


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase_			       =      logging.get_logger(__name__)


lowercase_			       =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase_			       =      {
    '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',
    },
}

lowercase_			       =      {
    'allenai/led-base-16384': 1_6_3_8_4,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  UpperCamelCase__	(    ):
 __lowerCamelCase						:				Union[str, Any]						   =		(
     list(range(ord('!'		)     ,			ord('~'		) + 1		)		) + list(range(ord('¡'		)     ,			ord('¬'		) + 1		)		) + list(range(ord('®'		)     ,			ord('ÿ'		) + 1		)		)
 )
 __lowerCamelCase						:				Tuple						   =		bs[:]
 __lowerCamelCase						:				Tuple						   =		0
 for b in range(2**8		):
  if b not in bs:
   bs.append(__a		)
   cs.append(2**8 + n		)
   n += 1
 __lowerCamelCase						:				List[Any]						   =		[chr(__a		) for n in cs]
 return dict(zip(__a     ,			__a		)		)
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				Union[str, Any]						   =		set()
 __lowerCamelCase						:				Union[str, Any]						   =		word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  __lowerCamelCase						:				List[str]						   =		char
 return pairs



class 			A_   (			A__       ):




      '''simple docstring'''


      __snake_case   			=  VOCAB_FILES_NAMES
      __snake_case   			=  PRETRAINED_VOCAB_FILES_MAP
      __snake_case   			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case   			=  ["""input_ids""", """attention_mask"""]




      def __init__(							self:			Any		, a:			List[Any]		, a:			Union[str, Any]		, a:			Optional[Any]="replace"		, a:			str="<s>"		, a:			Optional[int]="</s>"		, a:			int="</s>"		, a:			List[str]="<s>"		, a:			int="<unk>"		, a:			Optional[int]="<pad>"		, a:			Optional[Any]="<mask>"		, a:			Optional[int]=False		, **a:			Dict		, ):
       __lowerCamelCase						:				Tuple						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else bos_token
       __lowerCamelCase						:				str						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else eos_token
       __lowerCamelCase						:				List[Any]						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else sep_token
       __lowerCamelCase						:				Tuple						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else cls_token
       __lowerCamelCase						:				List[str]						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else unk_token
       __lowerCamelCase						:				List[str]						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       __lowerCamelCase						:				Union[str, Any]						   =		AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_		, UpperCamelCase_							) else mask_token

       super().__init__(
           errors=UpperCamelCase_		, bos_token=UpperCamelCase_		, eos_token=UpperCamelCase_		, unk_token=UpperCamelCase_		, sep_token=UpperCamelCase_		, cls_token=UpperCamelCase_		, pad_token=UpperCamelCase_		, mask_token=UpperCamelCase_		, add_prefix_space=UpperCamelCase_		, **UpperCamelCase_		, )

       with open(UpperCamelCase_		, encoding='utf-8'							) as vocab_handle:
        __lowerCamelCase						:				Dict						   =		json.load(UpperCamelCase_							)
       __lowerCamelCase						:				int						   =		{v: k for k, v in self.encoder.items()}
       __lowerCamelCase						:				List[Any]						   =		errors  # how to handle errors in decoding
       __lowerCamelCase						:				int						   =		bytes_to_unicode()
       __lowerCamelCase						:				List[str]						   =		{v: k for k, v in self.byte_encoder.items()}
       with open(UpperCamelCase_		, encoding='utf-8'							) as merges_handle:
        __lowerCamelCase						:				List[str]						   =		merges_handle.read().split('\n'							)[1:-1]
       __lowerCamelCase						:				List[Any]						   =		[tuple(merge.split()							) for merge in bpe_merges]
       __lowerCamelCase						:				List[str]						   =		dict(zip(UpperCamelCase_		, range(len(UpperCamelCase_							)							)							)							)
       __lowerCamelCase						:				Optional[int]						   =		{}
       __lowerCamelCase						:				Optional[int]						   =		add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       __lowerCamelCase						:				List[str]						   =		re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'							)




