




"""simple docstring"""







import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
		from ...tokenization_utils_base import TextInput
from ...utils import logging


a      =							logging.get_logger(__name__)

a      =							{'vocab_file': 'spiece.model'}

a      =							{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a      =							{
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

a      =							'▁'






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						VOCAB_FILES_NAMES
						_a    						=						PRETRAINED_VOCAB_FILES_MAP
						_a    						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a    						=						['input_ids', 'attention_mask']


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Any="</s>"						,						lowerCAmelCase				:			List[Any]="<unk>"						,						lowerCAmelCase				:			Any="<pad>"						,						lowerCAmelCase				:			Union[str, Any]=100						,						lowerCAmelCase				:			Any=None						,						lowerCAmelCase				:			Optional[Dict[str, Any]] = None						,						lowerCAmelCase				:			Dict=True						,						**lowerCAmelCase				:			List[str]						,						):
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
														lowerCAmelCase  =				[f'''<extra_id_{i}>''' for i in range(lowerCAmelCase					)]
										elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra_id special tokens
														lowerCAmelCase  =				len(set(filter(lambda lowerCAmelCase					: bool("""extra_id""" in str(lowerCAmelCase					)					)						,						lowerCAmelCase					)					)					)
														if extra_tokens != extra_ids:
																		raise ValueError(
																		    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																		    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
																		    """ tokens"""					)
										if legacy:
														logger.warning_once(
														    f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
														    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""					)

										lowerCAmelCase  =				legacy
										lowerCAmelCase  =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=lowerCAmelCase						,						unk_token=lowerCAmelCase						,						pad_token=lowerCAmelCase						,						extra_ids=lowerCAmelCase						,						additional_special_tokens=lowerCAmelCase						,						sp_model_kwargs=self.sp_model_kwargs						,						legacy=lowerCAmelCase						,						**lowerCAmelCase						,						)

										lowerCAmelCase  =				vocab_file
										lowerCAmelCase  =				extra_ids

										lowerCAmelCase  =				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(lowerCAmelCase					)


						@staticmethod
						def   __lowercase (							lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			str					):
										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
														lowerCAmelCase  =				TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																		return init_max_model_length
														elif init_max_model_length is None:
																		warnings.warn(
																		    """This tokenizer was incorrectly instantiated with a model max length of"""
																		    f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
																		    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
																		    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
																		    f''' {pretrained_model_name_or_path} automatically truncating your input to'''
																		    f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
																		    f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
																		    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
																		    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""						,						lowerCAmelCase						,						)

										return max_model_length


						@property
						def   __lowercase (							self				:			List[str]					):
										return self.sp_model.get_piece_size() + self._extra_ids


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				{self.convert_ids_to_tokens(lowerCAmelCase					): i for i in range(self.vocab_size					)}
										vocab.update(self.added_tokens_encoder					)
										return vocab


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			List[int]						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			bool = False					):
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowerCAmelCase						,						token_ids_a=lowerCAmelCase						,						already_has_special_tokens=lowerCAmelCase					)

										# normal case: some special tokens
										if token_ids_a is None:
														return ([0] * len(lowerCAmelCase					)) + [1]
										return ([0] * len(lowerCAmelCase					)) + [1] + ([0] * len(lowerCAmelCase					)) + [1]


						def   __lowercase (							self				:			Any					):
										return list(
										    set(filter(lambda lowerCAmelCase					: bool(re.search(R"""<extra_id_\d+>"""						,						lowerCAmelCase					)					) is not None						,						self.additional_special_tokens					)					)					)


						def   __lowercase (							self				:			Optional[Any]					):
										return [self._convert_token_to_id(lowerCAmelCase					) for token in self.get_sentinel_tokens()]


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			List[int]					):
										if len(lowerCAmelCase					) > 0 and token_ids[-1] == self.eos_token_id:
														warnings.warn(
														    f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
														    """ eos tokens being added."""					)
														return token_ids
										else:
														return token_ids + [self.eos_token_id]


