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							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  tempfile.mkdtemp()
        _A        =  8

        # DPR tok
        _A        =  [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """want""",
            """##want""",
            """##ed""",
            """wa""",
            """un""",
            """runn""",
            """##ing""",
            """,""",
            """low""",
            """lowest""",
        ]
        _A        =  os.path.join(self.tmpdirname		,				"""dpr_tokenizer"""							)
        os.makedirs(lowerCAmelCase_		,				exist_ok=lowerCAmelCase_							)
        _A        =  os.path.join(lowerCAmelCase_		,				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
        _A        =  [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """<unk>""",
        ]
        _A        =  dict(zip(lowerCAmelCase_		,				range(len(lowerCAmelCase_							)							)							)							)
        _A        =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        _A        =  {"""unk_token""": """<unk>"""}

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



 def      UpperCAmelCase							(					self							)  ->					DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"""dpr_tokenizer"""							)							)



 def      UpperCAmelCase							(					self							)  ->					BartTokenizer:
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"""bart_tokenizer"""							)							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        shutil.rmtree(self.tmpdirname							)



 @require_tokenizers
 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  os.path.join(self.tmpdirname		,				"""rag_tokenizer"""							)
        _A        =  RagConfig(question_encoder=DPRConfig().to_dict()		,				generator=BartConfig().to_dict()							)
        _A        =  RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,				generator=self.get_bart_tokenizer()							)
        rag_config.save_pretrained(lowerCAmelCase_							)
        rag_tokenizer.save_pretrained(lowerCAmelCase_							)
        _A        =  RagTokenizer.from_pretrained(lowerCAmelCase_		,				config=lowerCAmelCase_							)
        self.assertIsInstance(new_rag_tokenizer.question_encoder		,				lowerCAmelCase_							)
        self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,				rag_tokenizer.question_encoder.get_vocab()							)
        self.assertIsInstance(new_rag_tokenizer.generator		,				lowerCAmelCase_							)
        self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,				rag_tokenizer.generator.get_vocab()							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  RagTokenizer.from_pretrained("""facebook/rag-token-nq"""							)
        _A        =  [
            """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""",
        ]
        _A        =  tokenizer(lowerCAmelCase_							)
        self.assertIsNotNone(lowerCAmelCase_							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""							)
        _A        =  [
            """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""",
        ]
        _A        =  tokenizer(lowerCAmelCase_							)
        self.assertIsNotNone(lowerCAmelCase_							)





import argparse
import gc
import json
import os

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


_SCREAMING_SNAKE_CASE										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        _A        =  torch.cuda.memory_allocated()
        return self



 def __exit__(					self		,				*lowerCAmelCase_							)  ->					Optional[int]:
        gc.collect()
        torch.cuda.empty_cache()
        _A        =  torch.cuda.memory_allocated()
        _A        =  torch.cuda.max_memory_allocated()
        _A        =  bamb(self.end - self.begin							)
        _A        =  bamb(self.peak - self.begin							)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

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

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

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

       def collate_fn(snake_case__			:List[str]):
              # 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(snake_case__   ,	padding="""max_length"""   ,	max_length=128   ,	return_tensors="""pt""")
              return tokenizer.pad(snake_case__   ,	padding="""longest"""   ,	return_tensors="""pt""")

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

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _A        =  Accelerator()

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

       set_seed(snake_case__)
       _A   ,  _A        =  get_dataloaders(snake_case__   ,	snake_case__   ,	snake_case__   ,	args.n_train   ,	args.n_val)

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

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

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

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

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

       # Now we train the model
       _A        =  {}
       for epoch in range(snake_case__   ,	snake_case__):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(snake_case__):
                            _A        =  model(**snake_case__)
                            _A        =  outputs.loss
                            _A        =  loss / gradient_accumulation_steps
                            accelerator.backward(snake_case__)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin)))
              accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used))
              accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked))
              accelerator.print(
                  """Total Peak Memory consumed during the train (max): {}""".format(
                      tracemalloc.peaked + bamb(tracemalloc.begin)))
              _A        =  tracemalloc.peaked + bamb(tracemalloc.begin)
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir   ,	"""peak_memory_utilization.json""")   ,	"""w""") as f:
                     json.dump(snake_case__   ,	snake_case__)






def  snake_case  (       )						->   Optional[int]:
       _A        =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
       parser.add_argument(
           """--model_name_or_path"""   ,	type=snake_case__   ,	default="""bert-base-cased"""   ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,	required=snake_case__   ,	)
       parser.add_argument(
           """--output_dir"""   ,	type=snake_case__   ,	default="""."""   ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,	)
       parser.add_argument(
           """--peak_memory_upper_bound"""   ,	type=snake_case__   ,	default=snake_case__   ,	help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""   ,	)
       parser.add_argument(
           """--n_train"""   ,	type=snake_case__   ,	default=320   ,	help="""Number of training examples to use."""   ,	)
       parser.add_argument(
           """--n_val"""   ,	type=snake_case__   ,	default=160   ,	help="""Number of validation examples to use."""   ,	)
       parser.add_argument(
           """--num_epochs"""   ,	type=snake_case__   ,	default=1   ,	help="""Number of train epochs."""   ,	)
       _A        =  parser.parse_args()
       _A        =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()




