				{}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				__A							:   Optional[int]	  =				16
				__A							:   Any	  =				args.fpaa_opt_level

		if args.gpus > 1:
				__A							:   int	  =				"ddp"

		__A							:   int	  =				args.accumulate_grad_batches
		__A							:   Dict	  =				extra_train_kwargs.get("accelerator" ,     _lowercase    )
		__A							:   Optional[int]	  =				extra_train_kwargs.get("profiler" ,     _lowercase    )

		__A							:   List[str]	  =				pl.Trainer.from_argparse_args(
		    _lowercase ,     weights_summary=_lowercase ,     callbacks=[logging_callback] + extra_callbacks ,     logger=_lowercase ,     checkpoint_callback=_lowercase ,     **_lowercase ,     )

		if args.do_train:
				trainer.fit(_lowercase    )

		return trainer





