


import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case             =     16
_snake_case             =     32






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = 16	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				AutoTokenizer.from_pretrained("bert-base-cased"	)
  lowerCamelCase :					List[Any]    =				load_dataset("glue"      ,						"mrpc"	)

  def tokenize_function(SCREAMING_SNAKE_CASE_	):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase :					Any    =				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
  # starting with the main process first:
  with accelerator.main_process_first():
    lowerCamelCase :					Tuple    =				datasets.map(
        _UpperCAmelCase      ,						batched=_UpperCAmelCase      ,						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
  lowerCamelCase :					Union[str, Any]    =				tokenized_datasets.rename_column("label"      ,						"labels"	)

  def collate_fn(SCREAMING_SNAKE_CASE_	):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCamelCase :					List[str]    =				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":
      lowerCamelCase :					List[Any]    =				16
    elif accelerator.mixed_precision != "no":
      lowerCamelCase :					Optional[Any]    =				8
    else:
      lowerCamelCase :					List[str]    =				None

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

  # Instantiate dataloaders.
  lowerCamelCase :					Dict    =				DataLoader(
      tokenized_datasets["train"]      ,						shuffle=_UpperCAmelCase      ,						collate_fn=_UpperCAmelCase      ,						batch_size=_UpperCAmelCase      ,						drop_last=_UpperCAmelCase	)
  lowerCamelCase :					Union[str, Any]    =				DataLoader(
      tokenized_datasets["validation"]      ,						shuffle=_UpperCAmelCase      ,						collate_fn=_UpperCAmelCase      ,						batch_size=_UpperCAmelCase      ,						drop_last=(accelerator.mixed_precision == "fp8")      ,						)

  return train_dataloader, eval_dataloader






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					str    =				Accelerator(cpu=args.cpu      ,						mixed_precision=args.mixed_precision	)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase :					int    =				config["lr"]
  lowerCamelCase :					Any    =				int(config["num_epochs"]	)
  lowerCamelCase :					Optional[int]    =				int(config["seed"]	)
  lowerCamelCase :					List[Any]    =				int(config["batch_size"]	)

  lowerCamelCase :					List[str]    =				evaluate.load("glue"      ,						"mrpc"	)

  # If the batch size is too big we use gradient accumulation
  lowerCamelCase :					Optional[int]    =				1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowerCamelCase :					Tuple    =				batch_size // MAX_GPU_BATCH_SIZE
    lowerCamelCase :					Dict    =				MAX_GPU_BATCH_SIZE

  set_seed(_UpperCAmelCase	)
  lowerCamelCase :					str    =				get_dataloaders(_UpperCAmelCase      ,						_UpperCAmelCase	)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase :					List[Any]    =				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"      ,						return_dict=_UpperCAmelCase	)

  # 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).
  lowerCamelCase :					Tuple    =				model.to(accelerator.device	)
  # Instantiate optimizer
  lowerCamelCase :					Optional[int]    =				AdamW(params=model.parameters()      ,						lr=_UpperCAmelCase	)

  # Instantiate scheduler
  lowerCamelCase :					Optional[int]    =				get_linear_schedule_with_warmup(
      optimizer=_UpperCAmelCase      ,						num_warmup_steps=100      ,						num_training_steps=(len(_UpperCAmelCase	) * 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.

  lowerCamelCase :					Optional[int]    =				accelerator.prepare(
      _UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)

  # Now we train the model
  for epoch in range(_UpperCAmelCase	):
    model.train()
    for step, batch in enumerate(_UpperCAmelCase	):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device	)
      lowerCamelCase :					Tuple    =				model(**_UpperCAmelCase	)
      lowerCamelCase :					Optional[int]    =				outputs.loss
      lowerCamelCase :					Tuple    =				loss / gradient_accumulation_steps
      accelerator.backward(_UpperCAmelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    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():
        lowerCamelCase :					Optional[int]    =				model(**_UpperCAmelCase	)
      lowerCamelCase :					int    =				outputs.logits.argmax(dim=-1	)
      lowerCamelCase :					int    =				accelerator.gather_for_metrics((predictions, batch["labels"])	)
      metric.add_batch(
          predictions=_UpperCAmelCase      ,						references=_UpperCAmelCase      ,						)

    lowerCamelCase :					List[Any]    =				metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:"""      ,						_UpperCAmelCase	)






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Any    =				argparse.ArgumentParser(description="Simple example of training script."	)
  parser.add_argument(
      "--mixed_precision"      ,						type=_UpperCAmelCase      ,						default=_UpperCAmelCase      ,						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."	)
  lowerCamelCase :					Optional[Any]    =				parser.parse_args()
  lowerCamelCase :					Optional[int]    =				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(_UpperCAmelCase      ,						_UpperCAmelCase	)


if __name__ == "__main__":
   main()






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:  Optional[Any]   	=						get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     XGLMTokenizer
			_UpperCAmelCase   :							List[Any]										=     XGLMTokenizerFast
			_UpperCAmelCase   :							Optional[int]										=     True
			_UpperCAmelCase   :							Tuple										=     True



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE_: List[Any]  		=       XGLMTokenizer(lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__)
									tokenizer.save_pretrained(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: int  		=       1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__)   ,						lowerCAmelCase__)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__)   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       list(self.get_tokenizer().get_vocab().keys())

									self.assertEqual(vocab_keys[0]   ,						"<s>")
									self.assertEqual(vocab_keys[1]   ,						"<pad>")
									self.assertEqual(len(lowerCAmelCase__)   ,						1008)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									self.assertEqual(self.get_tokenizer().vocab_size   ,						1008)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       XGLMTokenizer(lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__   ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)   ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,						)

									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]   ,						)



			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									return XGLMTokenizer.from_pretrained("facebook/xglm-564M")



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(lowerCAmelCase__   ,						f.name)
															SCREAMING_SNAKE_CASE_: Tuple  		=       XGLMTokenizer(f.name   ,						keep_accents=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: List[str]  		=       pickle.dumps(lowerCAmelCase__)
									pickle.loads(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									if not self.test_rust_tokenizer:
															return

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_: Any  		=       "I was born in 92000, and this is falsé."

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenizer.tokenize(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       rust_tokenizer.tokenize(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       rust_tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_: str  		=       tokenizer.encode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       rust_tokenizer.encode(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       "Hello World!"
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [2, 3_1227, 4447, 35]

									self.assertListEqual(lowerCAmelCase__   ,						self.big_tokenizer.encode(lowerCAmelCase__))



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (
									    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
									    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
									)
									# fmt: off
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
									# fmt: on

									self.assertListEqual(lowerCAmelCase__   ,						self.big_tokenizer.encode(lowerCAmelCase__))




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									# fmt: off
									SCREAMING_SNAKE_CASE_: str  		=       {
									    "input_ids": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
									    "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
									}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCAmelCase__   ,						model_name="facebook/xglm-564M"   ,						padding=lowerCAmelCase__   ,						)






