

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a__				=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

a__				=		list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
a__				=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="cifar10"				,       metadata={"help": "Name of a dataset from the datasets package"}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}				,       )
							UpperCAmelCase__       :						Optional[str]	    =    field(default=__lowercase				,       metadata={"help": "A folder containing the training data."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(default=__lowercase				,       metadata={"help": "A folder containing the validation data."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=0.15				,       metadata={"help": "Percent to split off of train for validation."}   )
							UpperCAmelCase__       :						int	    =    field(default=32				,       metadata={"help": "The size of the square patches to use for masking."}   )
							UpperCAmelCase__       :						float	    =    field(
							    default=0.6				,       metadata={"help": "Percentage of patches to mask."}				,       )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }				,       )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }				,       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    int         =		{}
												if self.train_dir is not None:
																	_a	:    Optional[int]         =		self.train_dir
												if self.validation_dir is not None:
																	_a	:    str         =		self.validation_dir
												_a	:    Optional[Any]         =		data_files if data_files else None



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
							            "checkpoint identifier on the hub. "
							            "Don't set if you want to train a model from scratch."
							        )
							    }				,       )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowercase   )}				,       )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "Override some existing default config settings when a model is trained from scratch. Example: "
							            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
							        )
							    }				,       )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}				,       )
							UpperCAmelCase__       :						str	    =    field(
							    default="main"				,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,       )
							UpperCAmelCase__       :						str	    =    field(default=__lowercase				,       metadata={"help": "Name or path of preprocessor config."}   )
							UpperCAmelCase__       :						bool	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }				,       )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
							        )
							    }				,       )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={
							        "help": (
							            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
							        )
							    }				,       )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={"help": "Stride to use for the encoder."}				,       )



class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a=1_9_2       ,							_a=3_2       ,							_a=4       ,							_a=0.6       )    ->    str:
												_a	:    Dict         =		input_size
												_a	:    int         =		mask_patch_size
												_a	:    str         =		model_patch_size
												_a	:    str         =		mask_ratio

												if self.input_size % self.mask_patch_size != 0:
																	raise ValueError('''Input size must be divisible by mask patch size'''       )
												if self.mask_patch_size % self.model_patch_size != 0:
																	raise ValueError('''Mask patch size must be divisible by model patch size'''       )

												_a	:    List[str]         =		self.input_size // self.mask_patch_size
												_a	:    Optional[int]         =		self.mask_patch_size // self.model_patch_size

												_a	:    Optional[int]         =		self.rand_size**2
												_a	:    Optional[int]         =		int(np.ceil(self.token_count * self.mask_ratio       )       )




							def __call__(			self       )    ->    List[Any]:
												_a	:    Tuple         =		np.random.permutation(self.token_count       )[: self.mask_count]
												_a	:    Tuple         =		np.zeros(self.token_count       ,							dtype=_a       )
												_a	:    Tuple         =		1

												_a	:    int         =		mask.reshape((self.rand_size, self.rand_size)       )
												_a	:    List[Any]         =		mask.repeat(self.scale       ,							axis=0       ).repeat(self.scale       ,							axis=1       )

												return torch.tensor(mask.flatten()       )



def    __UpperCAmelCase (	__a      :			List[Any]	)			->     int:

					"""simple docstring"""

					_a	:    str         =		torch.stack([example['''pixel_values'''] for example in examples]	)
					_a	:    int         =		torch.stack([example['''mask'''] for example in examples]	)
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def    __UpperCAmelCase (	)			->     str:

					"""simple docstring"""

					_a	:    List[str]         =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
					if len(sys.argv	) == 2 and sys.argv[1].endswith('''.json'''	):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										_a      ,					_a      ,					_a	:    Any         =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
					else:
										_a      ,					_a      ,					_a	:    List[Any]         =		parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry('''run_mim'''		,__a		,__a	)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,datefmt='''%m/%d/%Y %H:%M:%S'''		,handlers=[logging.StreamHandler(sys.stdout	)]		,)

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					_a	:    Dict         =		training_args.get_process_log_level()
					logger.setLevel(__a	)
					transformers.utils.logging.set_verbosity(__a	)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
					    + F"""distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}"""	)
					logger.info(F"""Training/evaluation parameters {training_args}"""	)

					# Detecting last checkpoint.
					_a	:    Tuple         =		None
					if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
										_a	:    List[Any]         =		get_last_checkpoint(training_args.output_dir	)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
															raise ValueError(
															    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
															    '''Use --overwrite_output_dir to overcome.'''	)
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
															    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''	)

    # Initialize our dataset.
					_a	:    str         =		load_dataset(
					    data_args.dataset_name		,data_args.dataset_config_name		,data_files=data_args.data_files		,cache_dir=model_args.cache_dir		,use_auth_token=True if model_args.use_auth_token else None		,)

					# If we don't have a validation split, split off a percentage of train as validation.
					_a	:    List[Any]         =		None if '''validation''' in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split		,__a	) and data_args.train_val_split > 0.0:
										_a	:    Optional[Any]         =		ds['''train'''].train_test_split(data_args.train_val_split	)
										_a	:    int         =		split['''train''']
										_a	:    Optional[Any]         =		split['''test''']

