




'''simple docstring'''
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator
def 							snake_case_    (  a__						:      List[Any]     ):



    """simple docstring"""




    __lowercase     							=		fname.split(os.path.sep     )[-1]
    return re.search(r"""^(.*)_\d+\.jpg$"""       ,a__     ).groups()[0]






class 			SCREAMING_SNAKE_CASE(							__A	):

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__=None   ,						lowerCamelCase__=None	)       ->   List[Any]:

           """simple docstring"""



           __lowercase     							=		file_names
           __lowercase     							=		image_transform
           __lowercase     							=		label_to_id

       def __len__(       self	)       ->   Tuple:

           """simple docstring"""



           return len(self.file_names	)




       def __getitem__(       self   ,						lowerCamelCase__	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		self.file_names[idx]
           __lowercase     							=		PIL.Image.open(lowerCamelCase__	)
           __lowercase     							=		raw_image.convert("""RGB"""	)
           if self.image_transform is not None:
               __lowercase     							=		self.image_transform(lowerCamelCase__	)
           __lowercase     							=		extract_label(lowerCamelCase__	)
           if self.label_to_id is not None:
               __lowercase     							=		self.label_to_id[label]
           return {"image": image, "label": label}

def 							snake_case_    (  a__						:      int       ,a__						:      int     ):



    """simple docstring"""




    __lowercase     							=		Accelerator(cpu=args.cpu       ,mixed_precision=args.mixed_precision     )

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowercase     							=		config["""lr"""]
    __lowercase     							=		int(config["""num_epochs"""]     )
    __lowercase     							=		int(config["""seed"""]     )
    __lowercase     							=		int(config["""batch_size"""]     )
    __lowercase     							=		config["""image_size"""]
    if not isinstance(a__       ,(list, tuple)     ):
        __lowercase     							=		(image_size, image_size)

    # Grab all the image filenames
    __lowercase     							=		[os.path.join(args.data_dir       ,a__     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(""".jpg"""     )]

    # Build the label correspondences
    __lowercase     							=		[extract_label(a__     ) for fname in file_names]
    __lowercase     							=		list(set(a__     )     )
    id_to_label.sort()
    __lowercase     							=		{lbl: i for i, lbl in enumerate(a__     )}

    # Set the seed before splitting the data.
    np.random.seed(a__     )
    torch.manual_seed(a__     )
    torch.cuda.manual_seed_all(a__     )

    # Split our filenames between train and validation
    __lowercase     							=		np.random.permutation(len(a__     )     )
    __lowercase     							=		int(0.8 * len(a__     )     )
    __lowercase     							=		random_perm[:cut]
    __lowercase     							=		random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    __lowercase     							=		Compose([RandomResizedCrop(a__       ,scale=(0.5, 1.0)     ), ToTensor()]     )
    __lowercase     							=		PetsDataset(
        [file_names[i] for i in train_split]       ,image_transform=a__       ,label_to_id=a__     )

    # For evaluation, we use a deterministic Resize
    __lowercase     							=		Compose([Resize(a__     ), ToTensor()]     )
    __lowercase     							=		PetsDataset([file_names[i] for i in eval_split]       ,image_transform=a__       ,label_to_id=a__     )

    # Instantiate dataloaders.
    __lowercase     							=		DataLoader(a__       ,shuffle=a__       ,batch_size=a__       ,num_workers=4     )
    __lowercase     							=		DataLoader(a__       ,shuffle=a__       ,batch_size=a__       ,num_workers=4     )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __lowercase     							=		create_model("""resnet50d"""       ,pretrained=a__       ,num_classes=len(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).
    __lowercase     							=		model.to(accelerator.device     )

    # Freezing the base model
    for param in model.parameters():
        __lowercase     							=		False
    for param in model.get_classifier().parameters():
        __lowercase     							=		True

    # We normalize the batches of images to be a bit faster.
    __lowercase     							=		torch.tensor(model.default_cfg["""mean"""]     )[None, :, None, None].to(accelerator.device     )
    __lowercase     							=		torch.tensor(model.default_cfg["""std"""]     )[None, :, None, None].to(accelerator.device     )