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			List[int]						,						lowerCAmelCase				:			Optional[List[int]] = None					):
										lowerCAmelCase  =				[self.eos_token_id]

										if token_ids_a is None:
														return len(token_ids_a + eos					) * [0]
										return len(token_ids_a + eos + token_ids_a + eos					) * [0]


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			List[int]						,						lowerCAmelCase				:			Optional[List[int]] = None					):
										lowerCAmelCase  =				self._add_eos_if_not_present(lowerCAmelCase					)
										if token_ids_a is None:
														return token_ids_a
										else:
														lowerCAmelCase  =				self._add_eos_if_not_present(lowerCAmelCase					)
														return token_ids_a + token_ids_a


						def __getstate__(							self				:			int					):
										lowerCAmelCase  =				self.__dict__.copy()
										lowerCAmelCase  =				None
										return state


						def __setstate__(							self				:			Optional[int]						,						lowerCAmelCase				:			Dict					):
										lowerCAmelCase  =				d

										# for backward compatibility
										if not hasattr(self						,						"""sp_model_kwargs"""					):
														lowerCAmelCase  =				{}

										lowerCAmelCase  =				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(self.vocab_file					)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			"TextInput"						,						**lowerCAmelCase				:			Tuple					):
										# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
										# the beginning of the text
										if not self.legacy:
														lowerCAmelCase  =				SPIECE_UNDERLINE + text.replace(lowerCAmelCase						,						""" """					)
										return super().tokenize(lowerCAmelCase						,						**lowerCAmelCase					)


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Dict						,						**lowerCAmelCase				:			List[str]					):
										if not self.legacy:
														lowerCAmelCase  =				text.startswith(lowerCAmelCase					)
														if is_first:
																		lowerCAmelCase  =				text[1:]

										lowerCAmelCase  =				self.sp_model.encode(lowerCAmelCase						,						out_type=lowerCAmelCase					)

										if not self.legacy and not is_first and not text.startswith(""" """					) and tokens[0].startswith(lowerCAmelCase					):
														lowerCAmelCase  =				([tokens[0][1:]] if len(tokens[0]					) > 1 else []) + tokens[1:]
										return tokens


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			Optional[Any]					):
										if token.startswith("""<extra_id_"""					):
														lowerCAmelCase  =				re.match(R"""<extra_id_(\d+)>"""						,						lowerCAmelCase					)
														lowerCAmelCase  =				int(match.group(1					)					)
														return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(lowerCAmelCase					)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			List[Any]					):
										if index < self.sp_model.get_piece_size():
														lowerCAmelCase  =				self.sp_model.IdToPiece(lowerCAmelCase					)
										else:
														lowerCAmelCase  =				f'''<extra_id_{self.vocab_size - 1 - index}>'''
										return token


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Any					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				""""""
										lowerCAmelCase  =				False
										for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																		if not prev_is_special:
																						out_string += " "
																		out_string += self.sp_model.decode(lowerCAmelCase					) + token
																		lowerCAmelCase  =				True
																		lowerCAmelCase  =				[]
														else:
																		current_sub_tokens.append(lowerCAmelCase					)
																		lowerCAmelCase  =				False
										out_string += self.sp_model.decode(lowerCAmelCase					)
										return out_string.strip()




						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[str] = None					):
										if not os.path.isdir(lowerCAmelCase					):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
														return
										lowerCAmelCase  =				os.path.join(
										    lowerCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

										if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					) and os.path.isfile(self.vocab_file					):
														copyfile(self.vocab_file						,						lowerCAmelCase					)
										elif not os.path.isfile(self.vocab_file					):
														with open(lowerCAmelCase						,						"""wb"""					) as fi:
																		lowerCAmelCase  =				self.sp_model.serialized_model_proto()
																		fi.write(lowerCAmelCase					)

										return (out_vocab_file,)











"""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()





