


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
_snake_case     :		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")

_snake_case     :		Any						=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_snake_case     :		str						=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 				a		:




							"""simple docstring"""


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

							def     __snake_case					(							self       :  Optional[Any]				)	->    List[str]:
														__snake_case		:			str							 =   {}
														if self.train_dir is not None:
																					__snake_case		:			Any							 =   self.train_dir
														if self.validation_dir is not None:
																					__snake_case		:			List[str]							 =   self.validation_dir
														__snake_case		:			str							 =   data_files if data_files else None

@dataclass
class 				a		:




							"""simple docstring"""


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

class 				a		:




							"""simple docstring"""


							def __init__(							self       :  List[str]    ,	lowerCamelCase       :  Any=192    ,	lowerCamelCase       :  Optional[int]=32    ,	lowerCamelCase       :  List[str]=4    ,	lowerCamelCase       :  Union[str, Any]=0.6				)	->    Optional[int]:
														__snake_case		:			Optional[int]							 =   input_size
														__snake_case		:			str							 =   mask_patch_size
														__snake_case		:			int							 =   model_patch_size
														__snake_case		:			Dict							 =   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"				)

														__snake_case		:			Any							 =   self.input_size // self.mask_patch_size
														__snake_case		:			List[Any]							 =   self.mask_patch_size // self.model_patch_size

														__snake_case		:			int							 =   self.rand_size**2
														__snake_case		:			Union[str, Any]							 =   int(np.ceil(self.token_count * self.mask_ratio				)				)






							def __call__(							self       :  Dict				)	->    Optional[Any]:
														__snake_case		:			int							 =   np.random.permutation(self.token_count				)[: self.mask_count]
														__snake_case		:			Optional[Any]							 =   np.zeros(self.token_count    ,	dtype=lowerCamelCase				)
														__snake_case		:			Any							 =   1

														__snake_case		:			Optional[int]							 =   mask.reshape((self.rand_size, self.rand_size)				)
														__snake_case		:			str							 =   mask.repeat(self.scale    ,	axis=0				).repeat(self.scale    ,	axis=1				)

														return torch.tensor(mask.flatten()				)







def 				lowerCAmelCase_     (					__lowerCamelCase	):
							__snake_case		:			Dict							 =   torch.stack([example["pixel_values"] for example in examples]	)
							__snake_case		:			List[str]							 =   torch.stack([example["mask"] for example in examples]	)
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 				lowerCAmelCase_     (					):
							__snake_case		:			Tuple							 =   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.
														__snake_case ,			__snake_case ,			__snake_case		:			Any							 =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
							else:
														__snake_case ,			__snake_case ,			__snake_case		:			Optional[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"							,	UpperCamelCase__							,	UpperCamelCase__	)

							# 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()

							__snake_case		:			List[str]							 =   training_args.get_process_log_level()
							logger.setLevel(UpperCamelCase__	)
							transformers.utils.logging.set_verbosity(UpperCamelCase__	)
							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.
							__snake_case		:			List[str]							 =   None
							if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
														__snake_case		:			Optional[Any]							 =   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.
							__snake_case		:			int							 =   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.
							__snake_case		:			List[Any]							 =   None if "validation" in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split							,	UpperCamelCase__	) and data_args.train_val_split > 0.0:
														__snake_case		:			List[Any]							 =   ds["train"].train_test_split(data_args.train_val_split	)
														__snake_case		:			Optional[Any]							 =   split["train"]
														__snake_case		:			Optional[int]							 =   split["test"]

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							__snake_case		:			Optional[Any]							 =   {
							    "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:
														__snake_case		:			Optional[int]							 =   AutoConfig.from_pretrained(model_args.config_name_or_path							,	**UpperCamelCase__	)
							elif model_args.model_name_or_path:
														__snake_case		:			Optional[Any]							 =   AutoConfig.from_pretrained(model_args.model_name_or_path							,	**UpperCamelCase__	)
							else:
														__snake_case		:			Union[str, 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(UpperCamelCase__							,	"decoder_type"	):
														__snake_case		:			Tuple							 =   "simmim"

							# adapt config
							__snake_case		:			Dict							 =   model_args.image_size if model_args.image_size is not None else config.image_size
							__snake_case		:			Dict							 =   model_args.patch_size if model_args.patch_size is not None else config.patch_size
							__snake_case		:			int							 =   (
							    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:
														__snake_case		:			Any							 =   AutoImageProcessor.from_pretrained(model_args.image_processor_name							,	**UpperCamelCase__	)
							elif model_args.model_name_or_path:
														__snake_case		:			Any							 =   AutoImageProcessor.from_pretrained(model_args.model_name_or_path							,	**UpperCamelCase__	)
							else:
														__snake_case		:			Optional[int]							 =   {
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														__snake_case		:			Union[str, Any]							 =   IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														__snake_case		:			Optional[Any]							 =   AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path							,	from_tf=bool(".ckpt" in model_args.model_name_or_path	)							,	config=UpperCamelCase__							,	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"	)
														__snake_case		:			Union[str, Any]							 =   AutoModelForMaskedImageModeling.from_config(UpperCamelCase__	)

							if training_args.do_train:
														__snake_case		:			Optional[Any]							 =   ds["train"].column_names
							else:
														__snake_case		:			Tuple							 =   ds["validation"].column_names

							if data_args.image_column_name is not None:
														__snake_case		:			str							 =   data_args.image_column_name
							elif "image" in column_names:
														__snake_case		:			Dict							 =   "image"
							elif "img" in column_names:
														__snake_case		:			List[str]							 =   "img"
							else:
														__snake_case		:			Optional[int]							 =   column_names[0]

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							__snake_case		:			List[Any]							 =   Compose(
							    [
							        Lambda(lambda __lowerCamelCase	: 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
							__snake_case		:			int							 =   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(__lowerCamelCase	):
														__snake_case		:			str							 =   [transforms(UpperCamelCase__	) for image in examples[image_column_name]]
														__snake_case		:			str							 =   [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:
																					__snake_case		:			Union[str, Any]							 =   ds["train"].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
														# Set the training transforms
														ds["train"].set_transform(UpperCamelCase__	)

							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:
																					__snake_case		:			int							 =   (
																					    ds["validation"].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
																					)
														# Set the validation transforms
														ds["validation"].set_transform(UpperCamelCase__	)

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

							# Training
							if training_args.do_train:
														__snake_case		:			Dict							 =   None
														if training_args.resume_from_checkpoint is not None:
																					__snake_case		:			List[str]							 =   training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					__snake_case		:			Any							 =   last_checkpoint
														__snake_case		:			Optional[Any]							 =   trainer.train(resume_from_checkpoint=UpperCamelCase__	)
														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:
														__snake_case		:			Tuple							 =   trainer.evaluate()
														trainer.log_metrics("eval"							,	UpperCamelCase__	)
														trainer.save_metrics("eval"							,	UpperCamelCase__	)

							# Write model card and (optionally) push to hub
							__snake_case		:			Optional[Any]							 =   {
							    "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(**UpperCamelCase__	)
							else:
														trainer.create_model_card(**UpperCamelCase__	)


if __name__ == "__main__":
			main()




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self      ,   **snake_case       ):
											kwargs.update(self.special_tokens_map       )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

											snake_case_							=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											snake_case_							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=6       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode_plus(snake_case      ,   max_length=snake_case       )

																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'       )

																									snake_case_							=    None  # Hotfixing padding = None
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )      ,   1       )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )      ,   1       )




