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


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				"open-llama"






   def __init__(      self				:     Tuple	,						lowercase_				:     str=10_00_00	,						lowercase_				:     Any=40_96	,						lowercase_				:     Dict=1_10_08	,						lowercase_				:     Union[str, Any]=32	,						lowercase_				:     int=32	,						lowercase_				:     Tuple="silu"	,						lowercase_				:     Any=20_48	,						lowercase_				:     int=0.02	,						lowercase_				:     Any=1E-6	,						lowercase_				:     int=True	,						lowercase_				:     Optional[Any]=0	,						lowercase_				:     Optional[Any]=1	,						lowercase_				:     Any=2	,						lowercase_				:     Any=False	,						lowercase_				:     int=True	,						lowercase_				:     Union[str, Any]=0.1	,						lowercase_				:     Optional[int]=0.1	,						lowercase_				:     Optional[int]=True	,						lowercase_				:     Union[str, Any]=True	,						lowercase_				:     Any=None	,						**lowercase_				:     Any	,						)					->						Union[str, Any]:
       lowercase__ :   Tuple    					=				vocab_size
       lowercase__ :   Optional[Any]    					=				max_position_embeddings
       lowercase__ :   Union[str, Any]    					=				hidden_size
       lowercase__ :   Any    					=				intermediate_size
       lowercase__ :   Union[str, Any]    					=				num_hidden_layers
       lowercase__ :   Dict    					=				num_attention_heads
       lowercase__ :   List[str]    					=				hidden_act
       lowercase__ :   Any    					=				initializer_range
       lowercase__ :   List[Any]    					=				rms_norm_eps
       lowercase__ :   Optional[int]    					=				use_cache
       lowercase__ :   Tuple    					=				kwargs.pop(
           "use_memorry_efficient_attention"	,						lowercase_  )
       lowercase__ :   Optional[Any]    					=				hidden_dropout_prob
       lowercase__ :   str    					=				attention_dropout_prob
       lowercase__ :   Tuple    					=				use_stable_embedding
       lowercase__ :   Dict    					=				shared_input_output_embedding
       lowercase__ :   Optional[Any]    					=				rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=lowercase_	,						bos_token_id=lowercase_	,						eos_token_id=lowercase_	,						tie_word_embeddings=lowercase_	,						**lowercase_	,						)







   def       __UpperCamelCase     (      self				:     Dict  )					->						int:
       if self.rope_scaling is None:
           return

       if not isinstance(self.rope_scaling	,						lowercase_  ) or len(self.rope_scaling  ) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               F'''got {self.rope_scaling}'''  )
       lowercase__ :   Any    					=				self.rope_scaling.get("type"	,						lowercase_  )
       lowercase__ :   Tuple    					=				self.rope_scaling.get("factor"	,						lowercase_  )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''  )
       if rope_scaling_factor is None or not isinstance(lowercase_	,						lowercase_  ) or rope_scaling_factor <= 1.0:
           raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''  )





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

__A				=1_6
__A				=3_2





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 1_6    ):
				lowerCamelCase_						=			AutoTokenizer.from_pretrained("bert-base-cased"    )
				lowerCamelCase_						=			load_dataset("glue"	, "mrpc"    )

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

				def collate_fn(lowerCamelCase__    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCamelCase_						=			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_						=			1_6
								elif accelerator.mixed_precision != "no":
												lowerCamelCase_						=			8
								else:
												lowerCamelCase_						=			None

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

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

				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

			__A				=mocked_dataloaders  # noqa: F811





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"	, lowerCamelCase__    ) == "1":
								lowerCamelCase_						=			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:
								lowerCamelCase_						=			Accelerator(
								    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with="all"	, project_dir=args.project_dir    )
				else:
								lowerCamelCase_						=			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase_						=			config["lr"]
				lowerCamelCase_						=			int(config["num_epochs"]    )
				lowerCamelCase_						=			int(config["seed"]    )
				lowerCamelCase_						=			int(config["batch_size"]    )
				set_seed(lowerCamelCase__    )

				lowerCamelCase_	,    lowerCamelCase_						=			get_dataloaders(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_						=			evaluate.load("glue"	, "mrpc"    )

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

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

				# 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_						=			model.to(accelerator.device    )

				# Instantiate optimizer
				lowerCamelCase_						=			AdamW(params=model.parameters()	, lr=lowerCamelCase__    )

				# Instantiate scheduler
				lowerCamelCase_						=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__    ) * 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_						=			accelerator.prepare(
				    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

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

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

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

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

								# 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(lowerCamelCase__    ),
												        "epoch": epoch,
												    }	, step=lowerCamelCase__	, )

    # 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 			lowerCamelCase_ (				):
				lowerCamelCase_						=			argparse.ArgumentParser(description="Simple example of training script."    )
				parser.add_argument(
				    "--mixed_precision"	, type=lowerCamelCase__	, default=lowerCamelCase__	, 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=lowerCamelCase__	, default="logs"	, help="Location on where to store experiment tracking logs` and relevent project information"	, )
				lowerCamelCase_						=			parser.parse_args()
				lowerCamelCase_						=			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			main()


