



"""simple docstring"""






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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
_lowercase			:			Optional[Any]										=						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-classification/requirements.txt")

_lowercase			:			List[Any]										=						list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowercase			:			str										=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''rb'''     ) as f:
						lowerCamelCase__		:		Dict		     =Image.open(__lowerCamelCase     )
						return im.convert('''RGB'''     )

@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={
					        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
					    }		,       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
					_a             =							field(default=lowerCAmelCase_		,       metadata={'help': 'A folder containing the training data.'}       )
					_a             =							field(default=lowerCAmelCase_		,       metadata={'help': 'A folder containing the validation data.'}       )
					_a             =							field(
					    default=0.15		,       metadata={'help': 'Percent to split off of train for validation.'}       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }		,       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }		,       )



					def 		snake_case	(	self							:  Dict					)-> str:
								if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
											raise ValueError(
											    '''You must specify either a dataset name from the hub or a train and/or validation directory.'''					)





@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							field(
					    default='google/vit-base-patch16-224-in21k'		,       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		,       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCAmelCase_       )}		,       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}       )
					_a             =							field(
					    default='main'		,       metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,       )
					_a             =							field(default=lowerCAmelCase_		,       metadata={'help': 'Name or path of preprocessor config.'}       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }		,       )
					_a             =							field(
					    default=lowerCAmelCase_		,       metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}		,       )






def 					snake_case__       (		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =torch.stack([example['''pixel_values'''] for example in examples]     )
			lowerCamelCase__		:		Dict		     =torch.tensor([example['''labels'''] for example in examples]     )
			return {"pixel_values": pixel_values, "labels": labels}






def 					snake_case__       (		):
			"""simple docstring"""
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			lowerCamelCase__		:		Dict		     =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.
						lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Any		     =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =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_image_classification'''							,		__lowerCamelCase							,		__lowerCamelCase     )

			# 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()

			lowerCamelCase__		:		Union[str, Any]		     =training_args.get_process_log_level()
			logger.setLevel(__lowerCamelCase     )
			transformers.utils.logging.set_verbosity(__lowerCamelCase     )
			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.
			lowerCamelCase__		:		Tuple		     =None
			if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
						lowerCamelCase__		:		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.'''     )

    # Set seed before initializing model.
			set_seed(training_args.seed     )

			# Initialize our dataset and prepare it for the 'image-classification' task.
			if data_args.dataset_name is not None:
						lowerCamelCase__		:		List[Any]		     =load_dataset(
						    data_args.dataset_name							,		data_args.dataset_config_name							,		cache_dir=model_args.cache_dir							,		task='''image-classification'''							,		use_auth_token=True if model_args.use_auth_token else None							,		)
			else:
						lowerCamelCase__		:		Dict		     ={}
						if data_args.train_dir is not None:
									lowerCamelCase__		:		Optional[Any]		     =os.path.join(data_args.train_dir							,		'''**'''     )
						if data_args.validation_dir is not None:
									lowerCamelCase__		:		Tuple		     =os.path.join(data_args.validation_dir							,		'''**'''     )
						lowerCamelCase__		:		List[str]		     =load_dataset(
						    '''imagefolder'''							,		data_files=__lowerCamelCase							,		cache_dir=model_args.cache_dir							,		task='''image-classification'''							,		)

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

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			lowerCamelCase__		:		Tuple		     =dataset['''train'''].features['''labels'''].names
			lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     ={}, {}
			for i, label in enumerate(__lowerCamelCase     ):
						lowerCamelCase__		:		int		     =str(__lowerCamelCase     )
						lowerCamelCase__		:		Tuple		     =label

			# Load the accuracy metric from the datasets package
			lowerCamelCase__		:		Dict		     =evaluate.load('''accuracy'''     )

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(__lowerCamelCase    :   List[Any]     ):
						return metric.compute(predictions=np.argmax(p.predictions							,		axis=1     )							,		references=p.label_ids     )

			lowerCamelCase__		:		Union[str, Any]		     =AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path							,		num_labels=len(__lowerCamelCase     )							,		labelaid=__lowerCamelCase							,		idalabel=__lowerCamelCase							,		finetuning_task='''image-classification'''							,		cache_dir=model_args.cache_dir							,		revision=model_args.model_revision							,		use_auth_token=True if model_args.use_auth_token else None							,		)
			lowerCamelCase__		:		List[str]		     =AutoModelForImageClassification.from_pretrained(
			    model_args.model_name_or_path							,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )							,		config=__lowerCamelCase							,		cache_dir=model_args.cache_dir							,		revision=model_args.model_revision							,		use_auth_token=True if model_args.use_auth_token else None							,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,		)
			lowerCamelCase__		:		Tuple		     =AutoImageProcessor.from_pretrained(
			    model_args.image_processor_name or model_args.model_name_or_path							,		cache_dir=model_args.cache_dir							,		revision=model_args.model_revision							,		use_auth_token=True if model_args.use_auth_token else None							,		)

