





"""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
a						: int     	=			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						: Tuple     	=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->int:






	'''simple docstring'''






	with open(_lowercase    ,       "rb"					) as f:
		a      :		Optional[int]	=				Image.open(_lowercase					)
		return im.convert("RGB"					)


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       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=a__		,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
							lowerCamelCase   :					Optional[str]      					=field(default=a__		,       metadata={"""help""": """A folder containing the training data."""}						)
							lowerCamelCase   :					Optional[str]      					=field(default=a__		,       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=a__		,       metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    }		,       )
							lowerCamelCase   :					Optional[int]      					=field(
							    default=a__		,       metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
							            """value if set."""
							        )
							    }		,       )






							def     __a   (   self      )	-> Tuple:
								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				:
							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=a__		,       metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a__						)}		,       )
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       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=a__		,       metadata={"""help""": """Name or path of preprocessor config."""}						)
							lowerCamelCase   :					bool      					=field(
							    default=a__		,       metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    }		,       )
							lowerCamelCase   :					bool      					=field(
							    default=a__		,       metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}		,       )






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->Optional[Any]:






	'''simple docstring'''






	a      :		Union[str, Any]	=				torch.stack([example["pixel_values"] for example in examples]					)
	a      :		Dict	=				torch.tensor([example["labels"] for example in examples]					)
	return {"pixel_values": pixel_values, "labels": labels}






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	a      :		List[Any]	=				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      :		Union[str, Any]	=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		a,       a,       a      :		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"    ,       _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()

	a      :		List[str]	=				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.
	a      :		Tuple	=				None
	if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
		a      :		Optional[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:
		a      :		Optional[int]	=				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:
		a      :		int	=				{}
		if data_args.train_dir is not None:
			a      :		List[Any]	=				os.path.join(data_args.train_dir    ,       "**"					)
		if data_args.validation_dir is not None:
			a      :		Optional[Any]	=				os.path.join(data_args.validation_dir    ,       "**"					)
		a      :		List[Any]	=				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.
	a      :		List[Any]	=				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:
		a      :		Tuple	=				dataset["train"].train_test_split(data_args.train_val_split					)
		a      :		List[str]	=				split["train"]
		a      :		Optional[int]	=				split["test"]

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

	# Load the accuracy metric from the datasets package
	a      :		Optional[Any]	=				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						:    Optional[Any]					):
		return metric.compute(predictions=np.argmax(p.predictions    ,       axis=1					)    ,       references=p.label_ids					)

	a      :		str	=				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    ,       )
	a      :		int	=				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    ,       )
	a      :		Dict	=				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:
		a      :		str	=				image_processor.size["shortest_edge"]
	else:
		a      :		Any	=				(image_processor.size["height"], image_processor.size["width"])
	a      :		List[Any]	=				Normalize(mean=image_processor.image_mean    ,       std=image_processor.image_std					)
	a      :		List[Any]	=				Compose(
	    [
	        RandomResizedCrop(_lowercase					),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        normalize,
	    ]					)
	a      :		Optional[Any]	=				Compose(
	    [
	        Resize(_lowercase					),
	        CenterCrop(_lowercase					),
	        ToTensor(),
	        normalize,
	    ]					)

	def train_transforms(_lowercase						:    Optional[int]					):
		a      :		Union[str, Any]	=				[
		    _train_transforms(pil_img.convert("RGB"					)					) for pil_img in example_batch["image"]
		]
		return example_batch

	def val_transforms(_lowercase						:    List[str]					):
		a      :		List[Any]	=				[_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:
			a      :		Union[str, Any]	=				(
			    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:
			a      :		Any	=				(
			    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
	a      :		Optional[Any]	=				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:
		a      :		Any	=				None
		if training_args.resume_from_checkpoint is not None:
			a      :		Tuple	=				training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			a      :		Union[str, Any]	=				last_checkpoint
		a      :		Dict	=				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:
		a      :		Optional[Any]	=				trainer.evaluate()
		trainer.log_metrics("eval"    ,       _lowercase					)
		trainer.save_metrics("eval"    ,       _lowercase					)

	# Write model card and (optionally) push to hub
	a      :		Optional[int]	=				{
	    "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 torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   )  -> Tuple:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =False

        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)

        _lowercase	       =TaConfig(
            vocab_size=lowerCAmelCase							,   d_model=lowerCAmelCase							,   num_heads=lowerCAmelCase							,   d_kv=lowerCAmelCase							,   d_ff=lowerCAmelCase							,   dropout_rate=lowerCAmelCase							,   feed_forward_proj=lowerCAmelCase							,   is_decoder=lowerCAmelCase							,   is_encoder_decoder=lowerCAmelCase							,   )

        _lowercase	       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase					):
              _lowercase	       =TaBlock(lowerCAmelCase					)
              self.encoders.append(lowerCAmelCase					)

        _lowercase	       =TaLayerNorm(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.token_embedder(lowerCAmelCase					)

        _lowercase	       =encoder_input_tokens.shape[1]
        _lowercase	       =torch.arange(lowerCAmelCase							,   device=encoder_input_tokens.device					)
        x += self.position_encoding(lowerCAmelCase					)

        _lowercase	       =self.dropout_pre(lowerCAmelCase					)

        # inverted the attention mask
        _lowercase	       =encoder_input_tokens.size()
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase					)

        for lyr in self.encoders:
              _lowercase	       =lyr(lowerCAmelCase							,   lowerCAmelCase					)[0]
        _lowercase	       =self.layer_norm(lowerCAmelCase					)

        return self.dropout_post(lowerCAmelCase					), encoder_inputs_mask