      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 	_snake_case      (							self:			List[Any]							):
       return len(self.encoder							)




      def 	_snake_case      (							self:			List[str]							):
       return dict(self.encoder		, **self.added_tokens_encoder							)




      def 	_snake_case      (							self:			int		, a:			Any							):


       if token in self.cache:
        return self.cache[token]
       __lowerCamelCase						:				Union[str, Any]						   =		tuple(UpperCamelCase_							)
       __lowerCamelCase						:				Any						   =		get_pairs(UpperCamelCase_							)

       if not pairs:
        return token

       while True:
        __lowerCamelCase						:				List[Any]						   =		min(UpperCamelCase_		, key=lambda a							: self.bpe_ranks.get(UpperCamelCase_		, float('inf'							)							)							)
        if bigram not in self.bpe_ranks:
         break
        __lowerCamelCase						:				Dict						   =		bigram
        __lowerCamelCase						:				Optional[Any]						   =		[]
        __lowerCamelCase						:				Union[str, Any]						   =		0
        while i < len(UpperCamelCase_							):
         try:
          __lowerCamelCase						:				Optional[Any]						   =		word.index(UpperCamelCase_		, UpperCamelCase_							)
         except ValueError:
          new_word.extend(word[i:]							)
          break
         else:
          new_word.extend(word[i:j]							)
          __lowerCamelCase						:				List[Any]						   =		j

         if word[i] == first and i < len(UpperCamelCase_							) - 1 and word[i + 1] == second:
          new_word.append(first + second							)
          i += 2
         else:
          new_word.append(word[i]							)
          i += 1
        __lowerCamelCase						:				List[Any]						   =		tuple(UpperCamelCase_							)
        __lowerCamelCase						:				Tuple						   =		new_word
        if len(UpperCamelCase_							) == 1:
         break
        else:
         __lowerCamelCase						:				List[str]						   =		get_pairs(UpperCamelCase_							)
       __lowerCamelCase						:				Dict						   =		''' '''.join(UpperCamelCase_							)
       __lowerCamelCase						:				Dict						   =		word
       return word




      def 	_snake_case      (							self:			List[Any]		, a:			str							):
       __lowerCamelCase						:				str						   =		[]
       for token in re.findall(self.pat		, UpperCamelCase_							):
        __lowerCamelCase						:				Tuple						   =		''''''.join(
            self.byte_encoder[b] for b in token.encode('utf-8'							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase_							).split(' '							)							)
       return bpe_tokens




      def 	_snake_case      (							self:			List[str]		, a:			int							):
       return self.encoder.get(UpperCamelCase_		, self.encoder.get(self.unk_token							)							)




      def 	_snake_case      (							self:			Tuple		, a:			List[str]							):
       return self.decoder.get(UpperCamelCase_							)




      def 	_snake_case      (							self:			Dict		, a:			Dict							):
       __lowerCamelCase						:				Tuple						   =		''''''.join(UpperCamelCase_							)
       __lowerCamelCase						:				int						   =		bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'		, errors=self.errors							)
       return text




      def 	_snake_case      (							self:			int		, a:			List[str]		, a:			str = None							):


       if not os.path.isdir(UpperCamelCase_							):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
        return
       __lowerCamelCase						:				Union[str, Any]						   =		os.path.join(
           UpperCamelCase_		, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
       __lowerCamelCase						:				List[Any]						   =		os.path.join(
           UpperCamelCase_		, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

       with open(UpperCamelCase_		, 'w'		, encoding='utf-8'							) as f:
        f.write(json.dumps(self.encoder		, indent=2		, sort_keys=UpperCamelCase_		, ensure_ascii=UpperCamelCase_							) + '\n'							)

       __lowerCamelCase						:				Tuple						   =		0
       with open(UpperCamelCase_		, 'w'		, encoding='utf-8'							) as writer:
        writer.write('#version: 0.2\n'							)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		, key=lambda a							: kv[1]							):
         if index != token_index:
          logger.warning(
              F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
              ' Please check that the tokenizer is not corrupted!'							)
          __lowerCamelCase						:				Optional[int]						   =		token_index
         writer.write(' '.join(UpperCamelCase_							) + '\n'							)
         index += 1

       return vocab_file, merge_file




      def 	_snake_case      (							self:			str		, a:			List[str]		, a:			List[Any] = None							):


       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __lowerCamelCase						:				Tuple						   =		[self.cls_token_id]
       __lowerCamelCase						:				Dict						   =		[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 	_snake_case      (							self:			str		, a:			Optional[Any]		, a:			Optional[int] = None		, a:			Dict = False							):


