

'''simple docstring'''

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()
a       :      Dict  	=			logging.get_logger(__name__)

a       :      Optional[Any]  	=			"Hello world! cécé herlolip"





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase ,			UpperCAmelCase		:							Tuple        =       os.path.split(__magic_name__							)
					UpperCAmelCase		:							List[str]        =       DataaVecTextModel.from_pretrained(
					    __magic_name__			,   checkpoint_file=__magic_name__							)
					dataavec.eval()  # disable dropout
					UpperCAmelCase		:							List[Any]        =       dataavec.models[0]
					UpperCAmelCase		:							str        =       dataavec_model.encoder.sentence_encoder
					UpperCAmelCase		:							List[str]        =       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=514			,   type_vocab_size=1			,   layer_norm_eps=1e-5			,   )
					if classification_head:
										UpperCAmelCase		:							Dict        =       dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:"			,   __magic_name__							)

					UpperCAmelCase		:							Dict        =       DataaVecTextForSequenceClassification(__magic_name__							) if classification_head else DataaVecTextForMaskedLM(__magic_name__							)
					model.eval()

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

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

										# self attention
										UpperCAmelCase		:							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)							)}"
										)

										UpperCAmelCase		:							Union[str, Any]        =       dataavec_layer.self_attn.q_proj.weight
										UpperCAmelCase		:							Any        =       dataavec_layer.self_attn.q_proj.bias
										UpperCAmelCase		:							Any        =       dataavec_layer.self_attn.k_proj.weight
										UpperCAmelCase		:							Optional[int]        =       dataavec_layer.self_attn.k_proj.bias
										UpperCAmelCase		:							Union[str, Any]        =       dataavec_layer.self_attn.v_proj.weight
										UpperCAmelCase		:							Dict        =       dataavec_layer.self_attn.v_proj.bias

										# self-attention output
										UpperCAmelCase		:							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}"
										UpperCAmelCase		:							Dict        =       dataavec_layer.self_attn.out_proj.weight
										UpperCAmelCase		:							int        =       dataavec_layer.self_attn.out_proj.bias
										UpperCAmelCase		:							Optional[Any]        =       dataavec_layer.self_attn_layer_norm.weight
										UpperCAmelCase		:							Tuple        =       dataavec_layer.self_attn_layer_norm.bias

										# intermediate
										UpperCAmelCase		:							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}"
										UpperCAmelCase		:							Tuple        =       dataavec_layer.fca.weight
										UpperCAmelCase		:							Optional[Any]        =       dataavec_layer.fca.bias

										# output
										UpperCAmelCase		:							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}"
										UpperCAmelCase		:							List[str]        =       dataavec_layer.fca.weight
										UpperCAmelCase		:							Union[str, Any]        =       dataavec_layer.fca.bias
										UpperCAmelCase		:							List[str]        =       dataavec_layer.final_layer_norm.weight
										UpperCAmelCase		:							int        =       dataavec_layer.final_layer_norm.bias
										# end of layer

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

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

					UpperCAmelCase		:							Dict        =       model(__magic_name__							)[0]
					if classification_head:
										UpperCAmelCase		:							Any        =       dataavec.model.classification_heads["mnli"](dataavec.extract_features(__magic_name__							)							)
					else:
										UpperCAmelCase		:							List[Any]        =       dataavec_model(__magic_name__							)[0]
					print(our_output.shape			,   their_output.shape							)
					UpperCAmelCase		:							Optional[int]        =       torch.max(torch.abs(our_output - their_output							)							).item()
					print(F"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
					UpperCAmelCase		:							Dict        =       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__":
				a       :      int  	=			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."
				)
				a       :      Dict  	=			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 				lowercase (					__magic_name__							):



					'''simple docstring'''







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


a       :      int  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       pl.Callback       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case=True      ):


							'''simple docstring'''

							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							UpperCAmelCase		:							Optional[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
							UpperCAmelCase		:							List[str]        =       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
												UpperCAmelCase		:							Dict        =       od / "test_results.txt"
												UpperCAmelCase		:							Dict        =       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.
												UpperCAmelCase		:							Union[str, Any]        =       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
												UpperCAmelCase		:							int        =       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
												results_file.parent.mkdir(exist_ok=snake_case      )
												generations_file.parent.mkdir(exist_ok=snake_case      )
							with open(snake_case   ,      "a+"      ) as writer:
												for key in sorted(snake_case      ):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	UpperCAmelCase		:							Optional[Any]        =       metrics[key]
																	if isinstance(snake_case   ,      torch.Tensor      ):
																						UpperCAmelCase		:							Union[str, Any]        =       val.item()
																	UpperCAmelCase		:							List[str]        =       f"{key}: {val:.6f}\n"
																	writer.write(snake_case      )

							if not save_generations:
												return

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







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							try:
												UpperCAmelCase		:							Tuple        =       pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCAmelCase		:							int        =       pl_module.model.num_parameters()

							UpperCAmelCase		:							List[Any]        =       count_trainable_parameters(snake_case      )
							# 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   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

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







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=1			,   __magic_name__=False							):



					'''simple docstring'''







					if metric == "rouge2":
										UpperCAmelCase		:							Dict        =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										UpperCAmelCase		:							int        =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										UpperCAmelCase		:							Tuple        =       "{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."							)

					UpperCAmelCase		:							int        =       ModelCheckpoint(
					    dirpath=__magic_name__			,   filename=__magic_name__			,   monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   save_top_k=__magic_name__			,   )
					return checkpoint_callback





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







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