

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


UpperCAmelCase_							:					Any		 =   logging.get_logger(__name__)


UpperCAmelCase_							:					Tuple		 =   {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_							:					int		 =   {
    '''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''',
    },
}

UpperCAmelCase_							:					Dict		 =   {
    '''allenai/led-base-16384''': 16_384,
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				VOCAB_FILES_NAMES
	_lowercase		:   Union[str, Any]     					=				PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:   Optional[int]     					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:   Dict     					=				LEDTokenizer
	_lowercase		:   List[Any]     					=				['''input_ids''', '''attention_mask''']




	def __init__(     self						:     Optional[Any]					,      __A						:     Dict=None					,      __A						:     Tuple=None					,      __A						:     Optional[int]=None					,      __A						:     Any="replace"					,      __A						:     Any="<s>"					,      __A						:     Any="</s>"					,      __A						:     List[str]="</s>"					,      __A						:     List[Any]="<s>"					,      __A						:     Dict="<unk>"					,      __A						:     Optional[int]="<pad>"					,      __A						:     List[Any]="<mask>"					,      __A						:     List[Any]=False					,      __A						:     Tuple=True					,      **__A						:     Optional[Any]					,      ):
		super().__init__(
		    __A					,      __A					,      tokenizer_file=__A					,      errors=__A					,      bos_token=__A					,      eos_token=__A					,      sep_token=__A					,      cls_token=__A					,      unk_token=__A					,      pad_token=__A					,      mask_token=__A					,      add_prefix_space=__A					,      trim_offsets=__A					,      **__A					,      )

		__A :			List[Any]		    =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
		if pre_tok_state.get("""add_prefix_space"""					,      __A							) != add_prefix_space:
			__A :			int		    =			getattr(__A					,      pre_tok_state.pop("""type"""							)							)
			__A :			Optional[Any]		    =			add_prefix_space
			__A :			Optional[int]		    =			pre_tok_class(**__A							)

		__A :			Optional[Any]		    =			add_prefix_space

		# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
		__A :			Optional[int]		    =			"""post_processor"""
		__A :			Tuple		    =			getattr(self.backend_tokenizer					,      __A					,      __A							)
		if tokenizer_component_instance:
			__A :			Tuple		    =			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:
				__A :			Dict		    =			tuple(state["""sep"""]							)
			if "cls" in state:
				__A :			Optional[Any]		    =			tuple(state["""cls"""]							)

			__A :			Optional[Any]		    =			False

			if state.get("""add_prefix_space"""					,      __A							) != add_prefix_space:
				__A :			Tuple		    =			add_prefix_space
				__A :			Union[str, Any]		    =			True

			if state.get("""trim_offsets"""					,      __A							) != trim_offsets:
				__A :			int		    =			trim_offsets
				__A :			Optional[Any]		    =			True

			if changes_to_apply:
				__A :			Union[str, Any]		    =			getattr(__A					,      state.pop("""type"""							)							)
				__A :			Union[str, Any]		    =			component_class(**__A							)
				setattr(self.backend_tokenizer					,      __A					,      __A							)




	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def        lowerCAmelCase_       (     self						:     List[str]							):
		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        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Optional[Any]		    =			AddedToken(__A					,      lstrip=__A					,      rstrip=__A							) if isinstance(__A					,      __A							) else value
		__A :			List[Any]		    =			value




	def        lowerCAmelCase_       (     self						:     int					,      *__A						:     Optional[int]					,      **__A						:     Optional[int]							):
		__A :			Any		    =			kwargs.get("""is_split_into_words"""					,      __A							)

		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(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     Tuple					,      **__A						:     Optional[Any]							):
		__A :			Tuple		    =			kwargs.get("""is_split_into_words"""					,      __A							)

		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(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     str					,      __A						:     Optional[str] = None							):
		__A :			List[Any]		    =			self._tokenizer.model.save(__A					,      name=__A							)
		return tuple(__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     int					,      __A						:     Tuple=None							):
		__A :			Optional[int]		    =			[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        lowerCAmelCase_       (     self						:     str					,      __A						:     List[int]					,      __A						:     Optional[List[int]] = None							):
		__A :			Dict		    =			[self.sep_token_id]
		__A :			List[Any]		    =			[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        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[Dict[str, EncodedInput], BatchEncoding]					,      __A						:     Optional[int] = None					,      __A						:     PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,      __A						:     Optional[int] = None					,      __A						:     Optional[bool] = None					,      ):
		__A :			Optional[Any]		    =			super()._pad(
		    encoded_inputs=__A					,      max_length=__A					,      padding_strategy=__A					,      pad_to_multiple_of=__A					,      return_attention_mask=__A					,      )

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

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

			if needs_to_be_padded:
				__A :			Any		    =			len(__A							) - 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`
					__A :			int		    =			(
					    encoded_inputs["""global_attention_mask"""] + [-1] * difference
					)
				elif self.padding_side == "left":
					__A :			List[Any]		    =			[-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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


a    :							str										=    16
a    :							Union[str, Any]										=    32


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 16      )						->   Union[str, Any]:

					'''simple docstring'''

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

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

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

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

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

					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    :							Optional[Any]										=    mocked_dataloaders  # noqa: F811


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''', __UpperCAmelCase      ) == "1":
										snake_case_		     =							2
					# New Code #
					snake_case_		     =							int(args.gradient_accumulation_steps      )
					snake_case_		     =							int(args.local_sgd_steps      )
					# Initialize accelerator
					snake_case_		     =							Accelerator(
					    cpu=args.cpu, mixed_precision=args.mixed_precision, gradient_accumulation_steps=__UpperCAmelCase      )
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''      )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_		     =							config['''lr''']
					snake_case_		     =							int(config['''num_epochs''']      )
					snake_case_		     =							int(config['''seed''']      )
					snake_case_		     =							int(config['''batch_size''']      )

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

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

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

					# Instantiate optimizer
					snake_case_		     =							AdamW(params=model.parameters(), lr=__UpperCAmelCase      )

					# Instantiate scheduler
					snake_case_		     =							get_linear_schedule_with_warmup(
					    optimizer=__UpperCAmelCase, num_warmup_steps=100, num_training_steps=(len(__UpperCAmelCase      ) * 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.
					snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							accelerator.prepare(
					    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					# Now we train the model
					for epoch in range(__UpperCAmelCase      ):
										model.train()
										with LocalSGD(
										    accelerator=__UpperCAmelCase, model=__UpperCAmelCase, local_sgd_steps=__UpperCAmelCase, enabled=local_sgd_steps is not None      ) as local_sgd:
															for step, batch in enumerate(__UpperCAmelCase      ):
																				# 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(__UpperCAmelCase      ):
																									snake_case_		     =							model(**__UpperCAmelCase      )
																									snake_case_		     =							output.loss
																									accelerator.backward(__UpperCAmelCase      )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

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

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


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument(
					    '''--mixed_precision''', type=__UpperCAmelCase, default=__UpperCAmelCase, 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=__UpperCAmelCase, default=1, help='''The number of minibatches to be ran before gradients are accumulated.''', )
					parser.add_argument(
					    '''--local_sgd_steps''', type=__UpperCAmelCase, default=8, help='''Number of local SGD steps or None to disable local SGD'''      )
					parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.'''      )
					snake_case_		     =							parser.parse_args()
					snake_case_		     =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__UpperCAmelCase, __UpperCAmelCase      )


if __name__ == "__main__":
							main()