    # Instantiate optimizer
    __lowercase     							=		torch.optim.Adam(params=model.parameters()       ,lr=lr / 25     )

    # Instantiate learning rate scheduler
    __lowercase     							=		OneCycleLR(optimizer=a__       ,max_lr=a__       ,epochs=a__       ,steps_per_epoch=len(a__     )     )

    # 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.
    __lowercase							,__lowercase							,__lowercase							,__lowercase							,__lowercase     							=		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`.
            __lowercase     							=		{k: v.to(accelerator.device     ) for k, v in batch.items()}
            __lowercase     							=		(batch["""image"""] - mean) / std
            __lowercase     							=		model(a__     )
            __lowercase     							=		torch.nn.functional.cross_entropy(a__       ,batch["""label"""]     )
            accelerator.backward(a__     )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        __lowercase     							=		0
        __lowercase     							=		0
        for _, batch in enumerate(a__     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            __lowercase     							=		{k: v.to(accelerator.device     ) for k, v in batch.items()}
            __lowercase     							=		(batch["""image"""] - mean) / std
            with torch.no_grad():
                __lowercase     							=		model(a__     )
            __lowercase     							=		outputs.argmax(dim=-1     )
            __lowercase							,__lowercase     							=		accelerator.gather_for_metrics((predictions, batch["""label"""])     )
            __lowercase     							=		predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        __lowercase     							=		accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}: {1_00 * eval_metric:.2f}'     )
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		argparse.ArgumentParser(description="""Simple example of training script."""     )
    parser.add_argument("""--data_dir"""       ,required=a__       ,help="""The data folder on disk."""     )
    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(
        """--checkpointing_steps"""       ,type=a__       ,default=a__       ,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""       ,)
    parser.add_argument("""--cpu"""       ,action="""store_true"""       ,help="""If passed, will train on the CPU."""     )
    __lowercase     							=		parser.parse_args()
    __lowercase     							=		{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
    training_function(a__       ,a__     )


if __name__ == "__main__":
  main()










'''simple docstring'''
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


A				: List[str]					      =			logging.get_logger(__name__)


A				: Optional[int]					      =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
A				: Tuple					      =			{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

A				: Optional[int]					      =			{
    """RUCAIBox/mvp""": 1_024,
}
@lru_cache()
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		(
        list(range(ord("""!"""     )       ,ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )       ,ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )       ,ord("""ÿ"""     ) + 1     )     )
    )
    __lowercase     							=		bs[:]
    __lowercase     							=		0
    for b in range(2**8     ):
        if b not in bs:
            bs.append(a__     )
            cs.append(2**8 + n     )
            n += 1
    __lowercase     							=		[chr(a__     ) for n in cs]
    return dict(zip(a__       ,a__     )     )
def 							snake_case_    (  a__						:      Dict     ):



    """simple docstring"""




    __lowercase     							=		set()
    __lowercase     							=		word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)     )
        __lowercase     							=		char
    return pairs






class 			SCREAMING_SNAKE_CASE(							__A	):
       snake_case_					:	int       =   VOCAB_FILES_NAMES
       snake_case_					:	List[Any]       =   PRETRAINED_VOCAB_FILES_MAP
       snake_case_					:	List[Any]       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case_					:	Dict       =   ["""input_ids""", """attention_mask"""]

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__="replace"   ,						lowerCamelCase__="<s>"   ,						lowerCamelCase__="</s>"   ,						lowerCamelCase__="</s>"   ,						lowerCamelCase__="<s>"   ,						lowerCamelCase__="<unk>"   ,						lowerCamelCase__="<pad>"   ,						lowerCamelCase__="<mask>"   ,						lowerCamelCase__=False   ,						**lowerCamelCase__   ,						)       ->   Union[str, Any]:

           """simple docstring"""



