

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
#
########################################################################


_UpperCamelCase 			=		16
_UpperCamelCase 			=		32







def 		_lowercase (     lowercase__   ,	lowercase__ = 1_6						):
      __lowerCAmelCase					:						List[str]					=					AutoTokenizer.from_pretrained('''bert-base-cased'''						)
      __lowerCAmelCase					:						Tuple					=					load_dataset('''glue'''   ,	'''mrpc'''						)

      def tokenize_function(lowercase__						):
            # max_length=None => use the model max length (it's actually the default)
            __lowerCAmelCase					:						List[str]					=					tokenizer(examples['''sentence1''']   ,	examples['''sentence2''']   ,	truncation=lowercase__   ,	max_length=lowercase__						)
            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					:						Optional[int]					=					datasets.map(
                lowercase__   ,	batched=lowercase__   ,	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					:						str					=					tokenized_datasets.rename_column('''label'''   ,	'''labels'''						)

      def collate_fn(lowercase__						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            __lowerCAmelCase					:						List[Any]					=					1_2_8 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					:						Union[str, Any]					=					1_6
            elif accelerator.mixed_precision != "no":
                  __lowerCAmelCase					:						str					=					8
            else:
                  __lowerCAmelCase					:						Optional[Any]					=					None

            return tokenizer.pad(
                lowercase__   ,	padding='''longest'''   ,	max_length=lowercase__   ,	pad_to_multiple_of=lowercase__   ,	return_tensors='''pt'''   ,	)

      # Instantiate dataloaders.
      __lowerCAmelCase					:						Optional[int]					=					DataLoader(
          tokenized_datasets['''train''']   ,	shuffle=lowercase__   ,	collate_fn=lowercase__   ,	batch_size=lowercase__   ,	drop_last=lowercase__						)
      __lowerCAmelCase					:						int					=					DataLoader(
          tokenized_datasets['''validation''']   ,	shuffle=lowercase__   ,	collate_fn=lowercase__   ,	batch_size=lowercase__   ,	drop_last=(accelerator.mixed_precision == '''fp8''')   ,	)

      return train_dataloader, eval_dataloader







def 		_lowercase (     lowercase__   ,	lowercase__						):
      # Initialize accelerator
      __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					:						Optional[int]					=					config['''lr''']
      __lowerCAmelCase					:						List[str]					=					int(config['''num_epochs''']						)
      __lowerCAmelCase					:						Any					=					int(config['''seed''']						)
      __lowerCAmelCase					:						Optional[int]					=					int(config['''batch_size''']						)

      __lowerCAmelCase					:						List[Any]					=					evaluate.load('''glue'''   ,	'''mrpc'''						)

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

      set_seed(lowercase__						)
      __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					get_dataloaders(lowercase__   ,	lowercase__						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      __lowerCAmelCase					:						Union[str, Any]					=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,	return_dict=lowercase__						)

      # 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					:						Dict					=					model.to(accelerator.device						)
      # Instantiate optimizer
      __lowerCAmelCase					:						Dict					=					AdamW(params=model.parameters()   ,	lr=lowercase__						)

      # Instantiate scheduler
      __lowerCAmelCase					:						List[Any]					=					get_linear_schedule_with_warmup(
          optimizer=lowercase__   ,	num_warmup_steps=1_0_0   ,	num_training_steps=(len(lowercase__						) * 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,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					accelerator.prepare(
          lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)

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

            model.eval()
            for step, batch in enumerate(lowercase__						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  with torch.no_grad():
                        __lowerCAmelCase					:						int					=					model(**lowercase__						)
                  __lowerCAmelCase					:						Optional[int]					=					outputs.logits.argmax(dim=-1						)
                  __lowerCAmelCase,							__lowerCAmelCase					:						Union[str, Any]					=					accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                  metric.add_batch(
                      predictions=lowercase__   ,	references=lowercase__   ,	)

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







def 		_lowercase (     ):
      __lowerCAmelCase					:						Any					=					argparse.ArgumentParser(description='''Simple example of training script.'''						)
      parser.add_argument(
          '''--mixed_precision'''   ,	type=lowercase__   ,	default=lowercase__   ,	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					:						Dict					=					parser.parse_args()
      __lowerCAmelCase					:						Optional[Any]					=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(lowercase__   ,	lowercase__						)


if __name__ == "__main__":
 main()





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							padding=self.padding      ,							max_length=self.max_length      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors='''pt''' if labels is None else None      ,							)

       if labels is None:
             return batch

       __lowerCAmelCase					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