       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=UpperCamelCase_		, token_ids_a=UpperCamelCase_		, already_has_special_tokens=UpperCamelCase_							)

       if token_ids_a is None:
        return [1] + ([0] * len(UpperCamelCase_							)) + [1]
       return [1] + ([0] * len(UpperCamelCase_							)) + [1, 1] + ([0] * len(UpperCamelCase_							)) + [1]




      def 	_snake_case      (							self:			List[str]		, a:			Any		, a:			Optional[Any] = None							):
       __lowerCamelCase						:				Any						   =		[self.sep_token_id]
       __lowerCamelCase						:				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 	_snake_case      (							self:			Union[str, Any]		, a:			Tuple		, a:			Union[str, Any]=False		, **a:			Union[str, Any]							):
       __lowerCamelCase						:				Union[str, Any]						   =		kwargs.pop('add_prefix_space'		, self.add_prefix_space							)
       if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_							) > 0 and not text[0].isspace()):
        __lowerCamelCase						:				List[Any]						   =		''' ''' + text
       return (text, kwargs)




      def 	_snake_case      (							self:			Dict		, a:			Optional[int]		, a:			Optional[Any] = None		, a:			List[str] = PaddingStrategy.DO_NOT_PAD		, a:			str = None		, a:			List[Any] = None		, ):
       __lowerCamelCase						:				int						   =		super()._pad(
           encoded_inputs=UpperCamelCase_		, max_length=UpperCamelCase_		, padding_strategy=UpperCamelCase_		, pad_to_multiple_of=UpperCamelCase_		, return_attention_mask=UpperCamelCase_		, )

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

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        __lowerCamelCase						:				Optional[int]						   =		encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        __lowerCamelCase						:				Union[str, Any]						   =		len(encoded_inputs['global_attention_mask']							) != len(UpperCamelCase_							)

        if needs_to_be_padded:
         __lowerCamelCase						:				str						   =		len(UpperCamelCase_							) - 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`
          __lowerCamelCase						:				Optional[int]						   =		(
              encoded_inputs['''global_attention_mask'''] + [-1] * difference
          )
         elif self.padding_side == "left":
          __lowerCamelCase						:				int						   =		[-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 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
#
# 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
#
########################################################################


__snake_case				 =	16
__snake_case				 =	32


def      a (     __a					,   __a = 16  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					AutoTokenizer.from_pretrained('''bert-base-cased'''  )
					UpperCamelCase__  :Optional[Any]				   =					load_dataset('''glue'''					,   '''mrpc'''  )

					def tokenize_function(__a  ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__  :Any				   =					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__  :str				   =					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__  :Optional[int]				   =					tokenized_datasets.rename_column('''label'''					,   '''labels'''  )

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

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

					# Instantiate dataloaders.
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''train''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=__a  )
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''validation''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=(accelerator.mixed_precision == '''fp8''')					,   )

					return train_dataloader, eval_dataloader


def      a (     __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase__  :List[Any]				   =					config['''lr''']
					UpperCamelCase__  :List[str]				   =					int(config['''num_epochs''']  )
					UpperCamelCase__  :int				   =					int(config['''seed''']  )
					UpperCamelCase__  :str				   =					int(config['''batch_size''']  )

					UpperCamelCase__  :List[str]				   =					evaluate.load('''glue'''					,   '''mrpc'''  )

					# If the batch size is too big we use gradient accumulation
					UpperCamelCase__  :Tuple				   =					1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										UpperCamelCase__  :Optional[Any]				   =					batch_size // MAX_GPU_BATCH_SIZE
										UpperCamelCase__  :Dict				   =					MAX_GPU_BATCH_SIZE

					set_seed(__a  )
					UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					get_dataloaders(__a					,   __a  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :List[Any]				   =					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__  :Optional[int]				   =					model.to(accelerator.device  )
					# Instantiate optimizer
					UpperCamelCase__  :Union[str, Any]				   =					AdamW(params=model.parameters()					,   lr=__a  )

					# Instantiate scheduler
					UpperCamelCase__  :Optional[Any]				   =					get_linear_schedule_with_warmup(
					    optimizer=__a					,   num_warmup_steps=100					,   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__  :str				   =					accelerator.prepare(
					    __a					,   __a					,   __a					,   __a					,   __a  )

					# Now we train the model
					for epoch in range(__a  ):
										model.train()
										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__  :Dict				   =					model(**__a  )
															UpperCamelCase__  :Union[str, Any]				   =					outputs.loss
															UpperCamelCase__  :Tuple				   =					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`.
															batch.to(accelerator.device  )
															with torch.no_grad():
																				UpperCamelCase__  :Optional[int]				   =					model(**__a  )
															UpperCamelCase__  :int				   =					outputs.logits.argmax(dim=-1  )
															UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
															metric.add_batch(
															    predictions=__a					,   references=__a					,   )

										UpperCamelCase__  :List[str]				   =					metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:'''					,   __a  )


def      a (     )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					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.'''  )
					UpperCamelCase__  :int				   =					parser.parse_args()
					UpperCamelCase__  :int				   =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()