"""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


_A   					=    logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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

class lowerCamelCase		(pl.LightningModule      ):






					'''simple docstring'''


					def __init__(						self			:				List[Any]   , _snake_case			:				argparse.Namespace   , _snake_case			:				Tuple=None   , _snake_case			:				Optional[int]="base"   , _snake_case			:				List[Any]=None   , _snake_case			:				Tuple=None   , _snake_case			:				List[str]=None   , **_snake_case			:				Dict   , )							->       Optional[int]:
									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				0
									SCREAMING_SNAKE_CASE__		  =				Path(self.hparams.output_dir		)
									SCREAMING_SNAKE_CASE__		  =				self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
													SCREAMING_SNAKE_CASE__		  =				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=_snake_case   , **_snake_case   , )
									else:
													SCREAMING_SNAKE_CASE__		  =				config

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

									if tokenizer is None:
													SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   , cache_dir=_snake_case   , )
									else:
													SCREAMING_SNAKE_CASE__		  =				tokenizer
									SCREAMING_SNAKE_CASE__		  =				MODEL_MODES[mode]
									if model is None:
													SCREAMING_SNAKE_CASE__		  =				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=_snake_case   , )
									else:
													SCREAMING_SNAKE_CASE__		  =				model



					def 			lowerCAmelCase_					(						self			:				Dict   , *_snake_case			:				Dict   , **_snake_case			:				Dict		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				self.model_type.from_pretrained(*_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				arg_to_scheduler[self.hparams.lr_scheduler]
									SCREAMING_SNAKE_CASE__		  =				get_schedule_func(
									    self.opt   , num_warmup_steps=self.hparams.warmup_steps   , num_training_steps=self.total_steps()		)
									SCREAMING_SNAKE_CASE__		  =				{"scheduler": scheduler, "interval": "step", "frequency": 1}
									return scheduler



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model
									SCREAMING_SNAKE_CASE__		  =				["bias", "LayerNorm.weight"]
									SCREAMING_SNAKE_CASE__		  =				[
									    {
									        "params": [
									            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)
									        ],  # check this named paramters
									        "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:
													SCREAMING_SNAKE_CASE__		  =				Adafactor(
													    _snake_case   , lr=self.hparams.learning_rate   , scale_parameter=_snake_case   , relative_step=_snake_case		)

									else:
													SCREAMING_SNAKE_CASE__		  =				AdamW(
													    _snake_case   , lr=self.hparams.learning_rate   , eps=self.hparams.adam_epsilon		)
									SCREAMING_SNAKE_CASE__		  =				optimizer

									SCREAMING_SNAKE_CASE__		  =				self.get_lr_scheduler()

									return [optimizer], [scheduler]



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				int   , _snake_case			:				str		)							->       Union[str, Any]:
									return self.validation_step(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Any		)							->       Union[str, Any]:
									return self.validation_end(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				max(1   , self.hparams.gpus		)  # TODO: consider num_tpu_cores
									SCREAMING_SNAKE_CASE__		  =				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Tuple		)							->       int:
									if stage == "test":
													SCREAMING_SNAKE_CASE__		  =				len(self.test_dataloader().dataset		)
									else:
													SCREAMING_SNAKE_CASE__		  =				self.get_dataloader("train"   , self.hparams.train_batch_size   , shuffle=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				len(self.train_dataloader().dataset		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				str   , _snake_case			:				int   , _snake_case			:				bool = False		)							->       List[Any]:
									raise NotImplementedError("You must implement this for your task"		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									return self.train_loader



					def 			lowerCAmelCase_					(						self			:				int		)							->       List[str]:
									return self.get_dataloader("dev"   , self.hparams.eval_batch_size   , shuffle=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       int:
									return self.get_dataloader("test"   , self.hparams.eval_batch_size   , shuffle=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Tuple		)							->       int:
									return os.path.join(
									    self.hparams.data_dir   , "cached_{}_{}_{}".format(
									        _snake_case   , list(filter(_snake_case   , self.hparams.model_name_or_path.split("/"		)		)		).pop()   , str(self.hparams.max_seq_length		)   , )   , )



					@pl.utilities.rank_zero_only
					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict[str, Any]		)							->       None:
									SCREAMING_SNAKE_CASE__		  =				self.output_dir.joinpath("best_tfmr"		)
									SCREAMING_SNAKE_CASE__		  =				self.step_count
									self.model.save_pretrained(_snake_case		)
									self.tokenizer.save_pretrained(_snake_case		)



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



class lowerCamelCase		(pl.Callback      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]		)							->       str:
									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 lowerCamelCase		(pl.Callback      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				str		)							->       Optional[Any]:
									# print(pl_module.model.rag)
									for name, param in pl_module.model.rag.named_parameters():
													if param.grad is None:
																	print(_snake_case		)



class lowerCamelCase		(pl.Callback      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				str   , _snake_case			:				List[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				trainer.lr_schedulers[0]["scheduler"]
									SCREAMING_SNAKE_CASE__		  =				{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
									pl_module.logger.log_metrics(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				pl.Trainer   , _snake_case			:				pl.LightningModule		)							->       Union[str, Any]:
									rank_zero_info("***** Validation results *****"		)
									SCREAMING_SNAKE_CASE__		  =				trainer.callback_metrics
									# Log results
									for key in sorted(_snake_case		):
													if key not in ["log", "progress_bar"]:
																	rank_zero_info("{} = {}\n".format(_snake_case   , str(metrics[key]		)		)		)



