



"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
				from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
				raise ImportError('You need to install the version of fairseq from the Mega repo!')

class 	__lowerCAmelCase							(nn.Module  ):


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										super().__init__()
										snake_case_						:Any								=						mega_args
										snake_case_						:Tuple								=						nn.Embedding(a    ,       self.mega_args.encoder_embed_dim   )
										snake_case_						:List[str]								=						nn.ModuleList([MegaEncoderLayer(self.mega_args   ) for _ in range(a   )]   )
										snake_case_						:Union[str, Any]								=						depth







					def 		_a (      self    ,       a    ,       a    ,       a=True    ,       a=0   ):




										"""simple docstring"""





										if batch_first:
															snake_case_						:int								=						input_ids.T

										# to make things more confusing, Mega expects the attention mask to
										# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
										# which is the opposite of what HF returns
										if ignore_mask_value == 0:
															snake_case_						:str								=						1 - attention_mask

										# get token embeddings from IDs
										snake_case_						:Optional[int]								=						self.embedding_layer(a   )

										# pass through the Mega layers
										# input is (time, batch, encoder dim) and output is the same
										for encoder in self.encoders:
															snake_case_						:Dict								=						encoder(a    ,       a   )

										# return according to the shape specified
										if batch_first:
															# (T, B, H) --> (B, T, H)
															return torch.transpose(a    ,       0    ,       1   )
										else:
															return embeds



class 	__lowerCAmelCase							(nn.Module  ):


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										super().__init__()
										snake_case_						:Dict								=						MegaLM(a    ,       a    ,       a   )
										snake_case_						:List[str]								=						nn.Linear(mega_args.encoder_embed_dim    ,       a   )
										snake_case_						:Dict								=						nn.Dropout(p=0.1   )







					def 		_a (      self    ,       a    ,       a    ,       a=True    ,       a=0   ):




										"""simple docstring"""





										snake_case_						:Any								=						self.mega(a    ,       a    ,       a    ,       a   )
										return self.mlm_head(self.dropout(a   )   )



def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					with open(os.path.join(_A,     "model_args.pkl"							),     "rb"							) as f:
										snake_case_						:Any								=						pkl.load(_A							)

					# load the original encoder
					snake_case_						:Optional[int]								=						OriginalMegaForMaskedLM(**_A							).eval()

					# load its weights
					print(
					    "Original Mega encoder:",     original_mlm.mega.load_state_dict(
					        torch.load(os.path.join(_A,     "encoder_weights.pt"							),     map_location="cpu"							)							),     )
					print(
					    "Original Mega MLM layer:",     original_mlm.mlm_head.load_state_dict(
					        torch.load(os.path.join(_A,     "mlm_head_weights.pt"							),     map_location="cpu"							)							),     )

					# create a new config from the old one
					snake_case_						:int								=						MegaConfig(
					    num_hidden_layers=mega_original_args["depth"],     vocab_size=mega_original_args["vocab_size"],     hidden_size=mega_original_args["mega_args"].encoder_embed_dim,     shared_representation_size=mega_original_args["mega_args"].encoder_z_dim,     intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim,     ema_projection_size=mega_original_args["mega_args"].encoder_n_dim,     dropout_prob=mega_original_args["mega_args"].dropout,     attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout,     hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout,     activation=mega_original_args["mega_args"].activation_fn,     attention_activation=mega_original_args["mega_args"].attention_activation_fn,     bidirectional=mega_original_args["mega_args"].bidirectional,     use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0,     chunk_size=mega_original_args["mega_args"].encoder_chunk_size,     truncation=mega_original_args["mega_args"].truncation_length,     normalization_type=mega_original_args["mega_args"].normalization_type,     normalize_before_mega=_A,     norm_affine=_A,     use_feature_dropout=mega_original_args["mega_args"].feature_dropout,     relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias,     max_positions=mega_original_args["mega_args"].max_source_positions,     nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim,     normalize_before_ffn=mega_original_args["mega_args"].normalize_before,     nffn_activation_dropout_prob=0.0,     add_token_type_embeddings=_A,     add_lm_hidden_dense_layer=_A,     )

					snake_case_						:Optional[int]								=						MegaForMaskedLM(_A							).eval()

