



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


UpperCAmelCase__       =	16
UpperCAmelCase__       =	32





def 							A     (					_UpperCAmelCase    :       Accelerator							,       _UpperCAmelCase    :       int = 16							,       _UpperCAmelCase    :       str = "bert-base-cased"   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       AutoTokenizer.from_pretrained(_UpperCAmelCase   )
			_UpperCAmelCase			=       load_dataset('glue'							,       'mrpc'   )

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

			# Apply the method we just defined to all the examples in all the splits of the dataset
			_UpperCAmelCase			=       datasets.map(
			    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       remove_columns=['idx', 'sentence1', 'sentence2']							,       load_from_cache_file=_UpperCAmelCase   )

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

			def collate_fn(_UpperCAmelCase    :       List[Any]   ):
						# 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(_UpperCAmelCase							,       padding='max_length'							,       max_length=128							,       return_tensors='pt'   )
						return tokenizer.pad(_UpperCAmelCase							,       padding='longest'							,       return_tensors='pt'   )

			# Instantiate dataloaders.
			_UpperCAmelCase			=       DataLoader(
			    tokenized_datasets['train']							,       shuffle=_UpperCAmelCase							,       collate_fn=_UpperCAmelCase							,       batch_size=_UpperCAmelCase   )
			_UpperCAmelCase			=       DataLoader(
			    tokenized_datasets['validation']							,       shuffle=_UpperCAmelCase							,       collate_fn=_UpperCAmelCase							,       batch_size=_UpperCAmelCase   )

			return train_dataloader, eval_dataloader





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       int   )    ->			Dict:







			'''simple docstring'''
			# Initialize accelerator
			_UpperCAmelCase			=       Accelerator()

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

			set_seed(_UpperCAmelCase   )
			_UpperCAmelCase	,					_UpperCAmelCase			=       get_dataloaders(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_UpperCAmelCase			=       AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase							,       return_dict=_UpperCAmelCase   )

			# Instantiate optimizer
			_UpperCAmelCase			=       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			_UpperCAmelCase			=       optimizer_cls(params=model.parameters()							,       lr=_UpperCAmelCase   )

			if accelerator.state.deepspeed_plugin is not None:
						_UpperCAmelCase			=       accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						_UpperCAmelCase			=       1
			_UpperCAmelCase			=       (len(_UpperCAmelCase   ) * num_epochs) // gradient_accumulation_steps

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

			# We need to keep track of how many total steps we have iterated over
			_UpperCAmelCase			=       0
			# We also need to keep track of the stating epoch so files are named properly
			_UpperCAmelCase			=       0

			# Now we train the model
			_UpperCAmelCase			=       evaluate.load('glue'							,       'mrpc'   )
			_UpperCAmelCase			=       0
			_UpperCAmelCase			=       {}
			for epoch in range(_UpperCAmelCase							,       _UpperCAmelCase   ):
						model.train()
						for step, batch in enumerate(_UpperCAmelCase   ):
									_UpperCAmelCase			=       model(**_UpperCAmelCase   )
									_UpperCAmelCase			=       outputs.loss
									_UpperCAmelCase			=       loss / gradient_accumulation_steps
									accelerator.backward(_UpperCAmelCase   )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						_UpperCAmelCase			=       0
						for step, batch in enumerate(_UpperCAmelCase   ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device   )
									with torch.no_grad():
												_UpperCAmelCase			=       model(**_UpperCAmelCase   )
									_UpperCAmelCase			=       outputs.logits.argmax(dim=-1   )
									# It is slightly faster to call this once, than multiple times
									_UpperCAmelCase	,					_UpperCAmelCase			=       accelerator.gather(
									    (predictions, batch['labels'])   )  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(_UpperCAmelCase   ) - 1:
															_UpperCAmelCase			=       predictions[: len(eval_dataloader.dataset   ) - samples_seen]
															_UpperCAmelCase			=       references[: len(eval_dataloader.dataset   ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=_UpperCAmelCase							,       references=_UpperCAmelCase							,       )