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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       ):
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    "--output_dir"  ,					default=str(Path(__UpperCAmelCase       ).parent / "test_run" / "model_checkpoints"       )  ,					type=__UpperCAmelCase  ,					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=__UpperCAmelCase  ,					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=__UpperCAmelCase       )
				parser.add_argument("--max_grad_norm"  ,					dest="gradient_clip_val"  ,					default=1.0  ,					type=__UpperCAmelCase  ,					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=__UpperCAmelCase  ,					default=1  ,					help="Number of updates steps to accumulate before performing a backward/update pass."  ,					)
				parser.add_argument("--seed"  ,					type=__UpperCAmelCase  ,					default=42  ,					help="random seed for initialization"       )
				parser.add_argument(
				    "--data_dir"  ,					default=str(Path(__UpperCAmelCase       ).parent / "test_run" / "dummy-train-data"       )  ,					type=__UpperCAmelCase  ,					help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."  ,					)




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=[]  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase  ,					):
				pl.seed_everything(args.seed       )

				# init model
				SCREAMING_SNAKE_CASE__		  =				Path(model.hparams.output_dir       )
				odir.mkdir(exist_ok=__UpperCAmelCase       )

				# add custom checkpoints
				if checkpoint_callback is None:
								SCREAMING_SNAKE_CASE__		  =				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(__UpperCAmelCase       )
				if logging_callback is None:
								SCREAMING_SNAKE_CASE__		  =				LoggingCallback()

				SCREAMING_SNAKE_CASE__		  =				{}

				if args.fpaa:
								SCREAMING_SNAKE_CASE__		  =				16

				if args.gpus > 1:
								SCREAMING_SNAKE_CASE__		  =				"auto"
								SCREAMING_SNAKE_CASE__		  =				"ddp"

				SCREAMING_SNAKE_CASE__		  =				args.accumulate_grad_batches
				SCREAMING_SNAKE_CASE__		  =				None
				SCREAMING_SNAKE_CASE__		  =				"auto"

				SCREAMING_SNAKE_CASE__		  =				pl.Trainer.from_argparse_args(
				    __UpperCAmelCase  ,					weights_summary=__UpperCAmelCase  ,					callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]  ,					logger=__UpperCAmelCase  ,					val_check_interval=1  ,					num_sanity_val_steps=2  ,					**__UpperCAmelCase  ,					)

				if args.do_train:
								trainer.fit(__UpperCAmelCase       )

				else:
								print("RAG modeling tests with new set functions successfuly executed!"       )
				return trainer





"""simple docstring"""






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_A   					=    logging.get_logger(__name__)
logging.set_verbosity_info()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				["key_proj", "value_proj", "query_proj"]

				SCREAMING_SNAKE_CASE__		  =				{
				    "self_attn": "ngram_self_attn",
				    "cross_attn": "encoder_attn",
				    "cross_attn_layer_norm": "encoder_attn_layer_norm",
				    "feed_forward_layer_norm": "final_layer_norm",
				    "feed_forward": "",
				    "intermediate": "fc1",
				    "output": "fc2",
				    "key_proj": "k_proj",
				    "query_proj": "q_proj",
				    "value_proj": "v_proj",
				    "word_embeddings": "embed_tokens",
				    "embeddings_layer_norm": "emb_layer_norm",
				    "relative_pos_embeddings": "relative_linear",
				    "ngram_embeddings": "ngram_input_embed",
				    "position_embeddings": "embed_positions",
				}

				for key in loading_info["missing_keys"]:
								SCREAMING_SNAKE_CASE__		  =				key.split("."       )

								if attributes[0] == "lm_head":
												SCREAMING_SNAKE_CASE__		  =				prophet
												SCREAMING_SNAKE_CASE__		  =				prophet_old
								else:
												SCREAMING_SNAKE_CASE__		  =				prophet.prophetnet
												SCREAMING_SNAKE_CASE__		  =				prophet_old.model

								SCREAMING_SNAKE_CASE__		  =				False
								for attribute in attributes:
												if attribute in mapping:
																SCREAMING_SNAKE_CASE__		  =				mapping[attribute]
																if not hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ) and len(__UpperCAmelCase       ) > 0:
																				SCREAMING_SNAKE_CASE__		  =				attribute
												elif hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																SCREAMING_SNAKE_CASE__		  =				old_model.weight
																logger.info(F"""{attribute} is initialized."""       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																SCREAMING_SNAKE_CASE__		  =				old_model.bias
																logger.info(F"""{attribute} is initialized"""       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute in special_keys and hasattr(__UpperCAmelCase  ,					"in_proj_weight"       ):
																SCREAMING_SNAKE_CASE__		  =				old_model.in_proj_weight.shape[0] // 3
																SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																elif attribute == "key_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																elif attribute == "value_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																SCREAMING_SNAKE_CASE__		  =				True
																break

												if attribute.isdigit():
																SCREAMING_SNAKE_CASE__		  =				model[int(__UpperCAmelCase       )]
																SCREAMING_SNAKE_CASE__		  =				old_model[int(__UpperCAmelCase       )]
												else:
																SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

																if old_attribute == "":
																				SCREAMING_SNAKE_CASE__		  =				old_model
																else:
																				if not hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																								raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																				SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

								if not is_key_init:
												raise ValueError(F"""{key} was not correctly initialized!"""       )

				print(F"""Saving model to {pytorch_dump_folder_path}"""       )
				prophet.save_pretrained(__UpperCAmelCase       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						_A   					=    parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




