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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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  : 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""")

lowercase  : Tuple						   =							list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowercase  : Any						   =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class __snake_case		:
    _a		:						Optional[str]=    field(
        default="cifar10" ,     metadata={"help": "Name of a dataset from the datasets package"}					)
    _a		:						Optional[str]=    field(
        default=lowerCAmelCase ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}					)
    _a		:						Optional[str]=    field(
        default=lowerCAmelCase ,     metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."} ,     )
    _a		:						Optional[str]=    field(default=lowerCAmelCase ,     metadata={"help": "A folder containing the training data."}					)
    _a		:						Optional[str]=    field(default=lowerCAmelCase ,     metadata={"help": "A folder containing the validation data."}					)
    _a		:						Optional[float]=    field(
        default=0.15 ,     metadata={"help": "Percent to split off of train for validation."}					)
    _a		:						int=    field(default=32 ,     metadata={"help": "The size of the square patches to use for masking."}					)
    _a		:						float=    field(
        default=0.6 ,     metadata={"help": "Percentage of patches to mask."} ,     )
    _a		:						Optional[int]=    field(
        default=lowerCAmelCase ,     metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        } ,     )
    _a		:						Optional[int]=    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				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				{}
           if self.train_dir is not None:
                  lowercase							:					str	     =				self.train_dir
           if self.validation_dir is not None:
                  lowercase							:					int	     =				self.validation_dir
           lowercase							:					List[str]	     =				data_files if data_files else None






@dataclass
class __snake_case		:
    _a		:						str=    field(
        default=lowerCAmelCase ,     metadata={
            "help": (
                "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
                "checkpoint identifier on the hub. "
                "Don't set if you want to train a model from scratch."
            )
        } ,     )
    _a		:						Optional[str]=    field(
        default=lowerCAmelCase ,     metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase					)} ,     )
    _a		:						Optional[str]=    field(
        default=lowerCAmelCase ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
    _a		:						Optional[str]=    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"
            )
        } ,     )
    _a		:						Optional[str]=    field(
        default=lowerCAmelCase ,     metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"} ,     )
    _a		:						str=    field(
        default="main" ,     metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,     )
    _a		:						str=    field(default=lowerCAmelCase ,     metadata={"help": "Name or path of preprocessor config."}					)
    _a		:						bool=    field(
        default=lowerCAmelCase ,     metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        } ,     )
    _a		:						Optional[int]=    field(
        default=lowerCAmelCase ,     metadata={
            "help": (
                "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
            )
        } ,     )
    _a		:						Optional[int]=    field(
        default=lowerCAmelCase ,     metadata={
            "help": (
                "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
            )
        } ,     )
    _a		:						Optional[int]=    field(
        default=lowerCAmelCase ,     metadata={"help": "Stride to use for the encoder."} ,     )






class __snake_case		:
    def __init__(  self		,snake_case=192		,snake_case=32		,snake_case=4		,snake_case=0.6				):



           '''simple docstring'''







           lowercase							:					str	     =				input_size
           lowercase							:					List[str]	     =				mask_patch_size
           lowercase							:					int	     =				model_patch_size
           lowercase							:					List[Any]	     =				mask_ratio

           if self.input_size % self.mask_patch_size != 0:
                  raise ValueError("""Input size must be divisible by mask patch size"""				)
           if self.mask_patch_size % self.model_patch_size != 0:
                  raise ValueError("""Mask patch size must be divisible by model patch size"""				)

           lowercase							:					Dict	     =				self.input_size // self.mask_patch_size
           lowercase							:					Optional[Any]	     =				self.mask_patch_size // self.model_patch_size

           lowercase							:					int	     =				self.rand_size**2
           lowercase							:					int	     =				int(np.ceil(self.token_count * self.mask_ratio				)				)
    def __call__(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				np.random.permutation(self.token_count				)[: self.mask_count]
           lowercase							:					Union[str, Any]	     =				np.zeros(self.token_count		,dtype=snake_case				)
           lowercase							:					str	     =				1

           lowercase							:					List[Any]	     =				mask.reshape((self.rand_size, self.rand_size)				)
           lowercase							:					Optional[Any]	     =				mask.repeat(self.scale		,axis=0				).repeat(self.scale		,axis=1				)

           return torch.tensor(mask.flatten()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					Any	     =				torch.stack([example["""pixel_values"""] for example in examples]    )
       lowercase							:					List[str]	     =				torch.stack([example["""mask"""] for example in examples]    )
       return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 						_snake_case(  )			->   List[str]:
       # 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[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.
              lowercase     ,      lowercase     ,      lowercase							:					Optional[int]	     =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              lowercase     ,      lowercase     ,      lowercase							:					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_mim""" ,     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							:					str	     =				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[int]	     =				None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
              lowercase							:					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							:					List[str]	     =				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							:					Optional[Any]	     =				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							:					Dict	     =				ds["""train"""].train_test_split(data_args.train_val_split    )
              lowercase							:					Dict	     =				split["""train"""]
              lowercase							:					Dict	     =				split["""test"""]

