

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Union[str, Any]       				=  "philschmid/bart-large-cnn-samsum"
	_lowercase  :   Optional[int]       				=  (
	    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
	    "and returns a summary of the text."
	)
	_lowercase  :   List[str]       				=  "summarizer"
	_lowercase  :   int       				=  AutoTokenizer
	_lowercase  :   int       				=  AutoModelForSeqaSeqLM

	_lowercase  :   List[Any]       				=  ["text"]
	_lowercase  :   Optional[Any]       				=  ["text"]







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						return self.pre_processor(__UpperCamelCase  , return_tensors='pt'  , truncation=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						return self.model.generate(**__UpperCamelCase      )[0]







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return self.pre_processor.decode(__UpperCamelCase  , skip_special_tokens=__UpperCamelCase  , clean_up_tokenization_spaces=__UpperCamelCase      )







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__						=							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 				__UpperCamelCase				:
	_lowercase  :   Optional[str]       				=  field(
	    default="cifar10"  ,						metadata={"help": "Name of a dataset from the datasets package"}			)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "The column name of the images in the files."}			)
	_lowercase  :   Optional[str]       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "A folder containing the training data."}			)
	_lowercase  :   Optional[str]       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "A folder containing the validation data."}			)
	_lowercase  :   Optional[float]       				=  field(
	    default=0.15  ,						metadata={"help": "Percent to split off of train for validation."}			)
	_lowercase  :   Optional[int]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }  ,						)
	_lowercase  :   Optional[int]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }  ,						)







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    {}
						if self.train_dir is not None:
											__magic_name__							=    self.train_dir
						if self.validation_dir is not None:
											__magic_name__							=    self.validation_dir
						__magic_name__							=    data_files if data_files else None




@dataclass
class 				__UpperCamelCase				:
	_lowercase  :   str       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
	        )
	    }  ,						)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}			)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						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"
	        )
	    }  ,						)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}			)
	_lowercase  :   str       				=  field(
	    default="main"  ,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}  ,						)
	_lowercase  :   str       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Name or path of preprocessor config."}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }  ,						)
	_lowercase  :   float       				=  field(
	    default=0.75  ,						metadata={"help": "The ratio of the number of masked tokens in the input sequence."}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether or not to train with normalized pixel values as target."}			)




@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   float       				=  field(
	    default=1E-3  ,						metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}			)




def 					_lowercase     (				a_				:					str				)				->	Tuple:




					'''simple docstring'''







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




def 					_lowercase     (				)				->	Dict:




					'''simple docstring'''







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

					__magic_name__							=    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.
					__magic_name__							=    None
					if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
										__magic_name__							=    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.
					__magic_name__							=    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.
					__magic_name__							=    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:
										__magic_name__							=    ds['train'].train_test_split(data_args.train_val_split				)
										__magic_name__							=    split['train']
										__magic_name__							=    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.
					__magic_name__							=    {
					    '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:
										__magic_name__							=    ViTMAEConfig.from_pretrained(model_args.config_name					,**a_				)
					elif model_args.model_name_or_path:
										__magic_name__							=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,**a_				)
					else:
										__magic_name__							=    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:
										__magic_name__							=    ViTImageProcessor.from_pretrained(model_args.image_processor_name					,**a_				)
					elif model_args.model_name_or_path:
										__magic_name__							=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,**a_				)
					else:
										__magic_name__							=    ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										__magic_name__							=    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'				)
										__magic_name__							=    ViTMAEForPreTraining(a_				)

					if training_args.do_train:
										__magic_name__							=    ds['train'].column_names
					else:
										__magic_name__							=    ds['validation'].column_names

					if data_args.image_column_name is not None:
										__magic_name__							=    data_args.image_column_name
					elif "image" in column_names:
										__magic_name__							=    'image'
					elif "img" in column_names:
										__magic_name__							=    'img'
					else:
										__magic_name__							=    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:
										__magic_name__							=    image_processor.size['shortest_edge']
					else:
										__magic_name__							=    (image_processor.size['height'], image_processor.size['width'])
					__magic_name__							=    Compose(
					    [
					        Lambda(lambda a_				: 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(a_				:					Dict				):
										__magic_name__							=    [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:
															__magic_name__							=    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:
															__magic_name__							=    (
															    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
					__magic_name__							=    (
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										__magic_name__							=    training_args.base_learning_rate * total_train_batch_size / 2_5_6

					# Initialize our trainer
					__magic_name__							=    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:
										__magic_name__							=    None
										if training_args.resume_from_checkpoint is not None:
															__magic_name__							=    training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__magic_name__							=    last_checkpoint
										__magic_name__							=    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:
										__magic_name__							=    trainer.evaluate()
										trainer.log_metrics('eval'					,a_				)
										trainer.save_metrics('eval'					,a_				)

					# Write model card and (optionally) push to hub
					__magic_name__							=    {
					    '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 					_lowercase     (				a_				:					str				)				->	Tuple:




					'''simple docstring'''







					main()


if __name__ == "__main__":
						main()




