


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(_UpperCamelCase					,				_UpperCamelCase   )


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    emb.weight.shape
			__lowerCAmelCase       					=    nn.Linear(_UpperCamelCase					,				_UpperCamelCase					,				bias=_UpperCamelCase   )
			__lowerCAmelCase       					=    emb.weight.data
			return lin_layer


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location="cpu"   )
			__lowerCAmelCase       					=    Namespace(**checkpoint["cfg"]["model"]   )
			__lowerCAmelCase       					=    checkpoint["model"]
			remove_ignore_keys_(_UpperCamelCase   )
			__lowerCAmelCase       					=    state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase       					=    {key.replace("decoder"					,				"model"   ): val for key, val in state_dict.items()}

			__lowerCAmelCase       					=    XGLMConfig(
			    vocab_size=_UpperCamelCase					,				max_position_embeddings=args.max_target_positions					,				num_layers=args.decoder_layers					,				attention_heads=args.decoder_attention_heads					,				ffn_dim=args.decoder_ffn_embed_dim					,				d_model=args.decoder_embed_dim					,				layerdrop=args.decoder_layerdrop					,				dropout=args.dropout					,				attention_dropout=args.attention_dropout					,				activation_dropout=args.activation_dropout					,				activation_function="gelu"					,				scale_embedding=not args.no_scale_embedding					,				tie_word_embeddings=args.share_decoder_input_output_embed					,				)

			__lowerCAmelCase       					=    XGLMForCausalLM(_UpperCamelCase   )
			__lowerCAmelCase       					=    model.load_state_dict(_UpperCamelCase					,				strict=_UpperCamelCase   )
			print(_UpperCamelCase   )
			__lowerCAmelCase       					=    make_linear_from_emb(model.model.embed_tokens   )

			return model


if __name__ == "__main__":
	A  :     Tuple    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
	parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	A  :     Dict    					=    parser.parse_args()
	A  :     int    					=    convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)








"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    psutil.Process()
										__lowerCAmelCase       					=    False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    -1

										while True:
													__lowerCAmelCase       					=    max(self.process.memory_info().rss					,     self.cpu_memory_peak			)

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													if not self.peak_monitoring:
																break

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    threading.Thread(target=self.peak_monitor			)
										__lowerCAmelCase       					=    True
										self.thread.start()



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    False
										self.thread.join()
										return self.cpu_memory_peak
A  :     Any    					=    PeakCPUMemory()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    torch.cuda.memory_allocated(_UpperCamelCase   )
			torch.cuda.reset_peak_memory_stats()

			return measures


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase       					=    (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    (torch.cuda.memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20
						__lowerCAmelCase       					=    (torch.cuda.max_memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20

			return measures


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			print(f"{description}:"   )
			print(f"- Time: {measures['time']:.2f}s"   )
			for i in range(torch.cuda.device_count()   ):
						print(f"- GPU {i} allocated: {measures[str(_UpperCamelCase   )]:.2f}MiB"   )
						__lowerCAmelCase       					=    measures[f"{i}-peak"]
						print(f"- GPU {i} peak: {peak:.2f}MiB"   )
			print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"   )
			print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"   )