       # Create config
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       lowercase							:					str	     =				{
           """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_or_path:
              lowercase							:					List[str]	     =				AutoConfig.from_pretrained(model_args.config_name_or_path ,     **SCREAMING_SNAKE_CASE__    )
       elif model_args.model_name_or_path:
              lowercase							:					Any	     =				AutoConfig.from_pretrained(model_args.model_name_or_path ,     **SCREAMING_SNAKE_CASE__    )
       else:
              lowercase							:					List[Any]	     =				CONFIG_MAPPING[model_args.model_type]()
              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}"    )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
       if hasattr(SCREAMING_SNAKE_CASE__ ,     """decoder_type"""    ):
              lowercase							:					str	     =				"""simmim"""

       # adapt config
       lowercase							:					Any	     =				model_args.image_size if model_args.image_size is not None else config.image_size
       lowercase							:					Tuple	     =				model_args.patch_size if model_args.patch_size is not None else config.patch_size
       lowercase							:					Dict	     =				(
           model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
       )

       config.update(
           {
               """image_size""": model_args.image_size,
               """patch_size""": model_args.patch_size,
               """encoder_stride""": model_args.encoder_stride,
           }    )

       # create image processor
       if model_args.image_processor_name:
              lowercase							:					Union[str, Any]	     =				AutoImageProcessor.from_pretrained(model_args.image_processor_name ,     **SCREAMING_SNAKE_CASE__    )
       elif model_args.model_name_or_path:
              lowercase							:					Tuple	     =				AutoImageProcessor.from_pretrained(model_args.model_name_or_path ,     **SCREAMING_SNAKE_CASE__    )
       else:
              lowercase							:					str	     =				{
                  conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
              }
              lowercase							:					str	     =				IMAGE_PROCESSOR_TYPES[model_args.model_type]()

       # create model
       if model_args.model_name_or_path:
              lowercase							:					Any	     =				AutoModelForMaskedImageModeling.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							:					Union[str, Any]	     =				AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__    )

       if training_args.do_train:
              lowercase							:					List[Any]	     =				ds["""train"""].column_names
       else:
              lowercase							:					Any	     =				ds["""validation"""].column_names

       if data_args.image_column_name is not None:
              lowercase							:					List[Any]	     =				data_args.image_column_name
       elif "image" in column_names:
              lowercase							:					Union[str, Any]	     =				"""image"""
       elif "img" in column_names:
              lowercase							:					List[Any]	     =				"""img"""
       else:
              lowercase							:					List[Any]	     =				column_names[0]

       # transformations as done in original SimMIM paper
       # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
       lowercase							:					Tuple	     =				Compose(
           [
               Lambda(lambda SCREAMING_SNAKE_CASE__    : img.convert("""RGB"""    ) if img.mode != "RGB" else img    ),
               RandomResizedCrop(model_args.image_size ,     scale=(0.67, 1.0) ,     ratio=(3.0 / 4.0, 4.0 / 3.0)    ),
               RandomHorizontalFlip(),
               ToTensor(),
               Normalize(mean=image_processor.image_mean ,     std=image_processor.image_std    ),
           ]    )

       # create mask generator
       lowercase							:					Optional[Any]	     =				MaskGenerator(
           input_size=model_args.image_size ,     mask_patch_size=data_args.mask_patch_size ,     model_patch_size=model_args.patch_size ,     mask_ratio=data_args.mask_ratio ,     )

       def preprocess_images(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Dict	     =				[transforms(SCREAMING_SNAKE_CASE__    ) for image in examples[image_column_name]]
              lowercase							:					Optional[Any]	     =				[mask_generator() for i in range(len(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							:					Tuple	     =				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							:					Optional[Any]	     =				(
                         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__    )

       # Initialize our trainer
       lowercase							:					Union[str, Any]	     =				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							:					Tuple	     =				None
              if training_args.resume_from_checkpoint is not None:
                     lowercase							:					Any	     =				training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     lowercase							:					str	     =				last_checkpoint
              lowercase							:					str	     =				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							:					str	     =				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							:					Optional[int]	     =				{
           """finetuned_from""": model_args.model_name_or_path,
           """tasks""": """masked-image-modeling""",
           """dataset""": data_args.dataset_name,
           """tags""": ["""masked-image-modeling"""],
       }
       if training_args.push_to_hub:
              trainer.push_to_hub(**SCREAMING_SNAKE_CASE__    )
       else:
              trainer.create_model_card(**SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  main()


from typing import Mapping

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


lowercase  : List[str]						   =							logging.get_logger(__name__)

lowercase  : Tuple						   =							{
    """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 __snake_case		(				lowerCAmelCase					):
    _a		:						List[str]=    "umt5"
    _a		:						Optional[Any]=    ["past_key_values"]
    def __init__(  self		,snake_case=250112		,snake_case=512		,snake_case=64		,snake_case=1024		,snake_case=8		,snake_case=None		,snake_case=6		,snake_case=32		,snake_case=128		,snake_case=0.1		,snake_case=1e-6		,snake_case=1.0		,snake_case="gated-gelu"		,snake_case=True		,snake_case=True		,snake_case="T5Tokenizer"		,snake_case=True		,snake_case=0		,snake_case=1		,snake_case=0		,**snake_case		,):



           '''simple docstring'''







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

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

           if len(snake_case				) > 1 and act_info[0] != "gated" or len(snake_case				) > 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":
                  lowercase							:					int	     =				"""gelu_new"""
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.d_model
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_heads
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_layers






class __snake_case		(				lowerCAmelCase					):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""				)

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



           '''simple docstring'''







           return 13
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 5e-4