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					_a	:    Any         =		{
					    '''cache_dir''': model_args.cache_dir,
					    '''revision''': model_args.model_revision,
					    '''use_auth_token''': True if model_args.use_auth_token else None,
					}
					if model_args.config_name_or_path:
										_a	:    Optional[Any]         =		AutoConfig.from_pretrained(model_args.config_name_or_path		,**__a	)
					elif model_args.model_name_or_path:
										_a	:    Union[str, Any]         =		AutoConfig.from_pretrained(model_args.model_name_or_path		,**__a	)
					else:
										_a	:    Union[str, Any]         =		CONFIG_MAPPING[model_args.model_type]()
										logger.warning('''You are instantiating a new config instance from scratch.'''	)
										if model_args.config_overrides is not None:
															logger.info(F"""Overriding config: {model_args.config_overrides}"""	)
															config.update_from_string(model_args.config_overrides	)
															logger.info(F"""New config: {config}"""	)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(__a		,'''decoder_type'''	):
										_a	:    List[Any]         =		'''simmim'''

					# adapt config
					_a	:    Tuple         =		model_args.image_size if model_args.image_size is not None else config.image_size
					_a	:    Dict         =		model_args.patch_size if model_args.patch_size is not None else config.patch_size
					_a	:    List[str]         =		(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        '''image_size''': model_args.image_size,
					        '''patch_size''': model_args.patch_size,
					        '''encoder_stride''': model_args.encoder_stride,
					    }	)

					# create image processor
					if model_args.image_processor_name:
										_a	:    Optional[Any]         =		AutoImageProcessor.from_pretrained(model_args.image_processor_name		,**__a	)
					elif model_args.model_name_or_path:
										_a	:    Optional[int]         =		AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,**__a	)
					else:
										_a	:    Optional[Any]         =		{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										_a	:    Optional[Any]         =		IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										_a	:    Union[str, Any]         =		AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path		,from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)		,config=__a		,cache_dir=model_args.cache_dir		,revision=model_args.model_revision		,use_auth_token=True if model_args.use_auth_token else None		,)
					else:
										logger.info('''Training new model from scratch'''	)
										_a	:    List[str]         =		AutoModelForMaskedImageModeling.from_config(__a	)

					if training_args.do_train:
										_a	:    Dict         =		ds['''train'''].column_names
					else:
										_a	:    int         =		ds['''validation'''].column_names

					if data_args.image_column_name is not None:
										_a	:    int         =		data_args.image_column_name
					elif "image" in column_names:
										_a	:    Any         =		'''image'''
					elif "img" in column_names:
										_a	:    Dict         =		'''img'''
					else:
										_a	:    Optional[int]         =		column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					_a	:    List[str]         =		Compose(
					    [
					        Lambda(lambda __a	: img.convert('''RGB'''	) if img.mode != "RGB" else img	),
					        RandomResizedCrop(model_args.image_size		,scale=(0.67, 1.0)		,ratio=(3.0 / 4.0, 4.0 / 3.0)	),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean		,std=image_processor.image_std	),
					    ]	)

					# create mask generator
					_a	:    Tuple         =		MaskGenerator(
					    input_size=model_args.image_size		,mask_patch_size=data_args.mask_patch_size		,model_patch_size=model_args.patch_size		,mask_ratio=data_args.mask_ratio		,)

					def preprocess_images(__a      :			Optional[Any]	):
										_a	:    int         =		[transforms(__a	) for image in examples[image_column_name]]
										_a	:    int         =		[mask_generator() for i in range(len(examples[image_column_name]	)	)]

										return examples

					if training_args.do_train:
										if "train" not in ds:
															raise ValueError('''--do_train requires a train dataset'''	)
										if data_args.max_train_samples is not None:
															_a	:    Union[str, Any]         =		ds['''train'''].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
										# Set the training transforms
										ds["train"].set_transform(__a	)

					if training_args.do_eval:
										if "validation" not in ds:
															raise ValueError('''--do_eval requires a validation dataset'''	)
										if data_args.max_eval_samples is not None:
															_a	:    int         =		(
															    ds['''validation'''].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
															)
										# Set the validation transforms
										ds["validation"].set_transform(__a	)

					# Initialize our trainer
					_a	:    Dict         =		Trainer(
					    model=__a		,args=__a		,train_dataset=ds['''train'''] if training_args.do_train else None		,eval_dataset=ds['''validation'''] if training_args.do_eval else None		,tokenizer=__a		,data_collator=__a		,)

					# Training
					if training_args.do_train:
										_a	:    Union[str, Any]         =		None
										if training_args.resume_from_checkpoint is not None:
															_a	:    Union[str, Any]         =		training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															_a	:    str         =		last_checkpoint
										_a	:    Optional[Any]         =		trainer.train(resume_from_checkpoint=__a	)
										trainer.save_model()
										trainer.log_metrics('''train'''		,train_result.metrics	)
										trainer.save_metrics('''train'''		,train_result.metrics	)
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										_a	:    Tuple         =		trainer.evaluate()
										trainer.log_metrics('''eval'''		,__a	)
										trainer.save_metrics('''eval'''		,__a	)

					# Write model card and (optionally) push to hub
					_a	:    Optional[int]         =		{
					    '''finetuned_from''': model_args.model_name_or_path,
					    '''tasks''': '''masked-image-modeling''',
					    '''dataset''': data_args.dataset_name,
					    '''tags''': ['''masked-image-modeling'''],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**__a	)
					else:
										trainer.create_model_card(**__a	)


if __name__ == "__main__":
			main()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a	:    Dict         =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a	:    List[Any]         =		{0: '''batch''', 1: '''sequence'''}
												_a	:    Union[str, Any]         =		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]       )



