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
__a  :     List[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""")

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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				with open(lowercase							,			'''rb'''							) as f:
								__lowercase 			=							Image.open(lowercase							)
								return im.convert('''RGB'''							)




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							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    _UpperCamelCase      :


			"""simple docstring"""






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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							torch.stack([example['''pixel_values'''] for example in examples]							)
				__lowercase 			=							torch.tensor([example['''labels'''] for example in examples]							)
				return {"pixel_values": pixel_values, "labels": labels}





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							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.
								__lowercase			,     __lowercase			,     __lowercase 			=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
				else:
								__lowercase			,     __lowercase			,     __lowercase 			=							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'''							,			lowercase							,			lowercase							)

				# 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()

				__lowercase 			=							training_args.get_process_log_level()
				logger.setLevel(lowercase							)
				transformers.utils.logging.set_verbosity(lowercase							)
				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.
				__lowercase 			=							None
				if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
								__lowercase 			=							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:
								__lowercase 			=							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:
								__lowercase 			=							{}
								if data_args.train_dir is not None:
												__lowercase 			=							os.path.join(data_args.train_dir							,			'''**'''							)
								if data_args.validation_dir is not None:
												__lowercase 			=							os.path.join(data_args.validation_dir							,			'''**'''							)
								__lowercase 			=							load_dataset(
								    '''imagefolder'''							,			data_files=lowercase							,			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.
				__lowercase 			=							None if '''validation''' in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split							,			lowercase							) and data_args.train_val_split > 0.0:
								__lowercase 			=							dataset['''train'''].train_test_split(data_args.train_val_split							)
								__lowercase 			=							split['''train''']
								__lowercase 			=							split['''test''']

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

				# Load the accuracy metric from the datasets package
				__lowercase 			=							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(lowercase							):
								return metric.compute(predictions=np.argmax(p.predictions							,			axis=1							)							,			references=p.label_ids							)

				__lowercase 			=							AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path							,			num_labels=len(lowercase							)							,			labelaid=lowercase							,			idalabel=lowercase							,			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							,			)
				__lowercase 			=							AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path							,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)							,			config=lowercase							,			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							,			)
				__lowercase 			=							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:
								__lowercase 			=							image_processor.size['''shortest_edge''']
				else:
								__lowercase 			=							(image_processor.size['''height'''], image_processor.size['''width'''])
				__lowercase 			=							Normalize(mean=image_processor.image_mean							,			std=image_processor.image_std							)
				__lowercase 			=							Compose(
				    [
				        RandomResizedCrop(lowercase							),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]							)
				__lowercase 			=							Compose(
				    [
				        Resize(lowercase							),
				        CenterCrop(lowercase							),
				        ToTensor(),
				        normalize,
				    ]							)

				def train_transforms(lowercase							):
								__lowercase 			=							[
								    _train_transforms(pil_img.convert('''RGB'''							)							) for pil_img in example_batch['''image''']
								]
								return example_batch

				def val_transforms(lowercase							):
								__lowercase 			=							[_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:
												__lowercase 			=							(
												    dataset['''train'''].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
												)
								# Set the training transforms
								dataset["train"].set_transform(lowercase							)

				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:
												__lowercase 			=							(
												    dataset['''validation'''].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
												)
								# Set the validation transforms
								dataset["validation"].set_transform(lowercase							)

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

				# Training
				if training_args.do_train:
								__lowercase 			=							None
								if training_args.resume_from_checkpoint is not None:
												__lowercase 			=							training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												__lowercase 			=							last_checkpoint
								__lowercase 			=							trainer.train(resume_from_checkpoint=lowercase							)
								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:
								__lowercase 			=							trainer.evaluate()
								trainer.log_metrics('''eval'''							,			lowercase							)
								trainer.save_metrics('''eval'''							,			lowercase							)

				# Write model card and (optionally) push to hub
				__lowercase 			=							{
				    '''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(**lowercase							)
				else:
								trainer.create_model_card(**lowercase							)


if __name__ == "__main__":
		main()
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a  :     int       =  logging.get_logger(__name__)

__a  :     str       =  {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[int]			 =						'''deta'''
			__a  : Optional[int]			 =						{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}





			def __init__(   self      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=9_00      ,   lowerCAmelCase__=20_48      ,   lowerCAmelCase__=6      ,   lowerCAmelCase__=20_48      ,   lowerCAmelCase__=8      ,   lowerCAmelCase__=6      ,   lowerCAmelCase__=10_24      ,   lowerCAmelCase__=8      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__="relu"      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1.0      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__="sine"      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=3_00      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.25      ,   **lowerCAmelCase__      ,   )     ->					List[Any]:

							'''simple docstring'''


							if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
											__lowercase 			=							CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']						)
							else:
											if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						):
															__lowercase 			=							backbone_config.pop('''model_type'''						)
															__lowercase 			=							CONFIG_MAPPING[backbone_model_type]
															__lowercase 			=							config_class.from_dict(lowerCAmelCase__						)

							__lowercase 			=							backbone_config
							__lowercase 			=							num_queries
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							d_model
							__lowercase 			=							encoder_ffn_dim
							__lowercase 			=							encoder_layers
							__lowercase 			=							encoder_attention_heads
							__lowercase 			=							decoder_ffn_dim
							__lowercase 			=							decoder_layers
							__lowercase 			=							decoder_attention_heads
							__lowercase 			=							dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							activation_function
							__lowercase 			=							init_std
							__lowercase 			=							init_xavier_std
							__lowercase 			=							encoder_layerdrop
							__lowercase 			=							auxiliary_loss
							__lowercase 			=							position_embedding_type
							# deformable attributes
							__lowercase 			=							num_feature_levels
							__lowercase 			=							encoder_n_points
							__lowercase 			=							decoder_n_points
							__lowercase 			=							two_stage
							__lowercase 			=							two_stage_num_proposals
							__lowercase 			=							with_box_refine
							__lowercase 			=							assign_first_stage
							if two_stage is True and with_box_refine is False:
											raise ValueError('''If two_stage is True, with_box_refine must be True.'''						)
							# Hungarian matcher
							__lowercase 			=							class_cost
							__lowercase 			=							bbox_cost
							__lowercase 			=							giou_cost
							# Loss coefficients
							__lowercase 			=							mask_loss_coefficient
							__lowercase 			=							dice_loss_coefficient
							__lowercase 			=							bbox_loss_coefficient
							__lowercase 			=							giou_loss_coefficient
							__lowercase 			=							eos_coefficient
							__lowercase 			=							focal_alpha
							super().__init__(is_encoder_decoder=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.d_model





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							copy.deepcopy(self.__dict__						)
							__lowercase 			=							self.backbone_config.to_dict()
							__lowercase 			=							self.__class__.model_type
							return output