


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
       raise Exception('''requires fairseq >= 0.9.0''')


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

__lowerCamelCase   	=  '''Hello world! cécé herlolip'''



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  Dict				      =     os.path.split(__UpperCamelCase			)
     a_     :  str				      =     DataaVecTextModel.from_pretrained(
         __UpperCamelCase	,	checkpoint_file=__UpperCamelCase			)
     dataavec.eval()  # disable dropout
     a_     :  Tuple				      =     dataavec.models[0]
     a_     :  str				      =     dataavec_model.encoder.sentence_encoder
     a_     :  List[Any]				      =     DataaVecTextConfig(
         vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings	,	hidden_size=dataavec_model.args.encoder_embed_dim	,	num_hidden_layers=dataavec_model.args.encoder_layers	,	num_attention_heads=dataavec_model.args.encoder_attention_heads	,	intermediate_size=dataavec_model.args.encoder_ffn_embed_dim	,	max_position_embeddings=5_1_4	,	type_vocab_size=1	,	layer_norm_eps=1E-5	,	)
     if classification_head:
          a_     :  Any				      =     dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our BERT config:"""	,	__UpperCamelCase			)

     a_     :  Union[str, Any]				      =     DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     a_     :  List[str]				      =     dataavec_sent_encoder.embed_tokens.weight
     a_     :  Dict				      =     dataavec_sent_encoder.embed_positions.weight
     a_     :  Optional[Any]				      =     torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
     a_     :  Tuple				      =     dataavec_sent_encoder.layernorm_embedding.weight
     a_     :  Any				      =     dataavec_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers			):
          # Encoder: start of layer
          a_     :  BertLayer				      =     model.dataavec_text.encoder.layer[i]
          a_     :  TransformerSentenceEncoderLayer				      =     dataavec_sent_encoder.layers[i]

          # self attention
          a_     :  BertSelfAttention				      =     layer.attention.self
          assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)			), (
              "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
          )
          assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)			), (
              "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
          )
          assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)			), (
              "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
          )

          a_     :  str				      =     dataavec_layer.self_attn.q_proj.weight
          a_     :  Optional[int]				      =     dataavec_layer.self_attn.q_proj.bias
          a_     :  Optional[int]				      =     dataavec_layer.self_attn.k_proj.weight
          a_     :  int				      =     dataavec_layer.self_attn.k_proj.bias
          a_     :  Any				      =     dataavec_layer.self_attn.v_proj.weight
          a_     :  List[str]				      =     dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          a_     :  BertSelfOutput				      =     layer.attention.output
          assert (
              self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
          ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
          a_     :  List[Any]				      =     dataavec_layer.self_attn.out_proj.weight
          a_     :  List[str]				      =     dataavec_layer.self_attn.out_proj.bias
          a_     :  str				      =     dataavec_layer.self_attn_layer_norm.weight
          a_     :  int				      =     dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          a_     :  BertIntermediate				      =     layer.intermediate
          assert (
              intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          a_     :  Union[str, Any]				      =     dataavec_layer.fca.weight
          a_     :  List[str]				      =     dataavec_layer.fca.bias

          # output
          a_     :  BertOutput				      =     layer.output
          assert (
              bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          a_     :  Optional[Any]				      =     dataavec_layer.fca.weight
          a_     :  int				      =     dataavec_layer.fca.bias
          a_     :  Dict				      =     dataavec_layer.final_layer_norm.weight
          a_     :  List[Any]				      =     dataavec_layer.final_layer_norm.bias
          # end of layer

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

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

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

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


if __name__ == "__main__":
       __lowerCamelCase   	=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--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   	=  parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )




"""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 import save_json


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     int      					=				filter(lambda __UpperCAmelCase      : p.requires_grad  ,model.parameters()      )
	_lowercase			:     List[str]      					=				sum([np.prod(p.size()      ) for p in model_parameters]      )
	return params


SCREAMING_SNAKE_CASE						=    logging.getLogger(__name__)



class 			_lowerCamelCase      (pl.Callback  ):







				def 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				{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 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							pl.Trainer      ,       lowerCamelCase_     :							pl.LightningModule      ,       lowerCamelCase_     :							str      ,       lowerCamelCase_     :							Optional[Any]=True    ):

					"""simple docstring"""






					logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
					_lowercase			:     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
					_lowercase			:     List[str]      					=				Path(pl_module.hparams.output_dir    )
					if type_path == "test":
						_lowercase			:     Dict      					=				od / 'test_results.txt'
						_lowercase			:     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.
						_lowercase			:     int      					=				od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
						_lowercase			:     Any      					=				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
							_lowercase			:     List[str]      					=				metrics[key]
							if isinstance(lowerCamelCase_      ,       torch.Tensor    ):
								_lowercase			:     List[Any]      					=				val.item()
							_lowercase			:     Optional[int]      					=				F'''{key}: {val:.6f}\n'''
							writer.write(lowerCamelCase_    )

					if not save_generations:
						return

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







				@rank_zero_only
				def 				__UpperCAmelCase       (			self     :							int      ,       lowerCamelCase_     :							str      ,       lowerCamelCase_     :							Optional[Any]    ):

					"""simple docstring"""






					try:
						_lowercase			:     List[Any]      					=				pl_module.model.model.num_parameters()
					except AttributeError:
						_lowercase			:     Union[str, Any]      					=				pl_module.model.num_parameters()

					_lowercase			:     str      					=				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 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							pl.Trainer      ,       lowerCamelCase_     :							pl.LightningModule    ):

					"""simple docstring"""






					save_json(pl_module.metrics      ,       pl_module.metrics_save_path    )
					return self._write_logs(lowerCamelCase_      ,       lowerCamelCase_      ,       'test'    )







				@rank_zero_only
				def 				__UpperCAmelCase       (			self     :							int      ,       lowerCamelCase_     :							pl.Trainer      ,       lowerCamelCase_     :							List[str]    ):

					"""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")



def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=1  ,__UpperCAmelCase=False      ):






	"""simple docstring"""





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

	_lowercase			:     str      					=				ModelCheckpoint(
	    dirpath=__UpperCAmelCase  ,filename=__UpperCAmelCase  ,monitor=F'''val_{metric}'''  ,mode='min' if 'loss' in metric else 'max'  ,save_top_k=__UpperCAmelCase  ,)
	return checkpoint_callback







def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





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



