
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
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-pretraining/requirements.txt')







@dataclass
class 			__A:
				snake_case_						   =    field(
				    default='''cifar10'''  ,   metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''The column name of the images in the files.'''}			)
				snake_case_						   =    field(default=a  ,   metadata={'''help''': '''A folder containing the training data.'''}			)
				snake_case_						   =    field(default=a  ,   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=a  ,   metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=a  ,   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'''




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







@dataclass
class 			__A:
				snake_case_						   =    field(
				    default=a  ,   metadata={
				        '''help''': (
				            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}			)
				snake_case_						   =    field(
				    default=a  ,   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'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=a  ,   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=a  ,   metadata={'''help''': '''Name or path of preprocessor config.'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=0.75  ,   metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}			)







@dataclass
class 			__A( a			):
				snake_case_						   =    field(
				    default=1E-3  ,   metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}			)



def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    torch.stack([example['''pixel_values'''] for example in examples]  )
				return {"pixel_values": pixel_values}


def     __lowerCAmelCase			(			)       ->  int:
				# 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										=    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.
								__a							,     __a							,     __a										=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								__a							,     __a							,     __a										=    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'''						,    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										=    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										=    None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								__a										=    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.
				__a										=    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.
				__a										=    None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,    a__  ) and data_args.train_val_split > 0.0:
								__a										=    ds['''train'''].train_test_split(data_args.train_val_split  )
								__a										=    split['''train''']
								__a										=    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.
				__a										=    {
				    '''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:
								__a										=    ViTMAEConfig.from_pretrained(model_args.config_name						,    **a__  )
				elif model_args.model_name_or_path:
								__a										=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,    **a__  )
				else:
								__a										=    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:
								__a										=    ViTImageProcessor.from_pretrained(model_args.image_processor_name						,    **a__  )
				elif model_args.model_name_or_path:
								__a										=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,    **a__  )
				else:
								__a										=    ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								__a										=    ViTMAEForPreTraining.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						,    )
				else:
								logger.info('''Training new model from scratch'''  )
								__a										=    ViTMAEForPreTraining(a__  )

				if training_args.do_train:
								__a										=    ds['''train'''].column_names
				else:
								__a										=    ds['''validation'''].column_names

				if data_args.image_column_name is not None:
								__a										=    data_args.image_column_name
				elif "image" in column_names:
								__a										=    '''image'''
				elif "img" in column_names:
								__a										=    '''img'''
				else:
								__a										=    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:
								__a										=    image_processor.size['''shortest_edge''']
				else:
								__a										=    (image_processor.size['''height'''], image_processor.size['''width'''])
				__a										=    Compose(
				    [
				        Lambda(lambda a__  : img.convert('''RGB'''  ) if img.mode != "RGB" else img  ),
				        RandomResizedCrop(a__						,    scale=(0.2, 1.0)						,    interpolation=InterpolationMode.BICUBIC  ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean						,    std=image_processor.image_std  ),
				    ]  )

				def preprocess_images(a__  ):
								__a										=    [transforms(a__  ) 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:
												__a										=    ds['''train'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
								# Set the training transforms
								ds["train"].set_transform(a__  )

				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:
												__a										=    (
												    ds['''validation'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
												)
								# Set the validation transforms
								ds["validation"].set_transform(a__  )

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

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

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

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


def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()




'''simple docstring'''




from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowerCamelCase							:Tuple				    =					logging.get_logger(__name__)







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def     _a	(self       ,      lowercase							):
				if isinstance(lowercase       ,      lowercase							):
							A_	:						Tuple	=     [label.strip() for label in labels.split(""","""							) if label.strip()]
				return labels


	def __call__(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				if len(lowercase							) == 0 or len(lowercase							) == 0:
							raise ValueError("""You must include at least one label and at least one sequence."""							)
				if hypothesis_template.format(labels[0]							) == hypothesis_template:
							raise ValueError(
							    (
							        """The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. """
							        """Make sure the passed template includes formatting syntax such as {{}} where the label should go."""
							    ).format(lowercase							)							)

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Optional[Any]	=     [sequences]

				A_	:						List[Any]	=     []
				for sequence in sequences:
							sequence_pairs.extend([[sequence, hypothesis_template.format(lowercase							)] for label in labels]							)

				return sequence_pairs, sequences







@add_end_docstrings(__UpperCAmelCase )
class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase=ZeroShotClassificationArgumentHandler()       ,      *lowercase       ,      **lowercase							):
				A_	:						str	=     args_parser
				super().__init__(*lowercase       ,      **lowercase							)
				if self.entailment_id == -1:
							logger.warning(
							    """Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to """
							    """-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."""							)


	@property
	def     _a	(self							):
				for label, ind in self.model.config.labelaid.items():
							if label.lower().startswith("""entail"""							):
										return ind
				return -1


	def     _a	(self       ,      lowercase       ,      lowercase=True       ,      lowercase=True       ,      lowercase=TruncationStrategy.ONLY_FIRST       ,      **lowercase							):
				A_	:						Optional[int]	=     self.framework
				if self.tokenizer.pad_token is None:
							# Override for tokenizers not supporting padding
							logger.error(
							    """Tokenizer was not supporting padding necessary for zero-shot, attempting to use """
							    """ `pad_token=eos_token`"""							)
							A_	:						Any	=     self.tokenizer.eos_token
				try:
							A_	:						Optional[Any]	=     self.tokenizer(
							    lowercase       ,      add_special_tokens=lowercase       ,      return_tensors=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      )
				except Exception as e:
							if "too short" in str(lowercase							):
										# tokenizers might yell that we want to truncate
										# to a value that is not even reached by the input.
										# In that case we don't want to truncate.
										# It seems there's not a really better way to catch that
										# exception.

