



"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
		from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
		raise ImportError("You need to install the version of fairseq from the Mega repo!")

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Any,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    int	)->   str:
								super().__init__()
								lowerCamelCase__						:						Tuple						      =mega_args
								lowerCamelCase__						:						List[Any]						      =nn.Embedding(lowerCamelCase,    self.mega_args.encoder_embed_dim	)
								lowerCamelCase__						:						List[Any]						      =nn.ModuleList([MegaEncoderLayer(self.mega_args	) for _ in range(lowerCamelCase	)]	)
								lowerCamelCase__						:						List[Any]						      =depth




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   Union[str, Any]:

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
											lowerCamelCase__						:						Any						      =input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
											lowerCamelCase__						:						Union[str, Any]						      =1 - attention_mask

								# get token embeddings from IDs
								lowerCamelCase__						:						Any						      =self.embedding_layer(lowerCamelCase	)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
											lowerCamelCase__						:						Union[str, Any]						      =encoder(lowerCamelCase,    lowerCamelCase	)

								# return according to the shape specified
								if batch_first:
											# (T, B, H) --> (B, T, H)
											return torch.transpose(lowerCamelCase,    0,    1	)
								else:
											return embeds





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Tuple	)->   Dict:
								super().__init__()
								lowerCamelCase__						:						Dict						      =MegaLM(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =nn.Linear(mega_args.encoder_embed_dim,    lowerCamelCase	)
								lowerCamelCase__						:						List[Any]						      =nn.Dropout(p=0.1	)




					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.mega(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self.mlm_head(self.dropout(lowerCamelCase	)	)






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			with open(os.path.join(__lowerCamelCase				,						'''model_args.pkl'''      )				,						'''rb'''      ) as f:
						lowerCamelCase__						:						Any						      =pkl.load(__lowerCamelCase      )

			# load the original encoder
			lowerCamelCase__						:						Tuple						      =OriginalMegaForMaskedLM(**__lowerCamelCase      ).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''				,						original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''encoder_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)
			print(
			    '''Original Mega MLM layer:'''				,						original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# create a new config from the old one
			lowerCamelCase__						:						Union[str, Any]						      =MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']				,						vocab_size=mega_original_args['''vocab_size''']				,						hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim				,						shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim				,						intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim				,						ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim				,						dropout_prob=mega_original_args['''mega_args'''].dropout				,						attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout				,						hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout				,						activation=mega_original_args['''mega_args'''].activation_fn				,						attention_activation=mega_original_args['''mega_args'''].attention_activation_fn				,						bidirectional=mega_original_args['''mega_args'''].bidirectional				,						use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0				,						chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size				,						truncation=mega_original_args['''mega_args'''].truncation_length				,						normalization_type=mega_original_args['''mega_args'''].normalization_type				,						normalize_before_mega=__lowerCamelCase				,						norm_affine=__lowerCamelCase				,						use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout				,						relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias				,						max_positions=mega_original_args['''mega_args'''].max_source_positions				,						nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim				,						normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before				,						nffn_activation_dropout_prob=0.0				,						add_token_type_embeddings=__lowerCamelCase				,						add_lm_hidden_dense_layer=__lowerCamelCase				,						)

			lowerCamelCase__						:						Union[str, Any]						      =MegaForMaskedLM(__lowerCamelCase      ).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			lowerCamelCase__						:						str						      =original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			lowerCamelCase__						:						int						      =original_mlm.mega.encoders.state_dict()
			lowerCamelCase__						:						List[Any]						      ={}
			for module_name in original_state_dict.keys():
						lowerCamelCase__						:						Union[str, Any]						      =None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												lowerCamelCase__						:						Any						      =module_name.replace('''move.beta'''				,						'''ema_gate.ema_expansion_matrix'''      )
									elif "mega_layer.beta" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''beta'''				,						'''qk_bias'''      )
									else:
												lowerCamelCase__						:						Dict						      =module_name.replace('''beta'''				,						'''b_param'''      )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												lowerCamelCase__						:						List[str]						      =module_name.replace('''move.gamma'''				,						'''ema_gate.kernel_projection_matrix'''      )
									elif "mega_layer.gamma" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''gamma'''				,						'''qk_weight'''      )
									else:
												lowerCamelCase__						:						int						      =module_name.replace('''gamma'''				,						'''g_param'''      )
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									lowerCamelCase__						:						int						      =module_name.replace('''move.alpha'''				,						'''ema_gate.decay_factor'''      )
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									lowerCamelCase__						:						Union[str, Any]						      =module_name.replace('''move.delta'''				,						'''ema_gate.damping_factor'''      )
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									lowerCamelCase__						:						Any						      =module_name.replace('''move.omega'''				,						'''ema_gate.residual_weight'''      )

