


'''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
#
########################################################################

_lowercase      :      List[str]            =					16
_lowercase      :      Dict            =					32


def 				lowerCamelCase__				(	A       :      Accelerator				,       A       :      int = 16					):




						'''simple docstring'''






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

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

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

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

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

						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

	_lowercase      :      Dict            =					mocked_dataloaders  # noqa: F811


def 				lowerCamelCase__				(	A       :      str				,       A       :      Tuple					):




						'''simple docstring'''






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

						UpperCAmelCase    ,    UpperCAmelCase					    =     get_dataloaders(A				,       A					)
						UpperCAmelCase					    =     evaluate.load('''glue'''				,       '''mrpc'''					)

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

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

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

						# Instantiate optimizer
						UpperCAmelCase					    =     AdamW(params=model.parameters()				,       lr=A					)

						# Instantiate scheduler
						UpperCAmelCase					    =     get_linear_schedule_with_warmup(
						    optimizer=A				,       num_warmup_steps=1_00				,       num_training_steps=(len(A					) * 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.
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     accelerator.prepare(
						    A				,       A				,       A				,       A				,       A					)

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

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

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

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

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

    # 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__				(	):




						'''simple docstring'''






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


if __name__ == "__main__":
	main()









'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_lowercase      :      Optional[Any]            =					logging.get_logger(__name__)

_lowercase      :      List[str]            =					{"""tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Optional[Any]            =					{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[str]  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Optional[int]  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	["input_ids", "attention_mask"]
				__magic_name__    :				Optional[int]  		=	None




				def __init__(		self      :			List[Any]				,   lowerCAmelCase      :			str=None				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			str=None				,   lowerCAmelCase      :			Optional[int]="<unk>"				,   lowerCAmelCase      :			int="<s>"				,   lowerCAmelCase      :			Dict="</s>"				,   lowerCAmelCase      :			Union[str, Any]="<pad>"				,   lowerCAmelCase      :			int=False				,   lowerCAmelCase      :			Optional[int]=False				,   **lowerCAmelCase      :			Optional[int]				,   )->					Union[str, Any]:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase				,   lowerCAmelCase				,   tokenizer_file=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   pad_token=lowerCAmelCase				,   add_prefix_space=lowerCAmelCase				,   clean_up_tokenization_spaces=lowerCAmelCase				,   **lowerCAmelCase				,   )
										UpperCAmelCase					    =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get('''add_prefix_space'''				,   lowerCAmelCase				) != add_prefix_space:
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   pre_tok_state.pop('''type'''				)				)
																UpperCAmelCase					    =     add_prefix_space
																UpperCAmelCase					    =     pre_tok_class(**lowerCAmelCase				)

										UpperCAmelCase					    =     add_prefix_space




				def 					a__(		self      :			str				,   *lowerCAmelCase      :			Dict				,   **lowerCAmelCase      :			List[str]				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)
										if not (self.add_prefix_space or not is_split_into_words):
																raise Exception(
																    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
																    ''' pretokenized inputs.'''				)

										return super()._batch_encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Union[str, Any]				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)

										if not (self.add_prefix_space or not is_split_into_words):
																raise Exception(
																    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
																    ''' pretokenized inputs.'''				)

										return super()._encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				) + [self.eos_token_id]				)

										if len(lowerCAmelCase				) > self.model_max_length:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