										A_	:						str	=     self.tokenizer(
										    lowercase       ,      add_special_tokens=lowercase       ,      return_tensors=lowercase       ,      padding=lowercase       ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE       ,      )
							else:
										raise e

				return inputs


	def     _a	(self       ,      **lowercase							):
				if kwargs.get("""multi_class"""       ,      lowercase							) is not None:
							A_	:						Union[str, Any]	=     kwargs["""multi_class"""]
							logger.warning(
							    """The `multi_class` argument has been deprecated and renamed to `multi_label`. """
							    """`multi_class` will be removed in a future version of Transformers."""							)
				A_	:						Tuple	=     {}
				if "candidate_labels" in kwargs:
							A_	:						Optional[int]	=     self._args_parser._parse_labels(kwargs["""candidate_labels"""]							)
				if "hypothesis_template" in kwargs:
							A_	:						List[str]	=     kwargs["""hypothesis_template"""]

				A_	:						Union[str, Any]	=     {}
				if "multi_label" in kwargs:
							A_	:						Optional[int]	=     kwargs["""multi_label"""]
				return preprocess_params, {}, postprocess_params


	def __call__(self       ,      lowercase       ,      *lowercase       ,      **lowercase       ,      ):
				if len(lowercase							) == 0:
							pass
				elif len(lowercase							) == 1 and "candidate_labels" not in kwargs:
							A_	:						Any	=     args[0]
				else:
							raise ValueError(F'Unable to understand extra arguments {args}'							)

				return super().__call__(lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase=None       ,      lowercase="This example is {}."							):
				A_,    A_	:						Any	=     self._args_parser(lowercase       ,      lowercase       ,      lowercase							)

				for i, (candidate_label, sequence_pair) in enumerate(zip(lowercase       ,      lowercase							)							):
							A_	:						List[Any]	=     self._parse_and_tokenize([sequence_pair]							)

							yield {
							    "candidate_label": candidate_label,
							    "sequence": sequences[0],
							    "is_last": i == len(lowercase							) - 1,
							    **model_input,
							}


	def     _a	(self       ,      lowercase							):
				A_	:						Dict	=     inputs["""candidate_label"""]
				A_	:						Any	=     inputs["""sequence"""]
				A_	:						List[Any]	=     {k: inputs[k] for k in self.tokenizer.model_input_names}
				A_	:						Optional[int]	=     self.model(**lowercase							)

				A_	:						Optional[Any]	=     {
				    """candidate_label""": candidate_label,
				    """sequence""": sequence,
				    """is_last""": inputs["""is_last"""],
				    **outputs,
				}
				return model_outputs


	def     _a	(self       ,      lowercase       ,      lowercase=False							):
				A_	:						Tuple	=     [outputs["""candidate_label"""] for outputs in model_outputs]
				A_	:						Optional[int]	=     [outputs["""sequence"""] for outputs in model_outputs]
				A_	:						Union[str, Any]	=     np.concatenate([output["""logits"""].numpy() for output in model_outputs]							)
				A_	:						List[str]	=     logits.shape[0]
				A_	:						Optional[int]	=     len(lowercase							)
				A_	:						int	=     N // n
				A_	:						int	=     logits.reshape((num_sequences, n, -1)							)

				if multi_label or len(lowercase							) == 1:
							# softmax over the entailment vs. contradiction dim for each label independently
							A_	:						Dict	=     self.entailment_id
							A_	:						Dict	=     -1 if entailment_id == 0 else 0
							A_	:						str	=     reshaped_outputs[..., [contradiction_id, entailment_id]]
							A_	:						Optional[Any]	=     np.exp(lowercase							) / np.exp(lowercase							).sum(-1       ,      keepdims=lowercase							)
							A_	:						Tuple	=     scores[..., 1]
				else:
							# softmax the "entailment" logits over all candidate labels
							A_	:						Optional[int]	=     reshaped_outputs[..., self.entailment_id]
							A_	:						Any	=     np.exp(lowercase							) / np.exp(lowercase							).sum(-1       ,      keepdims=lowercase							)

				A_	:						List[str]	=     list(reversed(scores[0].argsort()							)							)
				return {
				    "sequence": sequences[0],
				    "labels": [candidate_labels[i] for i in top_inds],
				    "scores": scores[0, top_inds].tolist(),
				}