'''simple docstring'''




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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			List[Any]										=       {}
										if self.train_dir is not None:
																	lowercase_					:			List[str]										=       self.train_dir
										if self.validation_dir is not None:
																	lowercase_					:			Union[str, Any]										=       self.validation_dir
										lowercase_					:			Union[str, Any]										=       data_files if data_files else None





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       field(
			    default=lowerCAmelCase_,     metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=lowerCAmelCase_,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''})
			UpperCamelCase__				=       field(
			    default=lowerCAmelCase_,     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'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=lowerCAmelCase_,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''})
			UpperCamelCase__				=       field(
			    default='''main''',     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},     )
			UpperCamelCase__				=       field(default=lowerCAmelCase_,     metadata={'''help''': '''Name or path of preprocessor config.'''})
			UpperCamelCase__				=       field(
			    default=lowerCAmelCase_,     metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=0.75,     metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''})
			UpperCamelCase__				=       field(
			    default=lowerCAmelCase_,     metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''})





@dataclass
class     __magic_name__						(    lowerCAmelCase_):
			UpperCamelCase__				=       field(
			    default=1e-3,     metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''})




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      Any:
							lowercase_					:			Union[str, Any]										=       torch.stack([example["""pixel_values"""] for example in examples] )
							return {"pixel_values": pixel_values}




def 							lowerCamelCase	(      )			->      Optional[Any]:
							# 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.

							lowercase_					:			Optional[int]										=       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.
														lowercase_					:			Optional[Any]										=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
							else:
														lowercase_					:			Union[str, Any]										=       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()

							lowercase_					:			Optional[Any]										=       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.
							lowercase_					:			Optional[Any]										=       None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														lowercase_					:			Optional[int]										=       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.
							lowercase_					:			Any										=       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.
							lowercase_					:			str										=       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:
														lowercase_					:			List[str]										=       ds['train'].train_test_split(data_args.train_val_split )
														lowercase_					:			str										=       split['train']
														lowercase_					:			List[Any]										=       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.
							lowercase_					:			int										=       {
							    '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:
														lowercase_					:			Union[str, Any]										=       ViTMAEConfig.from_pretrained(model_args.config_name      ,      **_SCREAMING_SNAKE_CASE )
							elif model_args.model_name_or_path:
														lowercase_					:			Optional[int]										=       ViTMAEConfig.from_pretrained(model_args.model_name_or_path      ,      **_SCREAMING_SNAKE_CASE )
							else:
														lowercase_					:			str										=       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:
														lowercase_					:			Dict										=       ViTImageProcessor.from_pretrained(model_args.image_processor_name      ,      **_SCREAMING_SNAKE_CASE )
							elif model_args.model_name_or_path:
														lowercase_					:			Optional[int]										=       ViTImageProcessor.from_pretrained(model_args.model_name_or_path      ,      **_SCREAMING_SNAKE_CASE )
							else:
														lowercase_					:			Tuple										=       ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														lowercase_					:			Optional[int]										=       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""" )
														lowercase_					:			Dict										=       ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE )

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

							if data_args.image_column_name is not None:
														lowercase_					:			List[str]										=       data_args.image_column_name
							elif "image" in column_names:
														lowercase_					:			Optional[Any]										=       'image'
							elif "img" in column_names:
														lowercase_					:			int										=       'img'
							else:
														lowercase_					:			List[Any]										=       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:
														lowercase_					:			Tuple										=       image_processor.size['shortest_edge']
							else:
														lowercase_					:			Union[str, Any]										=       (image_processor.size['height'], image_processor.size['width'])
							lowercase_					:			Dict										=       Compose(
							    [
							        Lambda(lambda UpperCAmelCase__ : 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(UpperCAmelCase__       :					List[Any] ):
														lowercase_					:			Optional[int]										=       [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:
																					lowercase_					:			Any										=       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:
																					lowercase_					:			Tuple										=       (
																					    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
							lowercase_					:			int										=       (
							    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
							)
							if training_args.base_learning_rate is not None:
														lowercase_					:			Tuple										=       training_args.base_learning_rate * total_train_batch_size / 256

							# Initialize our trainer
							lowercase_					:			Dict										=       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:
														lowercase_					:			Union[str, Any]										=       None
														if training_args.resume_from_checkpoint is not None:
																					lowercase_					:			List[str]										=       training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowercase_					:			int										=       last_checkpoint
														lowercase_					:			Optional[int]										=       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:
														lowercase_					:			Dict										=       trainer.evaluate()
														trainer.log_metrics("""eval"""      ,      _SCREAMING_SNAKE_CASE )
														trainer.save_metrics("""eval"""      ,      _SCREAMING_SNAKE_CASE )

							# Write model card and (optionally) push to hub
							lowercase_					:			List[str]										=       {
							    '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 							lowerCamelCase	(      UpperCAmelCase__       :					List[str] )			->      Optional[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()









'''simple docstring'''



from typing import Mapping

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


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "umt5"
	A_				=    ["past_key_values"]




	def __init__(   self						,				__a=25_0112						,				__a=512						,				__a=64						,				__a=1024						,				__a=8						,				__a=None						,				__a=6						,				__a=32						,				__a=128						,				__a=0.1						,				__a=1E-6						,				__a=1.0						,				__a="gated-gelu"						,				__a=True						,				__a=True						,				__a="T5Tokenizer"						,				__a=True						,				__a=0						,				__a=1						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    is_encoder_decoder=__a						,				tokenizer_class=__a						,				tie_word_embeddings=__a						,				pad_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)
						__a			:       Any					    =  vocab_size
						__a			:       Any					    =  d_model
						__a			:       str					    =  d_kv
						__a			:       Dict					    =  d_ff
						__a			:       Union[str, Any]					    =  num_layers
						__a			:       int					    =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a			:       Optional[int]					    =  num_heads
						__a			:       Tuple					    =  relative_attention_num_buckets
						__a			:       Optional[Any]					    =  relative_attention_max_distance
						__a			:       Optional[int]					    =  dropout_rate
						__a			:       List[Any]					    =  layer_norm_epsilon
						__a			:       int					    =  initializer_factor
						__a			:       Union[str, Any]					    =  feed_forward_proj
						__a			:       Any					    =  use_cache

						__a			:       List[Any]					    =  self.feed_forward_proj.split('-'							)
						__a			:       Dict					    =  act_info[-1]
						__a			:       Dict					    =  act_info[0] == 'gated'

						if len(__a							) > 1 and act_info[0] != "gated" or len(__a							) > 2:
											raise ValueError(
											    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''							)

						if feed_forward_proj == "gated-gelu":
											__a			:       Optional[int]					    =  'gelu_new'




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.d_model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_heads





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_layers





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  {
						    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
						    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
						}
						if self.use_past:
											__a			:       Dict					    =  'past_encoder_sequence + sequence'
											__a			:       Tuple					    =  {0: 'batch'}
											__a			:       Tuple					    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											__a			:       List[Any]					    =  {0: 'batch', 1: 'decoder_sequence'}
											__a			:       int					    =  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(__a						,				direction='inputs'							)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 13





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 5E-4



