

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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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__       = 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-classification/requirements.txt")

lowerCAmelCase__       = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase__       = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def      _lowerCAmelCase(	__A			):






		with open(__A   ,       "rb"			) as f:
				UpperCAmelCase					      =  Image.open(__A			)
				return im.convert("RGB"			)






@dataclass
class 						__magic_name__    :
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						metadata={
	        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
	    }	,						)
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
	UpperCAmelCase							    =						field(default=_snake_case	,						metadata={"""help""": """A folder containing the training data."""}    )
	UpperCAmelCase							    =						field(default=_snake_case	,						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=_snake_case	,						metadata={
	        """help""": (
	            """For debugging purposes or quicker training, truncate the number of training examples to this """
	            """value if set."""
	        )
	    }	,						)
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						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]    )    ->      List[Any]:
			if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
					raise ValueError(
					    "You must specify either a dataset name from the hub or a train and/or validation directory."    )






@dataclass
class 						__magic_name__    :
	UpperCAmelCase							    =						field(
	    default="""google/vit-base-patch16-224-in21k"""	,						metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	,						)
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_snake_case    )}	,						)
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						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=_snake_case	,						metadata={"""help""": """Name or path of preprocessor config."""}    )
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						metadata={
	        """help""": (
	            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
	            """with private models)."""
	        )
	    }	,						)
	UpperCAmelCase							    =						field(
	    default=_snake_case	,						metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}	,						)

def      _lowerCAmelCase(	__A			):
		UpperCAmelCase					      =  torch.stack([example["pixel_values"] for example in examples]			)
		UpperCAmelCase					      =  torch.tensor([example["labels"] for example in examples]			)
		return {"pixel_values": pixel_values, "labels": labels}
def      _lowerCAmelCase(	):
		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					      =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				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_image_classification"   ,       __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."			)

    # Set seed before initializing model.
		set_seed(training_args.seed			)

		# Initialize our dataset and prepare it for the 'image-classification' task.
		if data_args.dataset_name is not None:
				UpperCAmelCase					      =  load_dataset(
				    data_args.dataset_name   ,       data_args.dataset_config_name   ,       cache_dir=model_args.cache_dir   ,       task="image-classification"   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )
		else:
				UpperCAmelCase					      =  {}
				if data_args.train_dir is not None:
						UpperCAmelCase					      =  os.path.join(data_args.train_dir   ,       "**"			)
				if data_args.validation_dir is not None:
						UpperCAmelCase					      =  os.path.join(data_args.validation_dir   ,       "**"			)
				UpperCAmelCase					      =  load_dataset(
				    "imagefolder"   ,       data_files=__A   ,       cache_dir=model_args.cache_dir   ,       task="image-classification"   ,       )

		# If we don't have a validation split, split off a percentage of train as validation.
		UpperCAmelCase					      =  None if '''validation''' in dataset.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split   ,       __A			) and data_args.train_val_split > 0.0:
				UpperCAmelCase					      =  dataset['''train'''].train_test_split(data_args.train_val_split			)
				UpperCAmelCase					      =  split['''train''']
				UpperCAmelCase					      =  split['''test''']

		# Prepare label mappings.
		# We'll include these in the model's config to get human readable labels in the Inference API.
		UpperCAmelCase					      =  dataset['''train'''].features['''labels'''].names
		UpperCAmelCase					      =  {}, {}
		for i, label in enumerate(__A			):
				UpperCAmelCase					      =  str(__A			)
				UpperCAmelCase					      =  label

		# Load the accuracy metric from the datasets package
		UpperCAmelCase					      =  evaluate.load("accuracy"			)

		# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(__A			):
				return metric.compute(predictions=np.argmax(p.predictions   ,       axis=1			)   ,       references=p.label_ids			)

		UpperCAmelCase					      =  AutoConfig.from_pretrained(
		    model_args.config_name or model_args.model_name_or_path   ,       num_labels=len(__A			)   ,       labelaid=__A   ,       idalabel=__A   ,       finetuning_task="image-classification"   ,       cache_dir=model_args.cache_dir   ,       revision=model_args.model_revision   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )
		UpperCAmelCase					      =  AutoModelForImageClassification.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   ,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes   ,       )
		UpperCAmelCase					      =  AutoImageProcessor.from_pretrained(
		    model_args.image_processor_name or model_args.model_name_or_path   ,       cache_dir=model_args.cache_dir   ,       revision=model_args.model_revision   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )

