
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,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowerCamelCase_		      =	16
lowerCamelCase_		      =	32






def 		__magic_name__					(       __a     :			Accelerator			,						__a     :			int = 16		):



     '''simple docstring'''




     UpperCamelCase__												=							AutoTokenizer.from_pretrained("""bert-base-cased"""		)
     UpperCamelCase__												=							load_dataset("""glue"""			,						"""mrpc"""		)

     def tokenize_function(__a     :			str		):
          # max_length=None => use the model max length (it's actually the default)
          UpperCamelCase__												=							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():
          UpperCamelCase__												=							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
     UpperCamelCase__												=							tokenized_datasets.rename_column("""label"""			,						"""labels"""		)

     def collate_fn(__a     :			int		):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCamelCase__												=							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":
               UpperCamelCase__												=							16
          elif accelerator.mixed_precision != "no":
               UpperCamelCase__												=							8
          else:
               UpperCamelCase__												=							None

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

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

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    lowerCamelCase_		      =	mocked_dataloaders  # noqa: F811






def 		__magic_name__					(       __a     :			Tuple			,						__a     :			Optional[Any]		):



     '''simple docstring'''




     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,						a__		) == "1":
          UpperCamelCase__												=							2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          UpperCamelCase__												=							Accelerator(
              cpu=args.cpu			,						mixed_precision=args.mixed_precision			,						log_with="""all"""			,						project_dir=args.project_dir		)
     else:
          UpperCamelCase__												=							Accelerator(cpu=args.cpu			,						mixed_precision=args.mixed_precision		)
     # 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"""]		)
     set_seed(a__		)

     UpperCamelCase__						,			UpperCamelCase__												=							get_dataloaders(a__			,						a__		)
     UpperCamelCase__												=							evaluate.load("""glue"""			,						"""mrpc"""		)

     # If the batch size is too big we use gradient accumulation
     UpperCamelCase__												=							1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          UpperCamelCase__												=							batch_size // MAX_GPU_BATCH_SIZE
          UpperCamelCase__												=							MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCamelCase__												=							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).
     UpperCamelCase__												=							model.to(accelerator.device		)

     # Instantiate optimizer
     UpperCamelCase__												=							AdamW(params=model.parameters()			,						lr=a__		)

     # Instantiate scheduler
     UpperCamelCase__												=							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.
     UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__												=							accelerator.prepare(
         a__			,						a__			,						a__			,						a__			,						a__		)

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          UpperCamelCase__												=							os.path.split(a__		)[-1].split("""."""		)[0]
          accelerator.init_trackers(a__			,						a__		)

     # Now we train the model
     for epoch in range(a__		):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               UpperCamelCase__												=							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		)
               UpperCamelCase__												=							model(**a__		)
               UpperCamelCase__												=							outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               UpperCamelCase__												=							loss / gradient_accumulation_steps
               accelerator.backward(a__		)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

          UpperCamelCase__												=							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:"			,						a__		)

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          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__			,						)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()






def 		__magic_name__					(       ):



     '''simple docstring'''




     UpperCamelCase__												=							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(
         """--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(
         """--project_dir"""			,						type=a__			,						default="""logs"""			,						help="""Location on where to store experiment tracking logs` and relevent project information"""			,						)
     UpperCamelCase__												=							parser.parse_args()
     UpperCamelCase__												=							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(a__			,						a__		)


if __name__ == "__main__":
    main()












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 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      tempfile.mkdtemp()
														UpperCamelCase__					  =      8

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

														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""bart_tokenizer"""	)
														os.makedirs(__lowerCAmelCase	,			exist_ok=__lowerCAmelCase	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			BART_VOCAB_FILES_NAMES["""vocab_file"""]	)
														UpperCamelCase__					  =      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 					_lowerCamelCase		(    self	):
														return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,			"""dpr_tokenizer"""	)	)






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






							def 					_lowerCamelCase		(    self	):
														shutil.rmtree(self.tmpdirname	)






							@require_tokenizers
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""rag_tokenizer"""	)
														UpperCamelCase__					  =      RagConfig(question_encoder=DPRConfig().to_dict()	,			generator=BartConfig().to_dict()	)
														UpperCamelCase__					  =      RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,			generator=self.get_bart_tokenizer()	)
														rag_config.save_pretrained(__lowerCAmelCase	)
														rag_tokenizer.save_pretrained(__lowerCAmelCase	)
														UpperCamelCase__					  =      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 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RagTokenizer.from_pretrained("""facebook/rag-token-nq"""	)
														UpperCamelCase__					  =      [
														    """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""",
														]
														UpperCamelCase__					  =      tokenizer(__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""	)
														UpperCamelCase__					  =      [
														    """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""",
														]
														UpperCamelCase__					  =      tokenizer(__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)