						if new_module_name:
									lowerCamelCase__						:						Union[str, Any]						      =new_module_name

			if len(__lowerCamelCase      ) != 0:
						print(f'''Renaming these keys: {updated_keys.keys()}'''      )
			else:
						print('''No need to rename state dict entries'''      )
			for old, new in updated_keys.items():
						lowerCamelCase__						:						Optional[int]						      =original_state_dict.pop(__lowerCamelCase      )

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''				,						hf_mlm.mega.layers.load_state_dict(__lowerCamelCase      )      )

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''				,						hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# test on a randomly generated input sequence
			lowerCamelCase__						:						str						      =torch.randint(0				,						hf_config.vocab_size				,						size=(4, 256)      )
			lowerCamelCase__						:						List[Any]						      =torch.ones_like(__lowerCamelCase      )
			# mask a few tokens to make sure masking is applied appropriately :)
			lowerCamelCase__						:						List[str]						      =0

			# run forward passes
			lowerCamelCase__						:						Optional[int]						      =original_mlm(__lowerCamelCase				,						__lowerCamelCase				,						batch_first=__lowerCamelCase				,						ignore_mask_value=0      )
			lowerCamelCase__						:						Optional[int]						      =hf_mlm(__lowerCamelCase				,						__lowerCamelCase      )[0]

			# print shapes and diff
			print(f'''original output {original_output.shape}'''      )
			print(f'''hf output {hf_output.shape}'''      )
			print(f'''max diff: {(original_output - hf_output).max()}'''      )  # 0.0
			lowerCamelCase__						:						List[Any]						      =torch.allclose(__lowerCamelCase				,						__lowerCamelCase				,						atol=1e-3      )

			if success:
						print('''Yay!'''      )
						hf_mlm.save_pretrained(__lowerCamelCase      )
			else:
						raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''      )

			if includes_tokenizer:
						print('''Transferring tokenizer'''      )
						lowerCamelCase__						:						Any						      =AutoTokenizer.from_pretrained(__lowerCamelCase      )
						tokenizer.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
		_lowercase					:					Dict 		=		argparse.ArgumentParser()

		parser.add_argument(
		    "--pretrained_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Point to the directory containing your model weights using the official Mega repo",
		)

		parser.add_argument(
		    "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
		)

		parser.add_argument(
		    "--includes_tokenizer",
		    action="store_true",
		    help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
		)

		_lowercase					:					Dict 		=		parser.parse_args()

		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)








"""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							:       int							,					__UpperCAmelCase							:       argparse.Namespace							,					__UpperCAmelCase							:       Any=None							,					__UpperCAmelCase							:       int="base"							,					__UpperCAmelCase							:       List[str]=None							,					__UpperCAmelCase							:       Optional[int]=None							,					__UpperCAmelCase							:       Dict=None							,					**__UpperCAmelCase							:       Tuple							,					):
      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		:						Optional[Any] 				=     0
      a		:						List[Any] 				=     Path(self.hparams.output_dir)
      a		:						Dict 				=     self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        a		:						int 				=     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		:						Union[str, 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		:						int 				=     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		:						str 				=     MODEL_MODES[mode]
      if model is None:
        a		:						Union[str, Any] 				=     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		:						int 				=     model






    def       __snake_case				(   self							:       List[str]							,					*__UpperCAmelCase							:       List[Any]							,					**__UpperCAmelCase							:       int):
      a		:						List[Any] 				=     self.model_type.from_pretrained(*__UpperCAmelCase							,					**__UpperCAmelCase)






