






'''simple docstring'''




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       filter(lambda __magic_name__						: p.requires_grad     ,model.parameters()						)
			snake_case_				:					List[str]   =       sum([np.prod(p.size()						) for p in model_parameters]						)
			return params


__lowerCamelCase   :					Optional[Any]   							=					logging.getLogger(__name__)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			if metric == "rouge2":
						snake_case_				:					List[str]   =       "{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						snake_case_				:					str   =       "{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						snake_case_				:					str   =       "{val_avg_em:.4f}-{step_count}"
			elif metric == "loss":
						snake_case_				:					Any   =       "{val_avg_loss:.4f}-{step_count}"
			else:
						raise NotImplementedError(
						    F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    " function."						)

			snake_case_				:					Optional[Any]   =       ModelCheckpoint(
			    dirpath=__magic_name__     ,filename=__magic_name__     ,monitor=F'''val_{metric}'''     ,mode="max"     ,save_top_k=1     ,every_n_epochs=1     ,)

			return checkpoint_callback

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



			return EarlyStopping(
			    monitor=F'''val_{metric}'''     ,mode="min" if "loss" in metric else "max"     ,patience=__magic_name__     ,verbose=__magic_name__     ,)







class    A_	(pl.Callback							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Any				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       {F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
										pl_module.logger.log_metrics(lowerCAmelCase__				)

							@rank_zero_only
							def 							_A	(		self			:int       ,							lowerCAmelCase__			:pl.Trainer       ,							lowerCAmelCase__			:pl.LightningModule       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Dict=True				)				->  None:


										'''simple docstring'''




										logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''				)
										snake_case_				:					List[Any]   =       trainer.callback_metrics
										trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}				)
										# Log results
										snake_case_				:					Tuple   =       Path(pl_module.hparams.output_dir				)
										if type_path == "test":
													snake_case_				:					str   =       od / "test_results.txt"
													snake_case_				:					List[str]   =       od / "test_generations.txt"
										else:
													# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
													# If people want this it will be easy enough to add back.
													snake_case_				:					Dict   =       od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
													snake_case_				:					Optional[int]   =       od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
													results_file.parent.mkdir(exist_ok=lowerCAmelCase__				)
													generations_file.parent.mkdir(exist_ok=lowerCAmelCase__				)
										with open(lowerCAmelCase__       ,							"a+"				) as writer:
													for key in sorted(lowerCAmelCase__				):
																if key in ["log", "progress_bar", "preds"]:
																			continue
																snake_case_				:					Dict   =       metrics[key]
																if isinstance(lowerCAmelCase__       ,							torch.Tensor				):
																			snake_case_				:					str   =       val.item()
																snake_case_				:					Optional[int]   =       F'''{key}: {val:.6f}\n'''
																writer.write(lowerCAmelCase__				)

										if not save_generations:
													return

										if "preds" in metrics:
													snake_case_				:					str   =       "\n".join(metrics["preds"]				)
													generations_file.open("w+"				).write(lowerCAmelCase__				)

							@rank_zero_only
							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										try:
													snake_case_				:					Dict   =       pl_module.model.model.num_parameters()
										except AttributeError:
													snake_case_				:					Dict   =       pl_module.model.num_parameters()

										snake_case_				:					Optional[Any]   =       count_trainable_parameters(lowerCAmelCase__				)
										# mp stands for million parameters
										trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6}				)

							@rank_zero_only
							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:pl.Trainer       ,							lowerCAmelCase__			:pl.LightningModule				)				->  int:


										'''simple docstring'''




										save_json(pl_module.metrics       ,							pl_module.metrics_save_path				)
										return self._write_logs(lowerCAmelCase__       ,							lowerCAmelCase__       ,							"test"				)






							@rank_zero_only
							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:pl.Trainer       ,							lowerCAmelCase__			:Any				)				->  Tuple:


										'''simple docstring'''




										save_json(pl_module.metrics       ,							pl_module.metrics_save_path				)
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")












'''simple docstring'''




