





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
A_												=     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")

A_												=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A_												=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    field(
					    default="cifar10"							,       metadata={"help": "Name of a dataset from the datasets package"}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}							,       )
					A__				 =    field(default=lowercase_							,       metadata={"help": "A folder containing the training data."}   )
					A__				 =    field(default=lowercase_							,       metadata={"help": "A folder containing the validation data."}   )
					A__				 =    field(
					    default=0.15							,       metadata={"help": "Percent to split off of train for validation."}   )
					A__				 =    field(default=3_2							,       metadata={"help": "The size of the square patches to use for masking."}   )
					A__				 =    field(
					    default=0.6							,       metadata={"help": "Percentage of patches to mask."}							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }							,       )





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       {}
										if self.train_dir is not None:
															lowerCamelCase__       =       self.train_dir
										if self.validation_dir is not None:
															lowerCamelCase__       =       self.validation_dir
										lowerCamelCase__       =       data_files if data_files else None

@dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    field(
					    default=lowercase_							,       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__				 =    field(
					    default=lowercase_							,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowercase_   )}							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
					A__				 =    field(
					    default=lowercase_							,       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__				 =    field(
					    default=lowercase_							,       metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}							,       )
					A__				 =    field(
					    default="main"							,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,       )
					A__				 =    field(default=lowercase_							,       metadata={"help": "Name or path of preprocessor config."}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={
					        "help": (
					            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
					        )
					    }							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={
					        "help": (
					            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
					        )
					    }							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "Stride to use for the encoder."}							,       )

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase=192	,  _lowerCAmelCase=32	,  _lowerCAmelCase=4	,  _lowerCAmelCase=0.6	):
										lowerCamelCase__       =       input_size
										lowerCamelCase__       =       mask_patch_size
										lowerCamelCase__       =       model_patch_size
										lowerCamelCase__       =       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"	)

										lowerCamelCase__       =       self.input_size // self.mask_patch_size
										lowerCamelCase__       =       self.mask_patch_size // self.model_patch_size

										lowerCamelCase__       =       self.rand_size**2
										lowerCamelCase__       =       int(np.ceil(self.token_count * self.mask_ratio	)	)






					def __call__( self	):
										lowerCamelCase__       =       np.random.permutation(self.token_count	)[: self.mask_count]
										lowerCamelCase__       =       np.zeros(self.token_count	,  dtype=_lowerCAmelCase	)
										lowerCamelCase__       =       1

										lowerCamelCase__       =       mask.reshape((self.rand_size, self.rand_size)	)
										lowerCamelCase__       =       mask.repeat(self.scale	,  axis=0	).repeat(self.scale	,  axis=1	)

										return torch.tensor(mask.flatten()	)





def    __UpperCamelCase      (    a) ->Optional[int]:
					lowerCamelCase__       =       torch.stack([example["pixel_values"] for example in examples])
					lowerCamelCase__       =       torch.stack([example["mask"] for example in examples])
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def    __UpperCamelCase      (    ) ->Tuple:
					# 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.

					lowerCamelCase__       =       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.
										lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
					else:
										lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       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",     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()

					lowerCamelCase__       =       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.
					lowerCamelCase__       =       None
					if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
										lowerCamelCase__       =       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.
					lowerCamelCase__       =       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.
					lowerCamelCase__       =       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:
										lowerCamelCase__       =       ds["train"].train_test_split(data_args.train_val_split)
										lowerCamelCase__       =       split["train"]
										lowerCamelCase__       =       split["test"]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					lowerCamelCase__       =       {
					    "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:
										lowerCamelCase__       =       AutoConfig.from_pretrained(model_args.config_name_or_path,     **a)
					elif model_args.model_name_or_path:
										lowerCamelCase__       =       AutoConfig.from_pretrained(model_args.model_name_or_path,     **a)
					else:
										lowerCamelCase__       =       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(a,     "decoder_type"):
										lowerCamelCase__       =       "simmim"

					# adapt config
					lowerCamelCase__       =       model_args.image_size if model_args.image_size is not None else config.image_size
					lowerCamelCase__       =       model_args.patch_size if model_args.patch_size is not None else config.patch_size
					lowerCamelCase__       =       (
					    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:
										lowerCamelCase__       =       AutoImageProcessor.from_pretrained(model_args.image_processor_name,     **a)
					elif model_args.model_name_or_path:
										lowerCamelCase__       =       AutoImageProcessor.from_pretrained(model_args.model_name_or_path,     **a)
					else:
										lowerCamelCase__       =       {
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										lowerCamelCase__       =       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										lowerCamelCase__       =       AutoModelForMaskedImageModeling.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")
										lowerCamelCase__       =       AutoModelForMaskedImageModeling.from_config(a)

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

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

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					lowerCamelCase__       =       Compose(
					    [
					        Lambda(lambda a: img.convert("RGB") if img.mode != "RGB" else img),
					        RandomResizedCrop(model_args.image_size,     scale=(0.6_7, 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
					lowerCamelCase__       =       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(a):
										lowerCamelCase__       =       [transforms(a) for image in examples[image_column_name]]
										lowerCamelCase__       =       [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:
															lowerCamelCase__       =       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:
															lowerCamelCase__       =       (
															    ds["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
															)
										# Set the validation transforms
										ds["validation"].set_transform(a)

					# Initialize our trainer
					lowerCamelCase__       =       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:
										lowerCamelCase__       =       None
										if training_args.resume_from_checkpoint is not None:
															lowerCamelCase__       =       training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															lowerCamelCase__       =       last_checkpoint
										lowerCamelCase__       =       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:
										lowerCamelCase__       =       trainer.evaluate()
										trainer.log_metrics("eval",     a)
										trainer.save_metrics("eval",     a)

					# Write model card and (optionally) push to hub
					lowerCamelCase__       =       {
					    "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(**a)
					else:
										trainer.create_model_card(**a)


if __name__ == "__main__":
						main()








import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
										# fmt: on
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file	,  "w"	,  encoding="utf-8"	) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

										lowerCamelCase__       =       {
										    "do_resize": True,
										    "size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.5, 0.5, 0.5],
										    "image_std": [0.5, 0.5, 0.5],
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return BertTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       [np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)]

										lowerCamelCase__       =       [Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	) for x in image_inputs]

										return image_inputs





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										lowerCamelCase__       =       self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"	)
										lowerCamelCase__       =       self.get_image_processor(do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

										for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)

										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

										# test if it raises when no input is passed
										with self.assertRaises(_lowerCAmelCase	):
															processor()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       =       processor.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)