					# the originl checkpoint just uses nn.Embedding for the word embeddings
					# we use a wrapper module for embeddings to add support for positional embeddings
					snake_case_						:Optional[int]								=						original_mlm.mega.embedding_layer.weight

					# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
					# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
					# also renaming previously confusing parameter names
					snake_case_						:Dict								=						original_mlm.mega.encoders.state_dict()
					snake_case_						:Union[str, Any]								=						{}
					for module_name in original_state_dict.keys():
										snake_case_						:int								=						None
										# have to handle gamma, beta, and alpha differently due to their use
										# in multiple modules within the original repository;
										# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
										# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
										if "beta" in module_name:
															# EMA sub-layers were always called "move" in the original repo
															if "move.beta" in module_name:
																				snake_case_						:str								=						module_name.replace("move.beta",     "ema_gate.ema_expansion_matrix"							)
															elif "mega_layer.beta" in module_name:
																				snake_case_						:List[Any]								=						module_name.replace("beta",     "qk_bias"							)
															else:
																				snake_case_						:Tuple								=						module_name.replace("beta",     "b_param"							)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
										elif "gamma" in module_name:
															if "move.gamma" in module_name:
																				snake_case_						:Optional[Any]								=						module_name.replace("move.gamma",     "ema_gate.kernel_projection_matrix"							)
															elif "mega_layer.gamma" in module_name:
																				snake_case_						:str								=						module_name.replace("gamma",     "qk_weight"							)
															else:
																				snake_case_						:Optional[Any]								=						module_name.replace("gamma",     "g_param"							)
        # alpha is used in EMA and positional bias; renaming to improve readability
										elif "move.alpha" in module_name:
															snake_case_						:str								=						module_name.replace("move.alpha",     "ema_gate.decay_factor"							)
										# delta is only used in EMA; renaming to improve readability
										elif "move.delta" in module_name:
															snake_case_						:int								=						module_name.replace("move.delta",     "ema_gate.damping_factor"							)
										# omega is only used in EMA; renaming to improve readability
										elif "omega" in module_name:
															snake_case_						:Tuple								=						module_name.replace("move.omega",     "ema_gate.residual_weight"							)

										if new_module_name:
															snake_case_						:Any								=						new_module_name

					if len(_A							) != 0:
										print(F'''Renaming these keys: {updated_keys.keys()}'''							)
					else:
										print("No need to rename state dict entries"							)
					for old, new in updated_keys.items():
										snake_case_						:int								=						original_state_dict.pop(_A							)

					# now attempt to load the state dictionary with updated names
					# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
					print("HF Mega encoder:",     hf_mlm.mega.layers.load_state_dict(_A							)							)

					# load the MLM head weights directly
					print(
					    "HF Mega MLM layer:",     hf_mlm.mlm_head.load_state_dict(
					        torch.load(os.path.join(_A,     "mlm_head_weights.pt"							),     map_location="cpu"							)							),     )

					# test on a randomly generated input sequence
					snake_case_						:int								=						torch.randint(0,     hf_config.vocab_size,     size=(4, 256)							)
					snake_case_						:Any								=						torch.ones_like(_A							)
					# mask a few tokens to make sure masking is applied appropriately :)
					snake_case_						:Optional[Any]								=						0

					# run forward passes
					snake_case_						:Dict								=						original_mlm(_A,     _A,     batch_first=_A,     ignore_mask_value=0							)
					snake_case_						:Dict								=						hf_mlm(_A,     _A							)[0]

