





'''simple docstring'''





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
UpperCamelCase__											= 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")

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

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






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






@dataclass
class        __SCREAMING_SNAKE_CASE					:
							snake_case     :      str						     =       field(
							    default=_a   ,		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."""
							        )
							    }   ,		)
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_a       )}   ,		)
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}       )
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={
							        """help""": (
							            """Override some existing default config settings when a model is trained from scratch. Example: """
							            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
							        )
							    }   ,		)
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}   ,		)
							snake_case     :      str						     =       field(
							    default="""main"""   ,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}   ,		)
							snake_case     :      str						     =       field(default=_a   ,		metadata={"""help""": """Name or path of preprocessor config."""}       )
							snake_case     :      bool						     =       field(
							    default=_a   ,		metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    }   ,		)
							snake_case     :      Optional[int]						     =       field(
							    default=_a   ,		metadata={
							        """help""": (
							            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
							        )
							    }   ,		)
							snake_case     :      Optional[int]						     =       field(
							    default=_a   ,		metadata={
							        """help""": (
							            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
							        )
							    }   ,		)
							snake_case     :      Optional[int]						     =       field(
							    default=_a   ,		metadata={"""help""": """Stride to use for the encoder."""}   ,		)






class        __SCREAMING_SNAKE_CASE					:






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

														UpperCamelCase__				     =			self.input_size // self.mask_patch_size
														UpperCamelCase__				     =			self.mask_patch_size // self.model_patch_size

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







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

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

														return torch.tensor(mask.flatten()  )






def     _UpperCamelCase     (a__   :Optional[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			torch.stack([example["""pixel_values"""] for example in examples]       )
							UpperCamelCase__				     =			torch.stack([example["""mask"""] for example in examples]       )
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def     _UpperCamelCase     ():






							"""simple docstring"""





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

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

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

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

							# create model
							if model_args.model_name_or_path:
														UpperCamelCase__				     =			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"""       )
														UpperCamelCase__				     =			AutoModelForMaskedImageModeling.from_config(a__       )

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

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

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							UpperCamelCase__				     =			Compose(
							    [
							        Lambda(lambda a__       : 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
							UpperCamelCase__				     =			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__   :str       ):
														UpperCamelCase__				     =			[transforms(a__       ) for image in examples[image_column_name]]
														UpperCamelCase__				     =			[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:
																					UpperCamelCase__				     =			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:
																					UpperCamelCase__				     =			(
																					    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
							UpperCamelCase__				     =			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:
														UpperCamelCase__				     =			None
														if training_args.resume_from_checkpoint is not None:
																					UpperCamelCase__				     =			training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					UpperCamelCase__				     =			last_checkpoint
														UpperCamelCase__				     =			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:
														UpperCamelCase__				     =			trainer.evaluate()
														trainer.log_metrics("""eval"""   ,			a__       )
														trainer.save_metrics("""eval"""   ,			a__       )

							# Write model card and (optionally) push to hub
							UpperCamelCase__				     =			{
							    """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()











from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403





def     _UpperCamelCase     (a__   :str   ,			a__   :complex   ,			a__   :str = "x"   ,			a__   :float = 10**-10   ,			a__   :int = 1   ,			):






							"""simple docstring"""





							UpperCamelCase__				     =			symbols(a__       )
							UpperCamelCase__				     =			lambdify(a__   ,			a__       )
							UpperCamelCase__				     =			lambdify(a__   ,			diff(a__   ,			a__       )       )

							UpperCamelCase__				     =			starting_point

							while True:
														if diff_function(a__       ) != 0:
																					UpperCamelCase__				     =			prev_guess - multiplicity * func(a__       ) / diff_function(
																					    a__       )
														else:
																					raise ZeroDivisionError("""Could not find root"""       ) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess       ) < precision:
																					return next_guess

														UpperCamelCase__				     =			next_guess


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

					# Find root of polynomial
					# Find fourth Root of 5
					print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}""",
					)

					# Find root of cos(x)
					print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")




