




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
_lowerCAmelCase  :      Optional[int]						=      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					:
      UpperCAmelCase_						=						field(
          default="""cifar10"""		,				metadata={"""help""": """Name of a dataset from the datasets package"""}						)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={"""help""": """The column name of the images in the files."""}						)
      UpperCAmelCase_						=						field(default=A__		,				metadata={"""help""": """A folder containing the training data."""}						)
      UpperCAmelCase_						=						field(default=A__		,				metadata={"""help""": """A folder containing the validation data."""}						)
      UpperCAmelCase_						=						field(
          default=0.15		,				metadata={"""help""": """Percent to split off of train for validation."""}						)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of training examples to this """
                  """value if set."""
              )
          }		,				)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                  """value if set."""
              )
          }		,				)
      def 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	int:
       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 	_UpperCamelCase					:
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={
              """help""": (
                  """The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."""
              )
          }		,				)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}						)
      UpperCAmelCase_						=						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"""
              )
          }		,				)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}						)
      UpperCAmelCase_						=						field(
          default="""main"""		,				metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,				)
      UpperCAmelCase_						=						field(default=A__		,				metadata={"""help""": """Name or path of preprocessor config."""}						)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={
              """help""": (
                  """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                  """with private models)."""
              )
          }		,				)
      UpperCAmelCase_						=						field(
          default=0.75		,				metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}						)
      UpperCAmelCase_						=						field(
          default=A__		,				metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}						)



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



def   lowerCAmelCase						(						_lowerCAmelCase							:      int		):




 """simple docstring"""







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



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__							       =  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.
  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_mae"  ,							_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()

 UpperCAmelCase__							       =  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.
 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  ,							_UpperCamelCase		) 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"]

 # Load pretrained model and image processor
 #
 # 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:
  UpperCAmelCase__							       =  ViTMAEConfig.from_pretrained(model_args.config_name  ,							**_UpperCamelCase		)
 elif model_args.model_name_or_path:
  UpperCAmelCase__							       =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,							**_UpperCamelCase		)
 else:
  UpperCAmelCase__							       =  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:
  UpperCAmelCase__							       =  ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,							**_UpperCamelCase		)
 elif model_args.model_name_or_path:
  UpperCAmelCase__							       =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,							**_UpperCamelCase		)
 else:
  UpperCAmelCase__							       =  ViTImageProcessor()

 # create model
 if model_args.model_name_or_path:
  UpperCAmelCase__							       =  ViTMAEForPreTraining.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"		)
  UpperCAmelCase__							       =  ViTMAEForPreTraining(_UpperCamelCase		)

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

 def preprocess_images(_lowerCAmelCase							:      Dict		):
  UpperCAmelCase__							       =  [transforms(_UpperCamelCase		) 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:
   UpperCAmelCase__							       =  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:
   UpperCAmelCase__							       =  (
       ds["validation"].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
   )
  # Set the validation transforms
  ds["validation"].set_transform(_UpperCamelCase		)

 # Compute absolute learning rate
 UpperCAmelCase__							       =  (
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  UpperCAmelCase__							       =  training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 UpperCAmelCase__							       =  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:
  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=_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:
  UpperCAmelCase__							       =  trainer.evaluate()
  trainer.log_metrics("eval"  ,							_UpperCamelCase		)
  trainer.save_metrics("eval"  ,							_UpperCamelCase		)

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      List[str]		):




 """simple docstring"""







 main()


if __name__ == "__main__":
  main()








'''simple docstring'''
# 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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image







class 							A__    (       A__  ):
  A__		 =				(
      'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
      'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
      'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
