

"""simple docstring"""




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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A_				:      List[str]           =			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")





@dataclass
class  lowerCamelCase    :
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='cifar10'      ,metadata={'help': 'Name of a dataset from the datasets package'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default=A__      ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default=A__      ,metadata={'help': 'The column name of the images in the files.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(default=A__      ,metadata={'help': 'A folder containing the training data.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(default=A__      ,metadata={'help': 'A folder containing the validation data.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.1_5      ,metadata={'help': 'Percent to split off of train for validation.'}      )
			lowerCamelCase__ :		Optional[int]       		=				field(
			    default=A__      ,metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }      ,)
			lowerCamelCase__ :		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        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   {}
						if self.train_dir is not None:
									SCREAMING_SNAKE_CASE__										=   self.train_dir
						if self.validation_dir is not None:
									SCREAMING_SNAKE_CASE__										=   self.validation_dir
						SCREAMING_SNAKE_CASE__										=   data_files if data_files else None





@dataclass
class  lowerCamelCase    :
			lowerCamelCase__ :		str       		=				field(
			    default=A__      ,metadata={
			        'help': (
			            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
			        )
			    }      ,)
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default=A__      ,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}      )
			lowerCamelCase__ :		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'
			        )
			    }      ,)
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default=A__      ,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}      )
			lowerCamelCase__ :		str       		=				field(
			    default='main'      ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}      ,)
			lowerCamelCase__ :		str       		=				field(default=A__      ,metadata={'help': 'Name or path of preprocessor config.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }      ,)
			lowerCamelCase__ :		float       		=				field(
			    default=0.7_5      ,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}      )





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		float       		=				field(
			    default=1E-3      ,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}      )

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   torch.stack([example["""pixel_values"""] for example in examples]     )
			return {"pixel_values": pixel_values}

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)     )
			if len(sys.argv     ) == 2 and sys.argv[1].endswith(""".json"""     ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("""run_mae"""		,					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()

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

			# Load pretrained model and image processor
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			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,
			}
			if model_args.config_name:
						SCREAMING_SNAKE_CASE__										=   ViTMAEConfig.from_pretrained(model_args.config_name		,					**a__     )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__										=   ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,					**a__     )
			else:
						SCREAMING_SNAKE_CASE__										=   ViTMAEConfig()
						logger.warning("""You are instantiating a new config instance from scratch."""     )
						if model_args.config_overrides is not None:
									logger.info(f"""Overriding config: {model_args.config_overrides}"""     )
									config.update_from_string(model_args.config_overrides     )
									logger.info(f"""New config: {config}"""     )

    # adapt config
			config.update(
			    {
			        """mask_ratio""": model_args.mask_ratio,
			        """norm_pix_loss""": model_args.norm_pix_loss,
			    }     )

			# create image processor
			if model_args.image_processor_name:
						SCREAMING_SNAKE_CASE__										=   ViTImageProcessor.from_pretrained(model_args.image_processor_name		,					**a__     )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__										=   ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,					**a__     )
			else:
						SCREAMING_SNAKE_CASE__										=   ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__										=   ViTMAEForPreTraining.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"""     )
						SCREAMING_SNAKE_CASE__										=   ViTMAEForPreTraining(a__     )

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

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

			# transformations as done in original MAE paper
			# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
			if "shortest_edge" in image_processor.size:
						SCREAMING_SNAKE_CASE__										=   image_processor.size["""shortest_edge"""]
			else:
						SCREAMING_SNAKE_CASE__										=   (image_processor.size["""height"""], image_processor.size["""width"""])
			SCREAMING_SNAKE_CASE__										=   Compose(
			    [
			        Lambda(lambda snake_case__     : img.convert("""RGB"""     ) if img.mode != "RGB" else img     ),
			        RandomResizedCrop(a__		,					scale=(0.2, 1.0)		,					interpolation=InterpolationMode.BICUBIC     ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean		,					std=image_processor.image_std     ),
			    ]     )

			def preprocess_images(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   [transforms(a__     ) for image in examples[image_column_name]]
						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError("""--do_train requires a train dataset"""     )
						if data_args.max_train_samples is not None:
									SCREAMING_SNAKE_CASE__										=   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:
									SCREAMING_SNAKE_CASE__										=   (
									    ds["""validation"""].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
									)
						# Set the validation transforms
						ds["validation"].set_transform(a__     )

			# Compute absolute learning rate
			SCREAMING_SNAKE_CASE__										=   (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						SCREAMING_SNAKE_CASE__										=   training_args.base_learning_rate * total_train_batch_size / 2_56

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

			# Write model card and (optionally) push to hub
			SCREAMING_SNAKE_CASE__										=   {
			    """tasks""": """masked-auto-encoding""",
			    """dataset""": data_args.dataset_name,
			    """tags""": ["""masked-auto-encoding"""],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**a__     )
			else:
						trainer.create_model_card(**a__     )

def 						A							(  snake_case__     ):




			'''simple docstring'''


			main()


if __name__ == "__main__":
						main()






import math

import qiskit

def 			lowerCAmelCase__					(     a__:						int = 1     ,    a__:						int = 1     ,    a__:						int = 1			)    ->						qiskit.result.counts.Counts:


							'''simple docstring'''





							if (
							    isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							):
														raise TypeError('inputs must be integers.'			)

							if (input_a < 0) or (input_a < 0) or (carry_in < 0):
														raise ValueError('inputs must be positive.'			)

							if (
							    (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != carry_in)
							):
														raise ValueError('inputs must be exact integers.'			)

							if (input_a > 2) or (input_a > 2) or (carry_in > 2):
														raise ValueError('inputs must be less or equal to 2.'			)

							# build registers
							_UpperCAmelCase											=     qiskit.QuantumRegister(4     ,    'qr'			)
							_UpperCAmelCase											=     qiskit.ClassicalRegister(2     ,    'cr'			)
							# list the entries
							_UpperCAmelCase											=     [input_a, input_a, carry_in]

							_UpperCAmelCase											=     qiskit.QuantumCircuit(a__     ,    a__			)

							for i in range(0     ,    3			):
														if entry[i] == 2:
																					quantum_circuit.h(a__			)  # for hadamard entries
														elif entry[i] == 1:
																					quantum_circuit.x(a__			)  # for 1 entries
														elif entry[i] == 0:
																					quantum_circuit.i(a__			)  # for 0 entries

    # build the circuit
							quantum_circuit.ccx(0     ,    1     ,    3			)  # ccx = toffoli gate
							quantum_circuit.cx(0     ,    1			)
							quantum_circuit.ccx(1     ,    2     ,    3			)
							quantum_circuit.cx(1     ,    2			)
							quantum_circuit.cx(0     ,    1			)

							quantum_circuit.measure([2, 3]     ,    a__			)  # measure the last two qbits

							_UpperCAmelCase											=     qiskit.Aer.get_backend('aer_simulator'			)
							_UpperCAmelCase											=     qiskit.execute(a__     ,    a__     ,    shots=1_0_0_0			)

							return job.result().get_counts(a__			)


if __name__ == "__main__":
		print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
