




"""simple docstring"""







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


a      =							logging.get_logger(__name__)


a      =							{
    '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       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'codegen'
						_a    						=						{
						    'max_position_embeddings': 'n_positions',
						    'hidden_size': 'n_embd',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			int=5_0400						,						lowerCAmelCase				:			Any=2048						,						lowerCAmelCase				:			Optional[Any]=2048						,						lowerCAmelCase				:			Dict=4096						,						lowerCAmelCase				:			Optional[int]=28						,						lowerCAmelCase				:			Tuple=16						,						lowerCAmelCase				:			Union[str, Any]=64						,						lowerCAmelCase				:			Union[str, Any]=None						,						lowerCAmelCase				:			Optional[Any]="gelu_new"						,						lowerCAmelCase				:			Tuple=0.0						,						lowerCAmelCase				:			Any=0.0						,						lowerCAmelCase				:			Tuple=0.0						,						lowerCAmelCase				:			Any=1e-5						,						lowerCAmelCase				:			Optional[int]=0.02						,						lowerCAmelCase				:			List[str]=True						,						lowerCAmelCase				:			Any=5_0256						,						lowerCAmelCase				:			Optional[Any]=5_0256						,						lowerCAmelCase				:			List[str]=False						,						**lowerCAmelCase				:			int						,						):
										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=lowerCAmelCase						,						eos_token_id=lowerCAmelCase						,						tie_word_embeddings=lowerCAmelCase						,						**lowerCAmelCase					)






class       SCREAMING_SNAKE_CASE__					(					_a					):


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


						@property
						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}					)
										if self.use_past:
														self.fill_with_past_key_values_(lowerCAmelCase						,						direction="""inputs"""					)
														lowerCAmelCase  =				{0: """batch""", 1: """past_sequence + sequence"""}
										else:
														lowerCAmelCase  =				{0: """batch""", 1: """sequence"""}

										return common_inputs


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


						@property
						def   __lowercase (							self				:			List[Any]					):
										return self._config.n_head


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

										# 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(lowerCAmelCase					), torch.zeros(lowerCAmelCase					)) 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(lowerCAmelCase						,						lowerCAmelCase						,						dtype=lowerCAmelCase					)]						,						dim=1					)

										return ordered_inputs




						@property
						def   __lowercase (							self				:			Optional[Any]					):
										return 13











"""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
#
########################################################################

a      =							1_6
a      =							3_2





def 							lowercase					(snake_case__   : Accelerator      ,       snake_case__   : int = 16 )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCAmelCase  =				load_dataset("""glue"""      ,       """mrpc""" )

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

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

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

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

		a      =							mocked_dataloaders  # noqa: F811





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       snake_case__ ) == "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(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				get_dataloaders(snake_case__      ,       snake_case__ )
				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=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).
				lowerCAmelCase  =				model.to(accelerator.device )

				# Instantiate optimizer
				lowerCAmelCase  =				AdamW(params=model.parameters()      ,       lr=snake_case__ )

				# Instantiate scheduler
				lowerCAmelCase  =				get_linear_schedule_with_warmup(
				    optimizer=snake_case__      ,       num_warmup_steps=100      ,       num_training_steps=(len(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.
				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				accelerator.prepare(
				    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )

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

				# Now we train the model
				for epoch in range(snake_case__ ):
								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(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase  =				loss / gradient_accumulation_steps
												accelerator.backward(snake_case__ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(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():
																lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.logits.argmax(dim=-1 )
												lowerCAmelCase ,						lowerCAmelCase  =				accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=snake_case__      ,       references=snake_case__      ,       )

								lowerCAmelCase  =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''      ,       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(snake_case__ ),
												        """epoch""": epoch,
												    }      ,       step=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 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""      ,       type=snake_case__      ,       default=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=snake_case__      ,       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""": 42, """batch_size""": 16}
				training_function(snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		main()





