"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType

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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""owlvit_text_model"""

				def __init__(				self		,    lowerCAmelCase=4_94_08		,    lowerCAmelCase=5_12		,    lowerCAmelCase=20_48		,    lowerCAmelCase=12		,    lowerCAmelCase=8		,    lowerCAmelCase=16		,    lowerCAmelCase="quick_gelu"		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=0.0		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1.0		,    lowerCAmelCase=0		,    lowerCAmelCase=4_94_06		,    lowerCAmelCase=4_94_07		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase    )

										snake_case								=			vocab_size
										snake_case								=			hidden_size
										snake_case								=			intermediate_size
										snake_case								=			num_hidden_layers
										snake_case								=			num_attention_heads
										snake_case								=			max_position_embeddings
										snake_case								=			hidden_act
										snake_case								=			layer_norm_eps
										snake_case								=			attention_dropout
										snake_case								=			initializer_range
										snake_case								=			initializer_factor

				@classmethod
				def 		snake_case (				cls		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										cls._set_token_in_kwargs(lowerCAmelCase    )

										snake_case					,snake_case								=			cls.get_config_dict(lowerCAmelCase		,    **lowerCAmelCase    )

										# get the text config dict if we are loading from OwlViTConfig
										if config_dict.get('model_type'    ) == "owlvit":
																snake_case								=			config_dict['text_config']

										if "model_type" in config_dict and hasattr(cls		,    'model_type'    ) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

										return cls.from_dict(lowerCAmelCase		,    **lowerCAmelCase    )





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""owlvit_vision_model"""

				def __init__(				self		,    lowerCAmelCase=7_68		,    lowerCAmelCase=30_72		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=3		,    lowerCAmelCase=7_68		,    lowerCAmelCase=32		,    lowerCAmelCase="quick_gelu"		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=0.0		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1.0		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase    )

										snake_case								=			hidden_size
										snake_case								=			intermediate_size
										snake_case								=			num_hidden_layers
										snake_case								=			num_attention_heads
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			patch_size
										snake_case								=			hidden_act
										snake_case								=			layer_norm_eps
										snake_case								=			attention_dropout
										snake_case								=			initializer_range
										snake_case								=			initializer_factor

				@classmethod
				def 		snake_case (				cls		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										cls._set_token_in_kwargs(lowerCAmelCase    )

										snake_case					,snake_case								=			cls.get_config_dict(lowerCAmelCase		,    **lowerCAmelCase    )

										# get the vision config dict if we are loading from OwlViTConfig
										if config_dict.get('model_type'    ) == "owlvit":
																snake_case								=			config_dict['vision_config']

										if "model_type" in config_dict and hasattr(cls		,    'model_type'    ) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

										return cls.from_dict(lowerCAmelCase		,    **lowerCAmelCase    )





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""owlvit"""
				_lowerCAmelCase     :    Union[str, Any]			  =						True

				def __init__(				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2.65_92		,    lowerCAmelCase=True		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase    )

										if text_config is None:
																snake_case								=			{}
																logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'    )

										if vision_config is None:
																snake_case								=			{}
																logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'    )

										snake_case								=			OwlViTTextConfig(**lowerCAmelCase    )
										snake_case								=			OwlViTVisionConfig(**lowerCAmelCase    )

										snake_case								=			projection_dim
										snake_case								=			logit_scale_init_value
										snake_case								=			return_dict
										snake_case								=			1.0

				@classmethod
				def 		snake_case (				cls		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										cls._set_token_in_kwargs(lowerCAmelCase    )

										snake_case					,snake_case								=			cls.get_config_dict(lowerCAmelCase		,    **lowerCAmelCase    )

										if "model_type" in config_dict and hasattr(cls		,    'model_type'    ) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

										return cls.from_dict(lowerCAmelCase		,    **lowerCAmelCase    )

				@classmethod
				def 		snake_case (				cls		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			{}
										snake_case								=			text_config
										snake_case								=			vision_config

										return cls.from_dict(lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			copy.deepcopy(self.__dict__    )
										snake_case								=			self.text_config.to_dict()
										snake_case								=			self.vision_config.to_dict()
										snake_case								=			self.__class__.model_type
										return output





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										        ('attention_mask', {0: 'batch', 1: 'sequence'}),
										    ]    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return OrderedDict(
										    [
										        ('logits_per_image', {0: 'batch'}),
										        ('logits_per_text', {0: 'batch'}),
										        ('text_embeds', {0: 'batch'}),
										        ('image_embeds', {0: 'batch'}),
										    ]    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-4

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										snake_case								=			super().generate_dummy_inputs(
										    processor.tokenizer		,    batch_size=lowerCAmelCase		,    seq_length=lowerCAmelCase		,    framework=lowerCAmelCase    )
										snake_case								=			super().generate_dummy_inputs(
										    processor.image_processor		,    batch_size=lowerCAmelCase		,    framework=lowerCAmelCase    )
										return {**text_input_dict, **image_input_dict}

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 14



"""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
SCREAMING_SNAKE_CASE__      						=  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")

SCREAMING_SNAKE_CASE__      						=  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE__      						=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				Dict:





						"""simple docstring"""






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



@dataclass
class   lowerCAmelCase_    :



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






										"""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   lowerCAmelCase_    :



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





def    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				Union[str, Any]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			)					->				List[Any]:





						"""simple docstring"""






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

						# 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()

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

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

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

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

						def train_transforms(_UpperCamelCase       :				Union[str, Any]					):
												snake_case								=			[
												    _train_transforms(pil_img.convert('RGB'					)					) for pil_img in example_batch['image']
												]
												return example_batch

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

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

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

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

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


if __name__ == "__main__":
					main()


