


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase__			:      List[str]								=16
lowerCAmelCase__			:      int								=32






def 						a__			(				A__,      A__ = 1_6,      A__ = "bert-base-cased"				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					AutoTokenizer.from_pretrained(A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					load_dataset('glue',      'mrpc'				)

			def tokenize_function(A__				):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE_		:     Dict					=					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
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					datasets.map(
			    A__,      batched=A__,      remove_columns=['idx', 'sentence1', 'sentence2'],      load_from_cache_file=A__				)

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

			def collate_fn(A__				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A__,      padding='max_length',      max_length=1_2_8,      return_tensors='pt'				)
						return tokenizer.pad(A__,      padding='longest',      return_tensors='pt'				)

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					DataLoader(
			    tokenized_datasets['train'],      shuffle=A__,      collate_fn=A__,      batch_size=A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					DataLoader(
			    tokenized_datasets['validation'],      shuffle=A__,      collate_fn=A__,      batch_size=A__				)

			return train_dataloader, eval_dataloader






def 						a__			(				A__,      A__				):
			# Initialize accelerator
			SCREAMING_SNAKE_CASE_		:     Any					=					Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE_		:     Dict					=					config['lr']
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					int(config['num_epochs']				)
			SCREAMING_SNAKE_CASE_		:     Any					=					int(config['seed']				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					int(config['batch_size']				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					args.model_name_or_path

			set_seed(A__				)
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Any					=					get_dataloaders(A__,      A__,      A__				)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					AutoModelForSequenceClassification.from_pretrained(A__,      return_dict=A__				)

			# Instantiate optimizer
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					optimizer_cls(params=model.parameters(),      lr=A__				)

			if accelerator.state.deepspeed_plugin is not None:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					1
			SCREAMING_SNAKE_CASE_		:     Tuple					=					(len(A__				) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						SCREAMING_SNAKE_CASE_		:     Any					=					get_linear_schedule_with_warmup(
						    optimizer=A__,      num_warmup_steps=0,      num_training_steps=A__,      )
			else:
						SCREAMING_SNAKE_CASE_		:     str					=					DummyScheduler(A__,      total_num_steps=A__,      warmup_num_steps=0				)

			# 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.
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					accelerator.prepare(
			    A__,      A__,      A__,      A__,      A__				)

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

			# Now we train the model
			SCREAMING_SNAKE_CASE_		:     Dict					=					evaluate.load('glue',      'mrpc'				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					0
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{}
			for epoch in range(A__,      A__				):
						model.train()
						for step, batch in enumerate(A__				):
									SCREAMING_SNAKE_CASE_		:     str					=					model(**A__				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					outputs.loss
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					loss / gradient_accumulation_steps
									accelerator.backward(A__				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						SCREAMING_SNAKE_CASE_		:     List[str]					=					0
						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():
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(**A__				)
									SCREAMING_SNAKE_CASE_		:     str					=					outputs.logits.argmax(dim=-1				)
									# It is slightly faster to call this once, than multiple times
									SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					accelerator.gather(
									    (predictions, batch['labels'])				)  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(A__				) - 1:
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					predictions[: len(eval_dataloader.dataset				) - samples_seen]
															SCREAMING_SNAKE_CASE_		:     Any					=					references[: len(eval_dataloader.dataset				) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=A__,      references=A__,      )

						SCREAMING_SNAKE_CASE_		:     List[Any]					=					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:''',      A__				)
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					eval_metric['accuracy']

						if best_performance < eval_metric["accuracy"]:
									SCREAMING_SNAKE_CASE_		:     List[str]					=					eval_metric['accuracy']

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir,      'all_results.json'				),      'w'				) as f:
									json.dump(A__,      A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'				)
			parser.add_argument(
			    '--model_name_or_path',      type=A__,      default='bert-base-cased',      help='Path to pretrained model or model identifier from huggingface.co/models.',      required=A__,      )
			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(
			    '--performance_lower_bound',      type=A__,      default=A__,      help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.',      )
			parser.add_argument(
			    '--num_epochs',      type=A__,      default=3,      help='Number of train epochs.',      )
			SCREAMING_SNAKE_CASE_		:     Any					=					parser.parse_args()
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
			training_function(A__,      A__				)


if __name__ == "__main__":
				main()









import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					[
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'encoder.embed_positions._float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(A__,      A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					emb.weight.shape
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(A__,      A__,      bias=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					emb.weight.data
			return lin_layer






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.load(A__,      map_location='cpu'				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					mam_aaa['args'] or mam_aaa['cfg']['model']
			SCREAMING_SNAKE_CASE_		:     Any					=					mam_aaa['model']
			remove_ignore_keys_(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:     Dict					=					MaMaaaConfig(
			    vocab_size=A__,      max_position_embeddings=1_0_2_4,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      encoder_layerdrop=args.encoder_layerdrop,      decoder_layerdrop=args.decoder_layerdrop,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='relu',      )

			SCREAMING_SNAKE_CASE_		:     int					=					state_dict['decoder.embed_tokens.weight']
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MaMaaaForConditionalGeneration(A__				)
			model.model.load_state_dict(A__,      strict=A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								=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.')
				lowerCAmelCase__			:      Optional[Any]								=parser.parse_args()
				lowerCAmelCase__			:      int								=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)