			# Define torchvision transforms to be applied to each image.
			if "shortest_edge" in image_processor.size:
						lowerCamelCase__		:		List[str]		     =image_processor.size['''shortest_edge''']
			else:
						lowerCamelCase__		:		str		     =(image_processor.size['''height'''], image_processor.size['''width'''])
			lowerCamelCase__		:		int		     =Normalize(mean=image_processor.image_mean							,		std=image_processor.image_std     )
			lowerCamelCase__		:		Optional[int]		     =Compose(
			    [
			        RandomResizedCrop(__lowerCamelCase     ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]     )
			lowerCamelCase__		:		List[str]		     =Compose(
			    [
			        Resize(__lowerCamelCase     ),
			        CenterCrop(__lowerCamelCase     ),
			        ToTensor(),
			        normalize,
			    ]     )

			def train_transforms(__lowerCamelCase    :   List[Any]     ):
						lowerCamelCase__		:		List[Any]		     =[
						    _train_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']
						]
						return example_batch

			def val_transforms(__lowerCamelCase    :   Any     ):
						lowerCamelCase__		:		Dict		     =[_val_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']]
						return example_batch

			if training_args.do_train:
						if "train" not in dataset:
									raise ValueError('''--do_train requires a train dataset'''     )
						if data_args.max_train_samples is not None:
									lowerCamelCase__		:		Dict		     =(
									    dataset['''train'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
									)
						# Set the training transforms
						dataset["train"].set_transform(__lowerCamelCase     )

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

			# Initalize our trainer
			lowerCamelCase__		:		Optional[int]		     =Trainer(
			    model=__lowerCamelCase							,		args=__lowerCamelCase							,		train_dataset=dataset['''train'''] if training_args.do_train else None							,		eval_dataset=dataset['''validation'''] if training_args.do_eval else None							,		compute_metrics=__lowerCamelCase							,		tokenizer=__lowerCamelCase							,		data_collator=__lowerCamelCase							,		)

			# Training
			if training_args.do_train:
						lowerCamelCase__		:		Union[str, Any]		     =None
						if training_args.resume_from_checkpoint is not None:
									lowerCamelCase__		:		int		     =training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowerCamelCase__		:		List[Any]		     =last_checkpoint
						lowerCamelCase__		:		List[Any]		     =trainer.train(resume_from_checkpoint=__lowerCamelCase     )
						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:
						lowerCamelCase__		:		Any		     =trainer.evaluate()
						trainer.log_metrics('''eval'''							,		__lowerCamelCase     )
						trainer.save_metrics('''eval'''							,		__lowerCamelCase     )

			# Write model card and (optionally) push to hub
			lowerCamelCase__		:		List[Any]		     ={
			    '''finetuned_from''': model_args.model_name_or_path,
			    '''tasks''': '''image-classification''',
			    '''dataset''': data_args.dataset_name,
			    '''tags''': ['''image-classification''', '''vision'''],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**__lowerCamelCase     )
			else:
						trainer.create_model_card(**__lowerCamelCase     )


if __name__ == "__main__":
		main()









"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			if "model" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''model.'''							,		''''''     )
			if "norm1" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''norm1'''							,		'''attention.output.LayerNorm'''     )
			if "norm2" in orig_key:
						lowerCamelCase__		:		List[Any]		     =orig_key.replace('''norm2'''							,		'''output.LayerNorm'''     )
			if "norm" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''norm'''							,		'''LayerNorm'''     )
			if "transformer" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.split('''.'''     )[0].split('''_'''     )[-1]
						lowerCamelCase__		:		Dict		     =orig_key.replace(f'''transformer_{layer_num}'''							,		f'''encoder.layer.{layer_num}'''     )
			if "mha.attn" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''mha.attn'''							,		'''attention.self'''     )
			if "mha" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''mha'''							,		'''attention'''     )
			if "W_q" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''W_q'''							,		'''self.query'''     )
			if "W_k" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''W_k'''							,		'''self.key'''     )
			if "W_v" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''W_v'''							,		'''self.value'''     )
			if "ff1" in orig_key:
						lowerCamelCase__		:		Dict		     =orig_key.replace('''ff1'''							,		'''intermediate.dense'''     )
			if "ff2" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''ff2'''							,		'''output.dense'''     )
			if "ff" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''ff'''							,		'''output.dense'''     )
			if "mlm_class" in orig_key:
						lowerCamelCase__		:		Tuple		     =orig_key.replace('''mlm.mlm_class'''							,		'''cls.predictions.decoder'''     )
			if "mlm" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''mlm'''							,		'''cls.predictions.transform'''     )
			if "cls" not in orig_key:
						lowerCamelCase__		:		Optional[int]		     ='''yoso.''' + orig_key

			return orig_key






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__		:		Optional[Any]		     =orig_state_dict.pop(__lowerCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase__		:		List[str]		     =val

			lowerCamelCase__		:		Optional[int]		     =orig_state_dict['''cls.predictions.decoder.bias''']
			lowerCamelCase__		:		str		     =torch.arange(__lowerCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )['''model_state_dict''']
			lowerCamelCase__		:		List[Any]		     =YosoConfig.from_json_file(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =YosoForMaskedLM(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     =convert_checkpoint_helper(config.max_position_embeddings							,		__lowerCamelCase     )

			print(model.load_state_dict(__lowerCamelCase     )     )
			model.eval()
			model.save_pretrained(__lowerCamelCase     )

			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''     )


if __name__ == "__main__":
		_lowercase			:			int										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase			:			Optional[Any]										=						parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