					# print shapes and diff
					print(F'''original output {original_output.shape}'''							)
					print(F'''hf output {hf_output.shape}'''							)
					print(F'''max diff: {(original_output - hf_output).max()}'''							)  # 0.0
					snake_case_						:Union[str, Any]								=						torch.allclose(_A,     _A,     atol=1e-3							)

					if success:
										print("Yay!"							)
										hf_mlm.save_pretrained(_A							)
					else:
										raise RuntimeError(F'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''							)

					if includes_tokenizer:
										print("Transferring tokenizer"							)
										snake_case_						:List[Any]								=						AutoTokenizer.from_pretrained(_A							)
										tokenizer.save_pretrained(_A							)


if __name__ == "__main__":
				__UpperCAmelCase      :      Optional[int]   					=					argparse.ArgumentParser()

				parser.add_argument(
				    '--pretrained_checkpoint_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Point to the directory containing your model weights using the official Mega repo',
				)

				parser.add_argument(
				    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
				)

				parser.add_argument(
				    '--includes_tokenizer',
				    action='store_true',
				    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
				)

				__UpperCAmelCase      :      Any   					=					parser.parse_args()

				convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)






"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase      :      Dict   					=					16
__UpperCAmelCase      :      List[Any]   					=					32
def      A      (			_A,     _A							):




					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						Accelerator(
										    cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with="all",     project_dir=args.project_dir							)
					else:
										snake_case_						:Tuple								=						Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision							)

					if hasattr(args.checkpointing_steps,     "isdigit"							):
										if args.checkpointing_steps == "epoch":
															snake_case_						:Tuple								=						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															snake_case_						:int								=						int(args.checkpointing_steps							)
										else:
															raise ValueError(
															    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
					else:
										snake_case_						:Tuple								=						None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_						:Union[str, Any]								=						config["lr"]
					snake_case_						:Any								=						int(config["num_epochs"]							)
					snake_case_						:Optional[int]								=						int(config["seed"]							)
					snake_case_						:List[Any]								=						int(config["batch_size"]							)

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						os.path.split(_A							)[-1].split("."							)[0]
										accelerator.init_trackers(_A,     _A							)

					snake_case_						:Any								=						AutoTokenizer.from_pretrained("bert-base-cased"							)
					snake_case_						:Any								=						load_dataset("glue",     "mrpc"							)
					snake_case_						:Optional[Any]								=						evaluate.load("glue",     "mrpc"							)

					def tokenize_function(_A							):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_						:Any								=						tokenizer(examples["sentence1"],     examples["sentence2"],     truncation=_A,     max_length=_A							)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										snake_case_						:int								=						datasets.map(
										    _A,     batched=_A,     remove_columns=["idx", "sentence1", "sentence2"],     )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					snake_case_						:Any								=						tokenized_datasets.rename_column("label",     "labels"							)

					# If the batch size is too big we use gradient accumulation
					snake_case_						:List[Any]								=						1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										snake_case_						:Optional[int]								=						batch_size // MAX_GPU_BATCH_SIZE
										snake_case_						:List[str]								=						MAX_GPU_BATCH_SIZE

					def collate_fn(_A							):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_						:Optional[int]								=						128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															snake_case_						:Union[str, Any]								=						16
										elif accelerator.mixed_precision != "no":
															snake_case_						:List[Any]								=						8
										else:
															snake_case_						:Any								=						None

										return tokenizer.pad(
										    _A,     padding="longest",     max_length=_A,     pad_to_multiple_of=_A,     return_tensors="pt",     )

					# Instantiate dataloaders.
					snake_case_						:str								=						DataLoader(
					    tokenized_datasets["train"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)
					snake_case_						:Any								=						DataLoader(
					    tokenized_datasets["validation"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)

					set_seed(_A							)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_						:Optional[Any]								=						AutoModelForSequenceClassification.from_pretrained("bert-base-cased",     return_dict=_A							)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					snake_case_						:str								=						model.to(accelerator.device							)

					# Instantiate optimizer
					snake_case_						:List[Any]								=						AdamW(params=model.parameters(),     lr=_A							)

