
import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


A		: Dict								=     logging.get_logger(__name__)


A		: List[str]								=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A		: Any								=     {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

A		: List[Any]								=     {
    '''allenai/led-base-16384''': 16_384,
}



class    a_ (			_SCREAMING_SNAKE_CASE    ):
			a	:			Dict					   =							VOCAB_FILES_NAMES
			a	:			Dict					   =							PRETRAINED_VOCAB_FILES_MAP
			a	:			int					   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a	:			Union[str, Any]					   =							LEDTokenizer
			a	:			Any					   =							["""input_ids""", """attention_mask"""]






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase="replace"				,			__UpperCamelCase="<s>"				,			__UpperCamelCase="</s>"				,			__UpperCamelCase="</s>"				,			__UpperCamelCase="<s>"				,			__UpperCamelCase="<unk>"				,			__UpperCamelCase="<pad>"				,			__UpperCamelCase="<mask>"				,			__UpperCamelCase=False				,			__UpperCamelCase=True				,			**__UpperCamelCase				,			):
									super().__init__(
									    lowerCAmelCase_				,			lowerCAmelCase_				,			tokenizer_file=lowerCAmelCase_				,			errors=lowerCAmelCase_				,			bos_token=lowerCAmelCase_				,			eos_token=lowerCAmelCase_				,			sep_token=lowerCAmelCase_				,			cls_token=lowerCAmelCase_				,			unk_token=lowerCAmelCase_				,			pad_token=lowerCAmelCase_				,			mask_token=lowerCAmelCase_				,			add_prefix_space=lowerCAmelCase_				,			trim_offsets=lowerCAmelCase_				,			**lowerCAmelCase_				,			)

									_lowercase					       =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
									if pre_tok_state.get("""add_prefix_space"""				,			lowerCAmelCase_  ) != add_prefix_space:
															_lowercase					       =					getattr(lowerCAmelCase_				,			pre_tok_state.pop("""type"""  )  )
															_lowercase					       =					add_prefix_space
															_lowercase					       =					pre_tok_class(**lowerCAmelCase_  )

									_lowercase					       =					add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									_lowercase					       =					"""post_processor"""
									_lowercase					       =					getattr(self.backend_tokenizer				,			lowerCAmelCase_				,			lowerCAmelCase_  )
									if tokenizer_component_instance:
															_lowercase					       =					json.loads(tokenizer_component_instance.__getstate__()  )

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																					_lowercase					       =					tuple(state["""sep"""]  )
															if "cls" in state:
																					_lowercase					       =					tuple(state["""cls"""]  )

															_lowercase					       =					False

															if state.get("""add_prefix_space"""				,			lowerCAmelCase_  ) != add_prefix_space:
																					_lowercase					       =					add_prefix_space
																					_lowercase					       =					True

															if state.get("""trim_offsets"""				,			lowerCAmelCase_  ) != trim_offsets:
																					_lowercase					       =					trim_offsets
																					_lowercase					       =					True

															if changes_to_apply:
																					_lowercase					       =					getattr(lowerCAmelCase_				,			state.pop("""type"""  )  )
																					_lowercase					       =					component_class(**lowerCAmelCase_  )
																					setattr(self.backend_tokenizer				,			lowerCAmelCase_				,			lowerCAmelCase_  )






			@property
			# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
			def    UpperCamelCase_				(      self  ):
									if self._mask_token is None:
															if self.verbose:
																					logger.error("""Using mask_token, but it is not set yet."""  )
															return None
									return str(self._mask_token  )






			@mask_token.setter
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					AddedToken(lowerCAmelCase_				,			lstrip=lowerCAmelCase_				,			rstrip=lowerCAmelCase_  ) if isinstance(lowerCAmelCase_				,			lowerCAmelCase_  ) else value
									_lowercase					       =					value






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.get("""is_split_into_words"""				,			lowerCAmelCase_  )

									if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    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    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.get("""is_split_into_words"""				,			lowerCAmelCase_  )

									if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									_lowercase					       =					self._tokenizer.model.save(lowerCAmelCase_				,			name=lowerCAmelCase_  )
									return tuple(lowerCAmelCase_  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase=None  ):
									_lowercase					       =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									_lowercase					       =					[self.sep_token_id]
									_lowercase					       =					[self.cls_token_id]

