"""simple docstring"""


import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer



@require_faiss
@require_torch
class    a__    (   UpperCAmelCase_	):


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							tempfile.mkdtemp()
        lowercase__				 =							8

        # DPR tok
        lowercase__				 =							[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowercase__				 =							os.path.join(self.tmpdirname,				"dpr_tokenizer"  )
        os.makedirs(_lowercase,				exist_ok=_lowercase  )
        lowercase__				 =							os.path.join(_lowercase,				DPR_VOCAB_FILES_NAMES["vocab_file"]  )
        with open(self.vocab_file,				"w",				encoding="utf-8"  ) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]  )  )

        # BART tok
        lowercase__				 =							[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        lowercase__				 =							dict(zip(_lowercase,				range(len(_lowercase  )  )  )  )
        lowercase__				 =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        lowercase__				 =							{"unk_token": "<unk>"}

        lowercase__				 =							os.path.join(self.tmpdirname,				"bart_tokenizer"  )
        os.makedirs(_lowercase,				exist_ok=_lowercase  )
        lowercase__				 =							os.path.join(_lowercase,				BART_VOCAB_FILES_NAMES["vocab_file"]  )
        lowercase__				 =							os.path.join(_lowercase,				BART_VOCAB_FILES_NAMES["merges_file"]  )
        with open(self.vocab_file,				"w",				encoding="utf-8"  ) as fp:
             fp.write(json.dumps(_lowercase  ) + "\n"  )
        with open(self.merges_file,				"w",				encoding="utf-8"  ) as fp:
             fp.write("\n".join(_lowercase  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,				"dpr_tokenizer"  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,				"bart_tokenizer"  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        shutil.rmtree(self.tmpdirname  )


   @require_tokenizers
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							os.path.join(self.tmpdirname,				"rag_tokenizer"  )
        lowercase__				 =							RagConfig(question_encoder=DPRConfig().to_dict(),				generator=BartConfig().to_dict()  )
        lowercase__				 =							RagTokenizer(question_encoder=self.get_dpr_tokenizer(),				generator=self.get_bart_tokenizer()  )
        rag_config.save_pretrained(_lowercase  )
        rag_tokenizer.save_pretrained(_lowercase  )
        lowercase__				 =							RagTokenizer.from_pretrained(_lowercase,				config=_lowercase  )
        self.assertIsInstance(new_rag_tokenizer.question_encoder,				_lowercase  )
        self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab(),				rag_tokenizer.question_encoder.get_vocab()  )
        self.assertIsInstance(new_rag_tokenizer.generator,				_lowercase  )
        self.assertEqual(new_rag_tokenizer.generator.get_vocab(),				rag_tokenizer.generator.get_vocab()  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							RagTokenizer.from_pretrained("facebook/rag-token-nq"  )
        lowercase__				 =							[
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
            "what is the first step in the evolution of the eye",
            "where is gall bladder situated in human body",
            "what is the main mineral in lithium batteries",
            "who is the president of usa right now",
            "where do the greasers live in the outsiders",
            "panda is a national animal of which country",
            "what is the name of manchester united stadium",
        ]
        lowercase__				 =							tokenizer(_lowercase  )
        self.assertIsNotNone(_lowercase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							RagTokenizer.from_pretrained("facebook/rag-sequence-nq"  )
        lowercase__				 =							[
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
            "what is the first step in the evolution of the eye",
            "where is gall bladder situated in human body",
            "what is the main mineral in lithium batteries",
            "who is the president of usa right now",
            "where do the greasers live in the outsiders",
            "panda is a national animal of which country",
            "what is the name of manchester united stadium",
        ]
        lowercase__				 =							tokenizer(_lowercase  )
        self.assertIsNotNone(_lowercase  )





"""simple docstring"""


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]     =					1_6
lowerCAmelCase_:					Optional[Any]     =					3_2







def 					__a   (		A    ,				A = 16    ,				A = "bert-base-cased"      ):






     '''simple docstring'''



     lowercase__				 =							AutoTokenizer.from_pretrained(A      )
     lowercase__				 =							load_dataset("glue"    ,				"mrpc"      )

     def tokenize_function(A      ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase__				 =							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
     lowercase__				 =							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
     lowercase__				 =							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_28    ,				return_tensors="pt"      )
          return tokenizer.pad(A    ,				padding="longest"    ,				return_tensors="pt"      )

     # Instantiate dataloaders.
     lowercase__				 =							DataLoader(
         tokenized_datasets["train"]    ,				shuffle=A    ,				collate_fn=A    ,				batch_size=A      )
     lowercase__				 =							DataLoader(
         tokenized_datasets["validation"]    ,				shuffle=A    ,				collate_fn=A    ,				batch_size=A      )

     return train_dataloader, eval_dataloader







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							Accelerator()

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

     set_seed(A      )
     lowercase__ ,						lowercase__				 =							get_dataloaders(A    ,				A    ,				A      )

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

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

     if accelerator.state.deepspeed_plugin is not None:
          lowercase__				 =							accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          lowercase__				 =							1
     lowercase__				 =							(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
     ):
          lowercase__				 =							get_linear_schedule_with_warmup(
              optimizer=A    ,				num_warmup_steps=0    ,				num_training_steps=A    ,				)
     else:
          lowercase__				 =							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.
     lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							accelerator.prepare(
         A    ,				A    ,				A    ,				A    ,				A      )

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

     # Now we train the model
     lowercase__				 =							evaluate.load("glue"    ,				"mrpc"      )
     lowercase__				 =							0
     lowercase__				 =							{}
     for epoch in range(A    ,				A      ):
          model.train()
          for step, batch in enumerate(A      ):
               lowercase__				 =							model(**A      )
               lowercase__				 =							outputs.loss
               lowercase__				 =							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()
          lowercase__				 =							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():
                    lowercase__				 =							model(**A      )
               lowercase__				 =							outputs.logits.argmax(dim=-1      )
               # It is slightly faster to call this once, than multiple times
               lowercase__ ,						lowercase__				 =							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:
                         lowercase__				 =							predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                         lowercase__				 =							references[: len(eval_dataloader.dataset      ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=A    ,				references=A    ,				)

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

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






     '''simple docstring'''



     lowercase__				 =							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."    ,				)
     lowercase__				 =							parser.parse_args()
     lowercase__				 =							{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
     training_function(A    ,				A      )


if __name__ == "__main__":
 main()