					# Instantiate scheduler
					snake_case_						:Tuple								=						get_linear_schedule_with_warmup(
					    optimizer=_A,     num_warmup_steps=100,     num_training_steps=(len(_A							) * num_epochs) // gradient_accumulation_steps,     )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					snake_case_	,  snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[Any]								=						accelerator.prepare(
					    _A,     _A,     _A,     _A,     _A							)

					# We need to keep track of how many total steps we have iterated over
					snake_case_						:Optional[int]								=						0
					# We also need to keep track of the stating epoch so files are named properly
					snake_case_						:int								=						0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
															accelerator.load_state(args.resume_from_checkpoint							)
															snake_case_						:str								=						os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															snake_case_						:int								=						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															snake_case_						:Tuple								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_						:Optional[int]								=						os.path.splitext(_A							)[0]

										if "epoch" in training_difference:
															snake_case_						:Union[str, Any]								=						int(training_difference.replace("epoch_",     ""							)							) + 1
															snake_case_						:List[str]								=						None
										else:
															snake_case_						:Tuple								=						int(training_difference.replace("step_",     ""							)							)
															snake_case_						:Optional[int]								=						resume_step // len(_A							)
															resume_step -= starting_epoch * len(_A							)

    # Now we train the model
					for epoch in range(_A,     _A							):
										model.train()
										if args.with_tracking:
															snake_case_						:Optional[Any]								=						0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															snake_case_						:Dict								=						accelerator.skip_first_batches(_A,     _A							)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															snake_case_						:Optional[Any]								=						train_dataloader
										for step, batch in enumerate(_A							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															snake_case_						:Union[str, Any]								=						model(**_A							)
															snake_case_						:str								=						outputs.loss
															snake_case_						:int								=						loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(_A							)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(_A,     _A							):
																				snake_case_						:List[str]								=						F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_						:Tuple								=						os.path.join(args.output_dir,     _A							)
																									accelerator.save_state(_A							)

										model.eval()
										for step, batch in enumerate(_A							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															with torch.no_grad():
																				snake_case_						:str								=						model(**_A							)
															snake_case_						:str								=						outputs.logits.argmax(dim=-1							)
															snake_case_	,  snake_case_						:List[str]								=						accelerator.gather_for_metrics((predictions, batch["labels"])							)
															metric.add_batch(
															    predictions=_A,     references=_A,     )

										snake_case_						:int								=						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:''',     _A							)
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": eval_metric["accuracy"],
															        "f1": eval_metric["f1"],
															        "train_loss": total_loss.item() / len(_A							),
															        "epoch": epoch,
															    },     step=_A,     )

										if checkpointing_steps == "epoch":
															snake_case_						:str								=						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				snake_case_						:Any								=						os.path.join(args.output_dir,     _A							)
															accelerator.save_state(_A							)

					if args.with_tracking:
										accelerator.end_training()
def      A      (			):




					"""simple docstring"""
					snake_case_						:str								=						argparse.ArgumentParser(description="Simple example of training script."							)
					parser.add_argument(
					    "--mixed_precision",     type=_A,     default=_A,     choices=["no", "fp16", "bf16", "fp8"],     help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU.",     )
					parser.add_argument("--cpu",     action="store_true",     help="If passed, will train on the CPU."							)
					parser.add_argument(
					    "--checkpointing_steps",     type=_A,     default=_A,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",     )
					parser.add_argument(
					    "--resume_from_checkpoint",     type=_A,     default=_A,     help="If the training should continue from a checkpoint folder.",     )
					parser.add_argument(
					    "--with_tracking",     action="store_true",     help="Whether to load in all available experiment trackers from the environment and use them for logging.",     )
					parser.add_argument(
					    "--output_dir",     type=_A,     default=".",     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",     )
					parser.add_argument(
					    "--project_dir",     type=_A,     default="logs",     help="Location on where to store experiment tracking logs` and relevent project information",     )
					snake_case_						:Optional[int]								=						parser.parse_args()
					snake_case_						:Optional[Any]								=						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_A,     _A							)


if __name__ == "__main__":
				main()

