


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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
__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-pretraining/requirements.txt")




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default="""cifar10"""      , metadata={"""help""": """Name of a dataset from the datasets package"""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """The column name of the images in the files."""}    )
			lowercase						= field(default=__snake_case      , metadata={"""help""": """A folder containing the training data."""}    )
			lowercase						= field(default=__snake_case      , metadata={"""help""": """A folder containing the validation data."""}    )
			lowercase						= field(
			    default=0.15      , metadata={"""help""": """Percent to split off of train for validation."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }      , )




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					{}
						if self.train_dir is not None:
									UpperCamelCase         =					self.train_dir
						if self.validation_dir is not None:
									UpperCamelCase         =					self.validation_dir
						UpperCamelCase         =					data_files if data_files else None




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """Override some existing default config settings when a model is trained from scratch. Example: """
			            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
			lowercase						= field(
			    default="""main"""      , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      , )
			lowercase						= field(default=__snake_case      , metadata={"""help""": """Name or path of preprocessor config."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }      , )
			lowercase						= field(
			    default=0.75      , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )




@dataclass
class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= field(
			    default=1e-3      , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}    )






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




			UpperCamelCase         =					torch.stack([example["""pixel_values"""] for example in examples]	)
			return {"pixel_values": pixel_values}






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)	)
			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.
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
			else:
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					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_mae"""       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# 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()

			UpperCamelCase         =					training_args.get_process_log_level()
			logger.setLevel(SCREAMING_SNAKE_CASE_	)
			transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_	)
			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.
			UpperCamelCase         =					None
			if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCamelCase         =					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."""	)

    # Initialize our dataset.
			UpperCamelCase         =					load_dataset(
			    data_args.dataset_name       ,  data_args.dataset_config_name       ,  data_files=data_args.data_files       ,  cache_dir=model_args.cache_dir       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )

			# If we don't have a validation split, split off a percentage of train as validation.
			UpperCamelCase         =					None if """validation""" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split       ,  SCREAMING_SNAKE_CASE_	) and data_args.train_val_split > 0.0:
						UpperCamelCase         =					ds["""train"""].train_test_split(data_args.train_val_split	)
						UpperCamelCase         =					split["""train"""]
						UpperCamelCase         =					split["""test"""]

			# Load pretrained model and image processor
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			UpperCamelCase         =					{
			    """cache_dir""": model_args.cache_dir,
			    """revision""": model_args.model_revision,
			    """use_auth_token""": True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						UpperCamelCase         =					ViTMAEConfig.from_pretrained(model_args.config_name       ,  **SCREAMING_SNAKE_CASE_	)
			elif model_args.model_name_or_path:
						UpperCamelCase         =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,  **SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					ViTMAEConfig()
						logger.warning("""You are instantiating a new config instance from scratch."""	)
						if model_args.config_overrides is not None:
									logger.info(f'Overriding config: {model_args.config_overrides}'	)
									config.update_from_string(model_args.config_overrides	)
									logger.info(f'New config: {config}'	)

    # adapt config
			config.update(
			    {
			        """mask_ratio""": model_args.mask_ratio,
			        """norm_pix_loss""": model_args.norm_pix_loss,
			    }	)

			# create image processor
			if model_args.image_processor_name:
						UpperCamelCase         =					ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,  **SCREAMING_SNAKE_CASE_	)
			elif model_args.model_name_or_path:
						UpperCamelCase         =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,  **SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						UpperCamelCase         =					ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path       ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)       ,  config=SCREAMING_SNAKE_CASE_       ,  cache_dir=model_args.cache_dir       ,  revision=model_args.model_revision       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
			else:
						logger.info("""Training new model from scratch"""	)
						UpperCamelCase         =					ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_	)

			if training_args.do_train:
						UpperCamelCase         =					ds["""train"""].column_names
			else:
						UpperCamelCase         =					ds["""validation"""].column_names

			if data_args.image_column_name is not None:
						UpperCamelCase         =					data_args.image_column_name
			elif "image" in column_names:
						UpperCamelCase         =					"""image"""
			elif "img" in column_names:
						UpperCamelCase         =					"""img"""
			else:
						UpperCamelCase         =					column_names[0]

			# transformations as done in original MAE paper
			# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
			if "shortest_edge" in image_processor.size:
						UpperCamelCase         =					image_processor.size["""shortest_edge"""]
			else:
						UpperCamelCase         =					(image_processor.size["""height"""], image_processor.size["""width"""])
			UpperCamelCase         =					Compose(
			    [
			        Lambda(lambda SCREAMING_SNAKE_CASE_	: img.convert("""RGB"""	) if img.mode != "RGB" else img	),
			        RandomResizedCrop(SCREAMING_SNAKE_CASE_       ,  scale=(0.2, 1.0)       ,  interpolation=InterpolationMode.BICUBIC	),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean       ,  std=image_processor.image_std	),
			    ]	)

			def preprocess_images(SCREAMING_SNAKE_CASE_   :		Optional[Any]	):
						UpperCamelCase         =					[transforms(SCREAMING_SNAKE_CASE_	) for image in examples[image_column_name]]
						return examples

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

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

			# Compute absolute learning rate
			UpperCamelCase         =					(
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						UpperCamelCase         =					training_args.base_learning_rate * total_train_batch_size / 256

			# Initialize our trainer
			UpperCamelCase         =					Trainer(
			    model=SCREAMING_SNAKE_CASE_       ,  args=SCREAMING_SNAKE_CASE_       ,  train_dataset=ds["""train"""] if training_args.do_train else None       ,  eval_dataset=ds["""validation"""] if training_args.do_eval else None       ,  tokenizer=SCREAMING_SNAKE_CASE_       ,  data_collator=SCREAMING_SNAKE_CASE_       ,  )

			# Training
			if training_args.do_train:
						UpperCamelCase         =					None
						if training_args.resume_from_checkpoint is not None:
									UpperCamelCase         =					training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									UpperCamelCase         =					last_checkpoint
						UpperCamelCase         =					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_	)
						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:
						UpperCamelCase         =					trainer.evaluate()
						trainer.log_metrics("""eval"""       ,  SCREAMING_SNAKE_CASE_	)
						trainer.save_metrics("""eval"""       ,  SCREAMING_SNAKE_CASE_	)

			# Write model card and (optionally) push to hub
			UpperCamelCase         =					{
			    """tasks""": """masked-auto-encoding""",
			    """dataset""": data_args.dataset_name,
			    """tags""": ["""masked-auto-encoding"""],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**SCREAMING_SNAKE_CASE_	)
			else:
						trainer.create_model_card(**SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




			main()


if __name__ == "__main__":
			main()








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

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

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




