"""simple docstring"""




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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ = 16 ):






							"""simple docstring"""





							A__		   =							AutoTokenizer.from_pretrained('bert-base-cased' )
							A__		   =							load_dataset('glue'				,			'mrpc' )

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

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

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

							# Instantiate dataloaders.
							A__		   =							DataLoader(
							    tokenized_datasets['train']				,			shuffle=SCREAMING_SNAKE_CASE__				,			collate_fn=SCREAMING_SNAKE_CASE__				,			batch_size=SCREAMING_SNAKE_CASE__ )
							A__		   =							DataLoader(
							    tokenized_datasets['validation']				,			shuffle=SCREAMING_SNAKE_CASE__				,			collate_fn=SCREAMING_SNAKE_CASE__				,			batch_size=SCREAMING_SNAKE_CASE__ )

							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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							if os.environ.get('TESTING_MOCKED_DATALOADERS'				,			SCREAMING_SNAKE_CASE__ ) == "1":
														A__		   =							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:
														A__		   =							Accelerator(
														    cpu=args.cpu				,			mixed_precision=args.mixed_precision				,			log_with='all'				,			project_dir=args.project_dir )
							else:
														A__		   =							Accelerator(cpu=args.cpu				,			mixed_precision=args.mixed_precision )
							# 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'] )
							set_seed(SCREAMING_SNAKE_CASE__ )

							A__		   =							get_dataloaders(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							A__		   =							evaluate.load('glue'				,			'mrpc' )

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

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__		   =							AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,			return_dict=SCREAMING_SNAKE_CASE__ )

							# 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).
							A__		   =							model.to(accelerator.device )

							# Instantiate optimizer
							A__		   =							AdamW(params=model.parameters()				,			lr=SCREAMING_SNAKE_CASE__ )

							# Instantiate scheduler
							A__		   =							get_linear_schedule_with_warmup(
							    optimizer=SCREAMING_SNAKE_CASE__				,			num_warmup_steps=100				,			num_training_steps=(len(SCREAMING_SNAKE_CASE__ ) * 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.
							A__		   =							accelerator.prepare(
							    SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )

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

							# Now we train the model
							for epoch in range(SCREAMING_SNAKE_CASE__ ):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					A__		   =							0
														for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device )
																					A__		   =							model(**SCREAMING_SNAKE_CASE__ )
																					A__		   =							outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					A__		   =							loss / gradient_accumulation_steps
																					accelerator.backward(SCREAMING_SNAKE_CASE__ )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

														A__		   =							metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''				,			SCREAMING_SNAKE_CASE__ )

														# 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(SCREAMING_SNAKE_CASE__ ),
																					        'epoch': epoch,
																					    }				,			step=SCREAMING_SNAKE_CASE__				,			)

    # 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      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							argparse.ArgumentParser(description='Simple example of training script.' )
							parser.add_argument(
							    '--mixed_precision'				,			type=SCREAMING_SNAKE_CASE__				,			default=SCREAMING_SNAKE_CASE__				,			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=SCREAMING_SNAKE_CASE__				,			default='logs'				,			help='Location on where to store experiment tracking logs` and relevent project information'				,			)
							A__		   =							parser.parse_args()
							A__		   =							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
				main()









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='transfo-xl'
      UpperCamelCase__							:  int      =['mems']
      UpperCamelCase__							:  List[str]      ={
          'n_token': 'vocab_size',
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(					self						:     Tuple			,   lowercase_						:     List[Any]=26_7735			,   lowercase_						:     List[Any]=[2_0000, 4_0000, 20_0000]			,   lowercase_						:     Tuple=1024			,   lowercase_						:     Optional[int]=1024			,   lowercase_						:     str=16			,   lowercase_						:     int=64			,   lowercase_						:     Any=4096			,   lowercase_						:     List[Any]=4			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=18			,   lowercase_						:     Tuple=1600			,   lowercase_						:     List[str]=1000			,   lowercase_						:     Any=True			,   lowercase_						:     str=True			,   lowercase_						:     Optional[int]=0			,   lowercase_						:     List[Any]=-1			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Any=0.1			,   lowercase_						:     Tuple=0.0			,   lowercase_						:     str=True			,   lowercase_						:     Union[str, Any]="normal"			,   lowercase_						:     Dict=0.01			,   lowercase_						:     Optional[Any]=0.01			,   lowercase_						:     List[Any]=0.02			,   lowercase_						:     List[Any]=1E-5			,   lowercase_						:     Tuple=0			,   **lowercase_						:     Tuple			,   )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Optional[Any]		=[]
             self.cutoffs.extend(lowercase_ )
             if proj_share_all_but_first:
                    _lowerCamelCase       :		List[str]		=[False] + [True] * len(self.cutoffs )
             else:
                    _lowerCamelCase       :		str		=[False] + [False] * len(self.cutoffs )
             _lowerCamelCase       :		str		=d_model
             _lowerCamelCase       :		str		=d_embed
             _lowerCamelCase       :		str		=d_head
             _lowerCamelCase       :		Dict		=d_inner
             _lowerCamelCase       :		Union[str, Any]		=div_val
             _lowerCamelCase       :		Dict		=pre_lnorm
             _lowerCamelCase       :		Tuple		=n_layer
             _lowerCamelCase       :		Optional[Any]		=n_head
             _lowerCamelCase       :		List[Any]		=mem_len
             _lowerCamelCase       :		List[Any]		=same_length
             _lowerCamelCase       :		Optional[int]		=attn_type
             _lowerCamelCase       :		List[str]		=clamp_len
             _lowerCamelCase       :		int		=sample_softmax
             _lowerCamelCase       :		List[Any]		=adaptive
             _lowerCamelCase       :		List[str]		=dropout
             _lowerCamelCase       :		Any		=dropatt
             _lowerCamelCase       :		Dict		=untie_r
             _lowerCamelCase       :		List[Any]		=init
             _lowerCamelCase       :		List[str]		=init_range
             _lowerCamelCase       :		int		=proj_init_std
             _lowerCamelCase       :		Optional[int]		=init_std
             _lowerCamelCase       :		Tuple		=layer_norm_epsilon
             super().__init__(eos_token_id=lowercase_			,   **lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
             return -1






      @max_position_embeddings.setter
      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




             raise NotImplementedError(
                 F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


