




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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)


_lowercase						  =		{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_lowercase						  =		{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_lowercase						  =		{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		lowerCAmelCase__    (					)->  Optional[int]:
    A__		 =					(
        list(range(ord('''!'''	) ,       ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	) ,       ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	) ,       ord('''ÿ'''	) + 1	)	)
    )
    A__		 =					bs[:]
    A__		 =					0
    for b in range(2**8	):
        if b not in bs:
            bs.append(UpperCamelCase_	)
            cs.append(2**8 + n	)
            n += 1
    A__		 =					[chr(UpperCamelCase_	) for n in cs]
    return dict(zip(UpperCamelCase_ ,       UpperCamelCase_	)	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any]	)->  List[str]:
    A__		 =					set()
    A__		 =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        A__		 =					char
    return pairs



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				['''input_ids''', '''attention_mask''']




     def __init__(				self ,						a__ ,						a__ ,						a__="replace" ,						a__="<s>" ,						a__="</s>" ,						a__="</s>" ,						a__="<s>" ,						a__="<unk>" ,						a__="<pad>" ,						a__="<mask>" ,						a__=False ,						**a__ ,						):
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else bos_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else eos_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else sep_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else cls_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else unk_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else mask_token

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

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

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




     @property
     def 		snake_case_		(				self):
         return len(self.encoder)




     def 		snake_case_		(				self):
         return dict(self.encoder ,						**self.added_tokens_encoder)




     def 		snake_case_		(				self ,						a__):
         if token in self.cache:
             return self.cache[token]
         A__		 =					tuple(a__)
         A__		 =					get_pairs(a__)

         if not pairs:
             return token

         while True:
             A__		 =					min(a__ ,						key=lambda a__: self.bpe_ranks.get(a__ ,						float('''inf''')))
             if bigram not in self.bpe_ranks:
                 break
             A__ ,	A__		 =					bigram
             A__		 =					[]
             A__		 =					0
             while i < len(a__):
                 try:
                     A__		 =					word.index(a__ ,						a__)
                 except ValueError:
                     new_word.extend(word[i:])
                     break
                 else:
                     new_word.extend(word[i:j])
                     A__		 =					j

                 if word[i] == first and i < len(a__) - 1 and word[i + 1] == second:
                     new_word.append(first + second)
                     i += 2
                 else:
                     new_word.append(word[i])
                     i += 1
             A__		 =					tuple(a__)
             A__		 =					new_word
             if len(a__) == 1:
                 break
             else:
                 A__		 =					get_pairs(a__)
         A__		 =					''' '''.join(a__)
         A__		 =					word
         return word




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]
         for token in re.findall(self.pat ,						a__):
             A__		 =					''''''.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(a__).split(''' '''))
         return bpe_tokens




     def 		snake_case_		(				self ,						a__):
         return self.encoder.get(a__ ,						self.encoder.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.decoder.get(a__)




     def 		snake_case_		(				self ,						a__):
         A__		 =					''''''.join(a__)
         A__		 =					bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8''' ,						errors=self.errors)
         return text




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         if not os.path.isdir(a__):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory")
             return
         A__		 =					os.path.join(
             a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
         A__		 =					os.path.join(
             a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

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

         A__		 =					0
         with open(a__ ,						'''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!''')
                     A__		 =					token_index
                 writer.write(''' '''.join(a__) + '''\n''')
                 index += 1

         return vocab_file, merge_file




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__		 =					[self.cls_token_id]
         A__		 =					[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = False):
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=a__ ,						token_ids_a=a__ ,						already_has_special_tokens=a__)

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




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					[self.sep_token_id]
         A__		 =					[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 ,						a__ ,						a__=False ,						**a__):
         A__		 =					kwargs.pop('''add_prefix_space''' ,						self.add_prefix_space)
         if (is_split_into_words or add_prefix_space) and (len(a__) > 0 and not text[0].isspace()):
             A__		 =					''' ''' + text
         return (text, kwargs)





import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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						  =		16
_lowercase						  =		32



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Accelerator ,       UpperCamelCase_				:       int = 1_6	)->  List[str]:
    A__		 =					AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    A__		 =					load_dataset('''glue''' ,       '''mrpc'''	)

    def tokenize_function(UpperCamelCase_				:       Any	):
        # max_length=None => use the model max length (it's actually the default)
        A__		 =					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():
        A__		 =					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
    A__		 =					tokenized_datasets.rename_column('''label''' ,       '''labels'''	)

    def collate_fn(UpperCamelCase_				:       str	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__		 =					1_2_8 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__		 =					1_6
        elif accelerator.mixed_precision != "no":
            A__		 =					8
        else:
            A__		 =					None

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

    # Instantiate dataloaders.
    A__		 =					DataLoader(
        tokenized_datasets['''train'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_	)
    A__		 =					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

   _lowercase						  =		mocked_dataloaders  # noqa: F811



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       Tuple	)->  Union[str, Any]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,       UpperCamelCase_	) == "1":
        A__		 =					2
    # Initialize accelerator
    A__		 =					Accelerator(cpu=args.cpu ,       mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__		 =					config['''lr''']
    A__		 =					int(config['''num_epochs''']	)
    A__		 =					int(config['''seed''']	)
    A__		 =					int(config['''batch_size''']	)

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

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=UpperCamelCase_	)
    def inner_training_loop(UpperCamelCase_				:       Optional[int]	):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(UpperCamelCase_	)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        A__		 =					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).
        A__		 =					model.to(accelerator.device	)

        # Instantiate optimizer
        A__		 =					AdamW(params=model.parameters() ,       lr=UpperCamelCase_	)
        A__ ,	A__		 =					get_dataloaders(UpperCamelCase_ ,       UpperCamelCase_	)

        # Instantiate scheduler
        A__		 =					get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase_ ,       num_warmup_steps=1_0_0 ,       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.
        A__ ,	A__ ,	A__ ,	A__ ,	A__		 =					accelerator.prepare(
            UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

        # Now we train the model
        for epoch in range(UpperCamelCase_	):
            model.train()
            for step, batch in enumerate(UpperCamelCase_	):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device	)
                A__		 =					model(**UpperCamelCase_	)
                A__		 =					outputs.loss
                accelerator.backward(UpperCamelCase_	)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            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():
                    A__		 =					model(**UpperCamelCase_	)
                A__		 =					outputs.logits.argmax(dim=-1	)
                A__ ,	A__		 =					accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                metric.add_batch(
                    predictions=UpperCamelCase_ ,       references=UpperCamelCase_ ,       )

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()



def 		lowerCAmelCase__    (					)->  Optional[Any]:
    A__		 =					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.''' ,       )
    parser.add_argument('''--cpu''' ,       action='''store_true''' ,       help='''If passed, will train on the CPU.'''	)
    A__		 =					parser.parse_args()
    A__		 =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
    training_function(UpperCamelCase_ ,       UpperCamelCase_	)


if __name__ == "__main__":
   main()