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

						if best_performance < eval_metric["accuracy"]:
									_UpperCAmelCase			=       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(_UpperCAmelCase							,       _UpperCAmelCase   )





def 							A     (					)    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
			parser.add_argument(
			    '--model_name_or_path'							,       type=_UpperCAmelCase							,       default='bert-base-cased'							,       help='Path to pretrained model or model identifier from huggingface.co/models.'							,       required=_UpperCAmelCase							,       )
			parser.add_argument(
			    '--output_dir'							,       type=_UpperCAmelCase							,       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=_UpperCAmelCase							,       default=_UpperCAmelCase							,       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=_UpperCAmelCase							,       default=3							,       help='Number of train epochs.'							,       )
			_UpperCAmelCase			=       parser.parse_args()
			_UpperCAmelCase			=       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
			training_function(_UpperCAmelCase							,       _UpperCAmelCase   )


if __name__ == "__main__":
							main()





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCAmelCase__       =	r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(A						)
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''rag'''
			UpperCamelCase        =					True



			def __init__(						self       :  str			,		A       :  int=None			,		A       :  Dict=True			,		A       :  int=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  int=None			,		A       :  str=" / "			,		A       :  int=" // "			,		A       :  Any=5			,		A       :  Optional[Any]=3_00			,		A       :  Optional[Any]=7_68			,		A       :  Optional[int]=8			,		A       :  Union[str, Any]="wiki_dpr"			,		A       :  int="train"			,		A       :  Tuple="compressed"			,		A       :  str=None			,		A       :  Any=None			,		A       :  Tuple=False			,		A       :  List[str]=False			,		A       :  Tuple=0.0			,		A       :  Any=True			,		A       :  int=False			,		A       :  Dict=False			,		A       :  List[str]=False			,		A       :  Optional[Any]=True			,		A       :  Tuple=None			,		**A       :  Optional[Any]			,		)       ->   Optional[Any]:



						"""simple docstring"""






						super().__init__(
						    bos_token_id=A			,		pad_token_id=A			,		eos_token_id=A			,		decoder_start_token_id=A			,		forced_eos_token_id=A			,		is_encoder_decoder=A			,		prefix=A			,		vocab_size=A			,		**A			,		)
						assert (
						    "question_encoder" in kwargs and "generator" in kwargs
						), "Config has to be initialized with question_encoder and generator config"
						_UpperCAmelCase			=       kwargs.pop('question_encoder')
						_UpperCAmelCase			=       question_encoder_config.pop('model_type')
						_UpperCAmelCase			=       kwargs.pop('generator')
						_UpperCAmelCase			=       decoder_config.pop('model_type')

						from ..auto.configuration_auto import AutoConfig

						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)
						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)

						_UpperCAmelCase			=       reduce_loss
						_UpperCAmelCase			=       label_smoothing
						_UpperCAmelCase			=       exclude_bos_score
						_UpperCAmelCase			=       do_marginalize

						_UpperCAmelCase			=       title_sep
						_UpperCAmelCase			=       doc_sep
						_UpperCAmelCase			=       n_docs
						_UpperCAmelCase			=       max_combined_length

						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       dataset_split
						_UpperCAmelCase			=       index_name

						_UpperCAmelCase			=       retrieval_vector_size
						_UpperCAmelCase			=       retrieval_batch_size
						_UpperCAmelCase			=       passages_path
						_UpperCAmelCase			=       index_path
						_UpperCAmelCase			=       use_dummy_dataset

						_UpperCAmelCase			=       output_retrieved

						_UpperCAmelCase			=       do_deduplication

						_UpperCAmelCase			=       use_cache

						if self.forced_eos_token_id is None:
									_UpperCAmelCase			=       getattr(self.generator			,		'forced_eos_token_id'			,		A)



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any]			,		A       :  PretrainedConfig			,		A       :  PretrainedConfig			,		**A       :  Optional[Any])       ->   PretrainedConfig:



						"""simple docstring"""






						return cls(question_encoder=question_encoder_config.to_dict()			,		generator=generator_config.to_dict()			,		**A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       copy.deepcopy(self.__dict__)
						_UpperCAmelCase			=       self.question_encoder.to_dict()
						_UpperCAmelCase			=       self.generator.to_dict()
						_UpperCAmelCase			=       self.__class__.model_type
						return output