           __lowercase     							=		AddedToken(lowerCamelCase__   ,						lstrip=lowerCamelCase__   ,						rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else bos_token
           __lowercase     							=		AddedToken(lowerCamelCase__   ,						lstrip=lowerCamelCase__   ,						rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else eos_token
           __lowercase     							=		AddedToken(lowerCamelCase__   ,						lstrip=lowerCamelCase__   ,						rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else sep_token
           __lowercase     							=		AddedToken(lowerCamelCase__   ,						lstrip=lowerCamelCase__   ,						rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else cls_token
           __lowercase     							=		AddedToken(lowerCamelCase__   ,						lstrip=lowerCamelCase__   ,						rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else unk_token
           __lowercase     							=		AddedToken(lowerCamelCase__   ,						lstrip=lowerCamelCase__   ,						rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else pad_token

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

           super().__init__(
               errors=lowerCamelCase__   ,						bos_token=lowerCamelCase__   ,						eos_token=lowerCamelCase__   ,						unk_token=lowerCamelCase__   ,						sep_token=lowerCamelCase__   ,						cls_token=lowerCamelCase__   ,						pad_token=lowerCamelCase__   ,						mask_token=lowerCamelCase__   ,						add_prefix_space=lowerCamelCase__   ,						**lowerCamelCase__   ,						)
           with open(lowerCamelCase__   ,						encoding="""utf-8"""	) as vocab_handle:
               __lowercase     							=		json.load(lowerCamelCase__	)
           __lowercase     							=		{v: k for k, v in self.encoder.items()}
           __lowercase     							=		errors  # how to handle errors in decoding
           __lowercase     							=		bytes_to_unicode()
           __lowercase     							=		{v: k for k, v in self.byte_encoder.items()}
           with open(lowerCamelCase__   ,						encoding="""utf-8"""	) as merges_handle:
               __lowercase     							=		merges_handle.read().split("""\n"""	)[1:-1]
           __lowercase     							=		[tuple(merge.split()	) for merge in bpe_merges]
           __lowercase     							=		dict(zip(lowerCamelCase__   ,						range(len(lowerCamelCase__	)	)	)	)
           __lowercase     							=		{}
           __lowercase     							=		add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           __lowercase     							=		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	)       ->   List[str]:

           """simple docstring"""



           return len(self.encoder	)

       def        snake_case__     (       self	)       ->   Optional[int]:

           """simple docstring"""



           return dict(self.encoder   ,						**self.added_tokens_encoder	)

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Any:

           """simple docstring"""



           if token in self.cache:
               return self.cache[token]
           __lowercase     							=		tuple(lowerCamelCase__	)
           __lowercase     							=		get_pairs(lowerCamelCase__	)

           if not pairs:
               return token

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

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

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Union[str, Any]:

           """simple docstring"""



           __lowercase     							=		[]
           for token in re.findall(self.pat   ,						lowerCamelCase__	):
               __lowercase     							=		"""""".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(lowerCamelCase__	).split(""" """	)	)
           return bpe_tokens

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   int:

           """simple docstring"""



           return self.encoder.get(lowerCamelCase__   ,						self.encoder.get(self.unk_token	)	)

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Dict:

           """simple docstring"""



           return self.decoder.get(lowerCamelCase__	)

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		"""""".join(lowerCamelCase__	)
           __lowercase     							=		bytearray([self.byte_decoder[c] for c in text]	).decode("""utf-8"""   ,						errors=self.errors	)
           return text

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None	)       ->   Tuple[str]:

           """simple docstring"""



           if not os.path.isdir(lowerCamelCase__	):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
               return
           __lowercase     							=		os.path.join(
               lowerCamelCase__   ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
           __lowercase     							=		os.path.join(
               lowerCamelCase__   ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

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

           __lowercase     							=		0
           with open(lowerCamelCase__   ,						"""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 lowerCamelCase__	: 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!"""	)
                       __lowercase     							=		token_index
                   writer.write(""" """.join(lowerCamelCase__	) + """\n"""	)
                   index += 1

           return vocab_file, merge_file

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None	)       ->   List[int]:

           """simple docstring"""



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

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = False	)       ->   List[int]:

           """simple docstring"""



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

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

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None	)       ->   List[int]:

           """simple docstring"""



           __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        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__=False   ,						**lowerCamelCase__	)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		kwargs.pop("""add_prefix_space"""   ,						self.add_prefix_space	)
           if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__	) > 0 and not text[0].isspace()):
               __lowercase     							=		""" """ + text
           return (text, kwargs)




