






import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_UpperCAmelCase			:		Optional[int]   				=    16
_UpperCAmelCase			:		Union[str, Any]   				=    32





def 	lowerCAmelCase_		(lowercase__						:       Accelerator     ,					lowercase__						:       int = 16       )		->    Any:


				'''simple docstring'''




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

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

				def collate_fn(lowercase__						:       Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase__      			=						1_28 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__      			=						16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase__      			=						8
								else:
												lowerCAmelCase__      			=						None

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

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

				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

			_UpperCAmelCase			:		List[Any]   				=    mocked_dataloaders  # noqa: F811





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Any       )		->    Dict:


				'''simple docstring'''




				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,					lowercase__       ) == "1":
								lowerCAmelCase__      			=						2
				# Initialize accelerator
				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''']       )

				lowerCAmelCase__      			=						evaluate.load('''glue'''     ,					'''mrpc'''       )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=lowercase__       )
				def inner_training_loop(lowercase__						:       Tuple       ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(lowercase__       )

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

								# Instantiate optimizer
								lowerCAmelCase__      			=						AdamW(params=model.parameters()     ,					lr=lowercase__       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						get_dataloaders(lowercase__     ,					lowercase__       )

								# Instantiate scheduler
								lowerCAmelCase__      			=						get_linear_schedule_with_warmup(
								    optimizer=lowercase__     ,					num_warmup_steps=1_00     ,					num_training_steps=(len(lowercase__       ) * num_epochs)     ,					)

								# 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(
								    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__      			=						model(**lowercase__       )
																lowerCAmelCase__      			=						outputs.loss
																accelerator.backward(lowercase__       )
																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__      			=						model(**lowercase__       )
																lowerCAmelCase__      			=						outputs.logits.argmax(dim=-1       )
																lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
																metric.add_batch(
																    predictions=lowercase__     ,					references=lowercase__     ,					)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()





def 	lowerCAmelCase_		()		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						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__      			=						parser.parse_args()
				lowerCAmelCase__      			=						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase__     ,					lowercase__       )


if __name__ == "__main__":
			main()













from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase			:		Tuple   				=    logging.get_logger(__name__)


_UpperCAmelCase			:		int   				=    {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:int		      =    'codegen'
							UpperCamelCase_		:int		      =    {
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}




							def __init__(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   str=50_400 ,							SCREAMING_SNAKE_CASE_     :   str=2_048 ,							SCREAMING_SNAKE_CASE_     :   int=2_048 ,							SCREAMING_SNAKE_CASE_     :   Any=4_096 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=28 ,							SCREAMING_SNAKE_CASE_     :   str=16 ,							SCREAMING_SNAKE_CASE_     :   str=64 ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict="gelu_new" ,							SCREAMING_SNAKE_CASE_     :   str=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Any=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=50_256 ,							SCREAMING_SNAKE_CASE_     :   Any=50_256 ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						n_ctx
											lowerCAmelCase__      			=						n_positions
											lowerCAmelCase__      			=						n_embd
											lowerCAmelCase__      			=						n_layer
											lowerCAmelCase__      			=						n_head
											lowerCAmelCase__      			=						n_inner
											lowerCAmelCase__      			=						rotary_dim
											lowerCAmelCase__      			=						activation_function
											lowerCAmelCase__      			=						resid_pdrop
											lowerCAmelCase__      			=						embd_pdrop
											lowerCAmelCase__      			=						attn_pdrop
											lowerCAmelCase__      			=						layer_norm_epsilon
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_cache

											lowerCAmelCase__      			=						bos_token_id
											lowerCAmelCase__      			=						eos_token_id

											super().__init__(
											    bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)



class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   PretrainedConfig ,							SCREAMING_SNAKE_CASE_     :   str = "default" ,							SCREAMING_SNAKE_CASE_     :   List[PatchingSpec] = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							):
											super().__init__(SCREAMING_SNAKE_CASE_ ,							task=SCREAMING_SNAKE_CASE_ ,							patching_specs=SCREAMING_SNAKE_CASE_ ,							use_past=SCREAMING_SNAKE_CASE_	)
											if not getattr(self._config ,							'''pad_token_id''' ,							SCREAMING_SNAKE_CASE_	):
															# TODO: how to do that better?
															lowerCAmelCase__      			=						0




							@property
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
											if self.use_past:
															self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ ,							direction='''inputs'''	)
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''sequence'''}

											return common_inputs




							@property
							def 						__snake_case					(   self     :   Dict	):
											return self._config.n_layer




							@property
							def 						__snake_case					(   self     :   Union[str, Any]	):
											return self._config.n_head




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   PreTrainedTokenizer ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   Optional[TensorType] = None ,							):
											lowerCAmelCase__      			=						super(SCREAMING_SNAKE_CASE_ ,							self	).generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE_ ,							batch_size=SCREAMING_SNAKE_CASE_ ,							seq_length=SCREAMING_SNAKE_CASE_ ,							is_pair=SCREAMING_SNAKE_CASE_ ,							framework=SCREAMING_SNAKE_CASE_	)

											# We need to order the input in the way they appears in the forward()
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
															else:
																			import torch

																			lowerCAmelCase__			,		lowerCAmelCase__      			=						common_inputs['''input_ids'''].shape
																			# Not using the same length for past_key_values
																			lowerCAmelCase__      			=						seqlen + 2
																			lowerCAmelCase__      			=						(
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			lowerCAmelCase__      			=						[
																			    (torch.zeros(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) for _ in range(self.num_layers	)
																			]

											lowerCAmelCase__      			=						common_inputs['''attention_mask''']
											if self.use_past:
															lowerCAmelCase__      			=						ordered_inputs['''attention_mask'''].dtype
															lowerCAmelCase__      			=						torch.cat(
															    [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)] ,							dim=1	)

											return ordered_inputs




							@property
							def 						__snake_case					(   self     :   Optional[int]	):
											return 13