		# Define torchvision transforms to be applied to each image.
		if "shortest_edge" in image_processor.size:
				UpperCAmelCase					      =  image_processor.size['''shortest_edge''']
		else:
				UpperCAmelCase					      =  (image_processor.size['''height'''], image_processor.size['''width'''])
		UpperCAmelCase					      =  Normalize(mean=image_processor.image_mean   ,       std=image_processor.image_std			)
		UpperCAmelCase					      =  Compose(
		    [
		        RandomResizedCrop(__A			),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        normalize,
		    ]			)
		UpperCAmelCase					      =  Compose(
		    [
		        Resize(__A			),
		        CenterCrop(__A			),
		        ToTensor(),
		        normalize,
		    ]			)

		def train_transforms(__A			):
				UpperCAmelCase					      =  [
				    _train_transforms(pil_img.convert("RGB"			)			) for pil_img in example_batch['''image''']
				]
				return example_batch

		def val_transforms(__A			):
				UpperCAmelCase					      =  [_val_transforms(pil_img.convert("RGB"			)			) for pil_img in example_batch['''image''']]
				return example_batch

		if training_args.do_train:
				if "train" not in dataset:
						raise ValueError("--do_train requires a train dataset"			)
				if data_args.max_train_samples is not None:
						UpperCAmelCase					      =  (
						    dataset['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
						)
				# Set the training transforms
				dataset["train"].set_transform(__A			)

		if training_args.do_eval:
				if "validation" not in dataset:
						raise ValueError("--do_eval requires a validation dataset"			)
				if data_args.max_eval_samples is not None:
						UpperCAmelCase					      =  (
						    dataset['''validation'''].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
						)
				# Set the validation transforms
				dataset["validation"].set_transform(__A			)

		# Initalize our trainer
		UpperCAmelCase					      =  Trainer(
		    model=__A   ,       args=__A   ,       train_dataset=dataset["train"] if training_args.do_train else None   ,       eval_dataset=dataset["validation"] if training_args.do_eval else None   ,       compute_metrics=__A   ,       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''': '''image-classification''',
		    '''dataset''': data_args.dataset_name,
		    '''tags''': ['''image-classification''', '''vision'''],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**__A			)
		else:
				trainer.create_model_card(**__A			)


if __name__ == "__main__":
						main()






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__       = "src/diffusers"

# Matches is_xxx_available()
lowerCAmelCase__       = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCAmelCase__       = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCAmelCase__       = "\n{0} = None\n"

lowerCAmelCase__       = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCAmelCase__       = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  _re_backend.findall(__A			)
  if len(__A			) == 0:
    return None

  return "_and_".join(__A			)
def      _lowerCAmelCase(	):
  with open(os.path.join(__A   ,       "__init__.py"			)   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    UpperCAmelCase					      =  f.readlines()

  # Get to the point we do the actual imports for type checking
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  {}
  # Go through the end of the file
  while line_index < len(__A			):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    UpperCAmelCase					      =  find_backend(lines[line_index]			)
    if backend is not None:
      while not lines[line_index].startswith("else:"			):
        line_index += 1
      line_index += 1
      UpperCAmelCase					      =  []
      # Until we unindent, add backend objects to the list
      while line_index < len(__A			) and len(lines[line_index]			) > 1:
        UpperCAmelCase					      =  lines[line_index]
        UpperCAmelCase					      =  _re_single_line_import.search(__A			)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(", "			)			)
        elif line.startswith(" " * 8			):
          objects.append(line[8:-2]			)
        line_index += 1

      if len(__A			) > 0:
        UpperCAmelCase					      =  objects
    else:
      line_index += 1

  return backend_specific_objects
def      _lowerCAmelCase(	__A   ,       __A			):
  if name.isupper():
    return DUMMY_CONSTANT.format(__A			)
  elif name.islower():
    return DUMMY_FUNCTION.format(__A   ,       __A			)
  else:
    return DUMMY_CLASS.format(__A   ,       __A			)
def      _lowerCAmelCase(	__A=None			):
  if backend_specific_objects is None:
    UpperCAmelCase					      =  read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  UpperCAmelCase					      =  {}

  for backend, objects in backend_specific_objects.items():
    UpperCAmelCase					      =  "[" + ", ".join(F"\"{b}\"" for b in backend.split("_and_"			)			) + "]"
    UpperCAmelCase					      =  "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(__A   ,       __A			) for o in objects]			)
    UpperCAmelCase					      =  dummy_file

  return dummy_files
def      _lowerCAmelCase(	__A=False			):
  UpperCAmelCase					      =  create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  UpperCAmelCase					      =  {"torch": "pt"}

  # Locate actual dummy modules and read their content.
  UpperCAmelCase					      =  os.path.join(__A   ,       "utils"			)
  UpperCAmelCase					      =  {
      backend: os.path.join(__A   ,       F"dummy_{short_names.get(__A   ,       __A			)}_objects.py"			)
      for backend in dummy_files.keys()
  }

  UpperCAmelCase					      =  {}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(__A			):
      with open(__A   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
        UpperCAmelCase					      =  f.read()
    else:
      UpperCAmelCase					      =  ""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F"Updating diffusers.utils.dummy_{short_names.get(__A   ,       __A			)}_objects.py as the main "
            "__init__ has new objects."			)
        with open(dummy_file_paths[backend]   ,       "w"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
          f.write(dummy_files[backend]			)
      else:
        raise ValueError(
            "The main __init__ has objects that are not present in "
            F"diffusers.utils.dummy_{short_names.get(__A   ,       __A			)}_objects.py. Run `make fix-copies` "
            "to fix this."			)


if __name__ == "__main__":
      lowerCAmelCase__       = argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      lowerCAmelCase__       = parser.parse_args()

      check_dummies(args.fix_and_overwrite)



