






'''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
__lowerCAmelCase       		=      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')

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





def 		_UpperCAmelCase   (							__A							:   str      ):
				with open(__A						,		'''rb'''      ) as f:
								a_		:  Optional[int]											=      Image.open(__A      )
								return im.convert('''RGB'''      )





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






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





def 		_UpperCAmelCase   (							__A							:   Dict      ):
				a_		:  List[str]											=      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 		_UpperCAmelCase   (							):
				# 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.

				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_		:  Optional[Any]											=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								a_				,			a_				,			a_		:  str											=      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'''						,		__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_		:  Any											=      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_		:  int											=      None
				if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
								a_		:  str											=      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_		:  List[str]											=      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_		:  str											=      {}
								if data_args.train_dir is not None:
												a_		:  Optional[Any]											=      os.path.join(data_args.train_dir						,		'''**'''      )
								if data_args.validation_dir is not None:
												a_		:  str											=      os.path.join(data_args.validation_dir						,		'''**'''      )
								a_		:  Dict											=      load_dataset(
								    '''imagefolder'''						,		data_files=__A						,		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_		:  Tuple											=      None if '''validation''' in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,		__A      ) and data_args.train_val_split > 0.0:
								a_		:  Tuple											=      dataset['''train'''].train_test_split(data_args.train_val_split      )
								a_		:  Tuple											=      split['''train''']
								a_		:  Any											=      split['''test''']

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

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

				a_		:  Dict											=      AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path						,		num_labels=len(__A      )						,		labelaid=__A						,		idalabel=__A						,		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_		:  Any											=      AutoModelForImageClassification.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						,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,		)
				a_		:  Union[str, Any]											=      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_		:  int											=      image_processor.size['''shortest_edge''']
				else:
								a_		:  Dict											=      (image_processor.size['''height'''], image_processor.size['''width'''])
				a_		:  str											=      Normalize(mean=image_processor.image_mean						,		std=image_processor.image_std      )
				a_		:  int											=      Compose(
				    [
				        RandomResizedCrop(__A      ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]      )
				a_		:  Union[str, Any]											=      Compose(
				    [
				        Resize(__A      ),
				        CenterCrop(__A      ),
				        ToTensor(),
				        normalize,
				    ]      )

				def train_transforms(__A							:   Optional[int]      ):
								a_		:  Any											=      [
								    _train_transforms(pil_img.convert('''RGB'''      )      ) for pil_img in example_batch['''image''']
								]
								return example_batch

				def val_transforms(__A							:   Optional[Any]      ):
								a_		:  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:
												a_		:  Tuple											=      (
												    dataset['''train'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
												)
								# Set the training transforms
								dataset["train"].set_transform(__A      )

				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_		:  List[str]											=      (
												    dataset['''validation'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
												)
								# Set the validation transforms
								dataset["validation"].set_transform(__A      )

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

				# Training
				if training_args.do_train:
								a_		:  Optional[Any]											=      None
								if training_args.resume_from_checkpoint is not None:
												a_		:  List[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_		:  Any											=      trainer.evaluate()
								trainer.log_metrics('''eval'''						,		__A      )
								trainer.save_metrics('''eval'''						,		__A      )

				# Write model card and (optionally) push to hub
				a_		:  List[str]											=      {
				    '''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(**__A      )
				else:
								trainer.create_model_card(**__A      )


if __name__ == "__main__":
		main()












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