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
		raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Union[str, Any]   							=					'''Hello world! cécé herlolip'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       FairseqRobertaModel.from_pretrained(__magic_name__						)
			roberta.eval()  # disable dropout
			snake_case_				:					Dict   =       roberta.model.encoder.sentence_encoder
			snake_case_				:					List[str]   =       XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,hidden_size=roberta.cfg.model.encoder_embed_dim     ,num_hidden_layers=roberta.cfg.model.encoder_layers     ,num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,max_position_embeddings=514     ,type_vocab_size=1     ,layer_norm_eps=1E-5     ,)
			if classification_head:
						snake_case_				:					List[str]   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"     ,__magic_name__						)

			snake_case_				:					List[str]   =       XLMRobertaXLForSequenceClassification(__magic_name__						) if classification_head else XLMRobertaXLForMaskedLM(__magic_name__						)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			snake_case_				:					List[Any]   =       roberta_sent_encoder.embed_tokens.weight
			snake_case_				:					int   =       roberta_sent_encoder.embed_positions.weight
			snake_case_				:					Union[str, Any]   =       torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

			snake_case_				:					Union[str, Any]   =       roberta_sent_encoder.layer_norm.weight
			snake_case_				:					str   =       roberta_sent_encoder.layer_norm.bias

			for i in range(config.num_hidden_layers						):
						# Encoder: start of layer
						snake_case_				:					BertLayer   =       model.roberta.encoder.layer[i]
						snake_case_				:					TransformerSentenceEncoderLayer   =       roberta_sent_encoder.layers[i]

						snake_case_				:					RobertaAttention   =       layer.attention
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.weight
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.bias

						# self attention
						snake_case_				:					BertSelfAttention   =       layer.attention.self
						assert (
						    roberta_layer.self_attn.k_proj.weight.data.shape
						    == roberta_layer.self_attn.q_proj.weight.data.shape
						    == roberta_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)						)
						)

						snake_case_				:					Dict   =       roberta_layer.self_attn.q_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.q_proj.bias
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.weight
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.bias
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.v_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.v_proj.bias

						# self-attention output
						snake_case_				:					BertSelfOutput   =       layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.self_attn.out_proj.weight
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						snake_case_				:					int   =       roberta_layer.final_layer_norm.weight
						snake_case_				:					Union[str, Any]   =       roberta_layer.final_layer_norm.bias

						# intermediate
						snake_case_				:					BertIntermediate   =       layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.fca.weight
						snake_case_				:					List[Any]   =       roberta_layer.fca.bias

						# output
						snake_case_				:					BertOutput   =       layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					Any   =       roberta_layer.fca.weight
						snake_case_				:					Any   =       roberta_layer.fca.bias
						# end of layer

			if classification_head:
						snake_case_				:					int   =       roberta.model.classification_heads["mnli"].dense.weight
						snake_case_				:					Union[str, Any]   =       roberta.model.classification_heads["mnli"].dense.bias
						snake_case_				:					Tuple   =       roberta.model.classification_heads["mnli"].out_proj.weight
						snake_case_				:					str   =       roberta.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.dense.weight
						snake_case_				:					int   =       roberta.model.encoder.lm_head.dense.bias
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.layer_norm.weight
						snake_case_				:					Optional[int]   =       roberta.model.encoder.lm_head.layer_norm.bias
						snake_case_				:					int   =       roberta.model.encoder.lm_head.weight
						snake_case_				:					List[str]   =       roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			snake_case_				:					torch.Tensor   =       roberta.encode(__magic_name__						).unsqueeze(0						)  # batch of size 1

			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)[0]
			if classification_head:
						snake_case_				:					Optional[Any]   =       roberta.model.classification_heads["mnli"](roberta.extract_features(__magic_name__						)						)
			else:
						snake_case_				:					List[str]   =       roberta.model(__magic_name__						)[0]
			print(our_output.shape     ,their_output.shape						)
			snake_case_				:					str   =       torch.max(torch.abs(our_output - their_output						)						).item()
			print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
			snake_case_				:					Any   =       torch.allclose(__magic_name__     ,__magic_name__     ,atol=1E-3						)
			print("Do both models output the same tensors?"     ,"🔥" if success else "💩"						)
			if not success:
						raise Exception("Something went wRoNg"						)

			pathlib.Path(__magic_name__						).mkdir(parents=__magic_name__     ,exist_ok=__magic_name__						)
			print(F'''Saving model to {pytorch_dump_folder_path}'''						)
			model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--roberta_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.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		__lowerCamelCase   :					Tuple   							=					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