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None				,			__UpperCamelCase = PaddingStrategy.DO_NOT_PAD				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			):
									_lowercase					       =					super()._pad(
									    encoded_inputs=lowerCAmelCase_				,			max_length=lowerCAmelCase_				,			padding_strategy=lowerCAmelCase_				,			pad_to_multiple_of=lowerCAmelCase_				,			return_attention_mask=lowerCAmelCase_				,			)

									# Load from model defaults
									if return_attention_mask is None:
															_lowercase					       =					"""attention_mask""" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
															_lowercase					       =					encoded_inputs[self.model_input_names[0]]
															# `global_attention_mask` need to have the same length as other (sequential) inputs.
															_lowercase					       =					len(encoded_inputs["""global_attention_mask"""]  ) != len(lowerCAmelCase_  )

															if needs_to_be_padded:
																					_lowercase					       =					len(lowerCAmelCase_  ) - len(encoded_inputs["""global_attention_mask"""]  )

																					if self.padding_side == "right":
																											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																											_lowercase					       =					(
																											    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																											)
																					elif self.padding_side == "left":
																											_lowercase					       =					[-1] * difference + encoded_inputs[
																											    """global_attention_mask"""
																											]
																					else:
																											raise ValueError("""Invalid padding strategy:""" + str(self.padding_side  )  )

									return encoded_inputs







"""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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCAmelCase       =				16
_lowerCAmelCase       =				32






def       __UpperCamelCase						(		snake_case__ , snake_case__ = 16       ):
	A_		:    List[str]						     = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	A_		:    Optional[Any]						     = load_dataset("""glue""" , """mrpc"""       )

	def tokenize_function(snake_case__       ):
		# max_length=None => use the model max length (it's actually the default)
		A_		:    Tuple						     = 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():
		A_		:    str						     = 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
	A_		:    int						     = tokenized_datasets.rename_column("""label""" , """labels"""       )

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

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

	# Instantiate dataloaders.
	A_		:    Optional[Any]						     = DataLoader(
	    tokenized_datasets["""train"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )
	A_		:    List[str]						     = 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

							_lowerCAmelCase       =				mocked_dataloaders  # noqa: F811






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case__       ) == "1":
		A_		:    List[Any]						     = 2
	# New Code #
	A_		:    Tuple						     = int(args.gradient_accumulation_steps       )
	# Initialize accelerator
	A_		:    Any						     = Accelerator(
	    cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case__       )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""       )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_		:    Union[str, Any]						     = config["""lr"""]
	A_		:    Tuple						     = int(config["""num_epochs"""]       )
	A_		:    List[Any]						     = int(config["""seed"""]       )
	A_		:    Dict						     = int(config["""batch_size"""]       )

	A_		:    Tuple						     = evaluate.load("""glue""" , """mrpc"""       )

	set_seed(snake_case__       )
	A_     ,  A_		:    List[Any]						     = get_dataloaders(snake_case__ , snake_case__       )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_		:    Dict						     = 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).
	A_		:    Union[str, Any]						     = model.to(accelerator.device       )

	# Instantiate optimizer
	A_		:    Optional[int]						     = AdamW(params=model.parameters() , lr=snake_case__       )

	# Instantiate scheduler
	A_		:    Union[str, Any]						     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__       ) * 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.
	A_     ,  A_     ,  A_     ,  A_     ,  A_		:    Optional[int]						     = accelerator.prepare(
	    snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		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       )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(snake_case__       ):
				A_		:    Optional[int]						     = model(**snake_case__       )
				A_		:    Tuple						     = output.loss
				accelerator.backward(snake_case__       )
				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`.
			batch.to(accelerator.device       )
			with torch.no_grad():
				A_		:    List[str]						     = model(**snake_case__       )
			A_		:    List[str]						     = outputs.logits.argmax(dim=-1       )
			A_     ,  A_		:    List[Any]						     = accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
			metric.add_batch(
			    predictions=snake_case__ , references=snake_case__ , )

		A_		:    str						     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:""" , snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = 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.""" , )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps""" , type=snake_case__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
	parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU."""       )
	A_		:    int						     = parser.parse_args()
	A_		:    Optional[Any]						     = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__ , snake_case__       )


if __name__ == "__main__":
							main()