    def       __snake_case				(   self							:       Union[str, Any]):
      a		:						Optional[Any] 				=     arg_to_scheduler[self.hparams.lr_scheduler]
      a		:						int 				=     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       __snake_case				(   self							:       Tuple):
      a		:						Optional[Any] 				=     self.model
      a		:						List[Any] 				=     ["bias", "LayerNorm.weight"]
      a		:						Optional[Any] 				=     [
          {
              "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		:						List[Any] 				=     Adafactor(
            __UpperCAmelCase							,					lr=self.hparams.learning_rate							,					scale_parameter=__UpperCAmelCase							,					relative_step=__UpperCAmelCase)

      else:
        a		:						Union[str, Any] 				=     AdamW(
            __UpperCAmelCase							,					lr=self.hparams.learning_rate							,					eps=self.hparams.adam_epsilon)
      a		:						int 				=     optimizer

      a		:						Dict 				=     self.get_lr_scheduler()

      return [optimizer], [scheduler]






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       List[str]							,					__UpperCAmelCase							:       Tuple):
      return self.validation_step(__UpperCAmelCase							,					__UpperCAmelCase)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       Optional[int]):
      return self.validation_end(__UpperCAmelCase)






    def       __snake_case				(   self							:       str):
      a		:						Union[str, 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       __snake_case				(   self							:       List[str]							,					__UpperCAmelCase							:       Any):
      if stage == "test":
        a		:						Any 				=     len(self.test_dataloader().dataset)
      else:
        a		:						str 				=     self.get_dataloader("train"							,					self.hparams.train_batch_size							,					shuffle=__UpperCAmelCase)
        a		:						Dict 				=     len(self.train_dataloader().dataset)






    def       __snake_case				(   self							:       List[str]							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       bool = False):
      raise NotImplementedError("You must implement this for your task")






    def       __snake_case				(   self							:       Tuple):
      return self.train_loader






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






    def       __snake_case				(   self							:       Optional[Any]):
      return self.get_dataloader("test"							,					self.hparams.eval_batch_size							,					shuffle=__UpperCAmelCase)






    def       __snake_case				(   self							:       Tuple							,					__UpperCAmelCase							:       Tuple):
      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       __snake_case				(   self							:       Optional[int]							,					__UpperCAmelCase							:       Dict[str, Any]):
      a		:						Optional[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       __snake_case				(   __UpperCAmelCase							:       str							,					__UpperCAmelCase							:       int):
      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       __snake_case				(   self							:       int							,					__UpperCAmelCase							:       Optional[int]							,					__UpperCAmelCase							:       Tuple):
      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       __snake_case				(   self							:       List[Any]							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       str):
      a		:						List[str] 				=     trainer.lr_schedulers[0]["scheduler"]
      a		:						Tuple 				=     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
      pl_module.logger.log_metrics(__UpperCAmelCase)






    def       __snake_case				(   self							:       List[Any]							,					__UpperCAmelCase							:       pl.Trainer							,					__UpperCAmelCase							:       pl.LightningModule):
      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       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       pl.Trainer							,					__UpperCAmelCase							:       pl.LightningModule):
      rank_zero_info("***** Test results *****")
      a		:						Tuple 				=     trainer.callback_metrics
      # Log and save results to file
      a		:						List[str] 				=     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 	lowercase     (							A_    ,  A_			)-> None:
  '''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 	lowercase     (							A_    ,  A_    ,  A_=None    ,  A_=True    ,  A_=None    ,  A_=[]    ,  A_=None    ,  A_=None    ,  **A_    ,  )-> str:
  '''simple docstring'''







  pl.seed_everything(args.seed			)

  # init model
  a		:						List[Any] 				=     Path(model.hparams.output_dir			)
  odir.mkdir(exist_ok=A_			)

  # add custom checkpoints
  if checkpoint_callback is None:
    a		:						Union[str, Any] 				=     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		:						Optional[Any] 				=     LoggingCallback()

  a		:						Any 				=     {}

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

  if args.gpus > 1:
    a		:						Optional[Any] 				=     "auto"  # "ddp"
    a		:						List[str] 				=     "ddp"

  a		:						Dict 				=     args.accumulate_grad_batches
  a		:						Tuple 				=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  a		:						List[str] 				=     "auto"

  a		:						Union[str, Any] 				=     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






