




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__			:       Tuple         =							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__			:       Optional[int]         =							list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase__			:       List[Any]         =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			Optional[Any]:
      with open(__UpperCAmelCase      ,      'rb'							) as f:
            SCREAMING_SNAKE_CASE_     						=      Image.open(__UpperCAmelCase							)
            return im.convert('RGB'							)





@dataclass
class 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={
         "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
     }	,       )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "A folder containing the training data."} )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "A folder containing the validation data."} )
 lowercase_       =   field(
     default=0.1_5	,       metadata={"help": "Percent to split off of train for validation."} )
 lowercase_       =   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_       =   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 				lowerCAmelCase_  (							self  :		Optional[int]						):
       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 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   field(
     default="google/vit-base-patch16-224-in21k"	,       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	,       )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_SCREAMING_SNAKE_CASE )}	,       )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
 lowercase_       =   field(
     default="main"	,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}	,       )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Name or path of preprocessor config."} )
 lowercase_       =   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_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}	,       )


def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]							) ->			int:
      SCREAMING_SNAKE_CASE_     						=      torch.stack([example['pixel_values'] for example in examples]							)
      SCREAMING_SNAKE_CASE_     						=      torch.tensor([example['labels'] for example in examples]							)
      return {"pixel_values": pixel_values, "labels": labels}

def  UpperCAmelCase_							(				) ->			Tuple:
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      SCREAMING_SNAKE_CASE_     						=      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.
            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_image_classification'      ,      __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()

      SCREAMING_SNAKE_CASE_     						=      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.
      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.'							)

    # 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:
            SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      {}
            if data_args.train_dir is not None:
                  SCREAMING_SNAKE_CASE_     						=      os.path.join(data_args.train_dir      ,      '**'							)
            if data_args.validation_dir is not None:
                  SCREAMING_SNAKE_CASE_     						=      os.path.join(data_args.validation_dir      ,      '**'							)
            SCREAMING_SNAKE_CASE_     						=      load_dataset(
                'imagefolder'      ,      data_files=__UpperCAmelCase      ,      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.
      SCREAMING_SNAKE_CASE_     						=      None if 'validation' in dataset.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split      ,      __UpperCAmelCase							) and data_args.train_val_split > 0.0:
            SCREAMING_SNAKE_CASE_     						=      dataset['train'].train_test_split(data_args.train_val_split							)
            SCREAMING_SNAKE_CASE_     						=      split['train']
            SCREAMING_SNAKE_CASE_     						=      split['test']

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

      # Load the accuracy metric from the datasets package
      SCREAMING_SNAKE_CASE_     						=      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(__UpperCAmelCase						:     List[Any]							):
            return metric.compute(predictions=np.argmax(p.predictions      ,      axis=1							)      ,      references=p.label_ids							)

      SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path      ,      num_labels=len(__UpperCAmelCase							)      ,      labelaid=__UpperCAmelCase      ,      idalabel=__UpperCAmelCase      ,      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      ,      )
      SCREAMING_SNAKE_CASE_     						=      AutoModelForImageClassification.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      ,      ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      ,      )
      SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      image_processor.size['shortest_edge']
      else:
            SCREAMING_SNAKE_CASE_     						=      (image_processor.size['height'], image_processor.size['width'])
      SCREAMING_SNAKE_CASE_     						=      Normalize(mean=image_processor.image_mean      ,      std=image_processor.image_std							)
      SCREAMING_SNAKE_CASE_     						=      Compose(
          [
              RandomResizedCrop(__UpperCAmelCase							),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]							)
      SCREAMING_SNAKE_CASE_     						=      Compose(
          [
              Resize(__UpperCAmelCase							),
              CenterCrop(__UpperCAmelCase							),
              ToTensor(),
              normalize,
          ]							)

      def train_transforms(__UpperCAmelCase						:     List[str]							):
            SCREAMING_SNAKE_CASE_     						=      [
                _train_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']
            ]
            return example_batch

      def val_transforms(__UpperCAmelCase						:     str							):
            SCREAMING_SNAKE_CASE_     						=      [_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:
                  SCREAMING_SNAKE_CASE_     						=      (
                      dataset['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
                  )
            # Set the training transforms
            dataset["train"].set_transform(__UpperCAmelCase							)

      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:
                  SCREAMING_SNAKE_CASE_     						=      (
                      dataset['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
                  )
            # Set the validation transforms
            dataset["validation"].set_transform(__UpperCAmelCase							)

      # Initalize our trainer
      SCREAMING_SNAKE_CASE_     						=      Trainer(
          model=__UpperCAmelCase      ,      args=__UpperCAmelCase      ,      train_dataset=dataset['train'] if training_args.do_train else None      ,      eval_dataset=dataset['validation'] if training_args.do_eval else None      ,      compute_metrics=__UpperCAmelCase      ,      tokenizer=__UpperCAmelCase      ,      data_collator=__UpperCAmelCase      ,      )

      # 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=__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:
            SCREAMING_SNAKE_CASE_     						=      trainer.evaluate()
            trainer.log_metrics('eval'      ,      __UpperCAmelCase							)
            trainer.save_metrics('eval'      ,      __UpperCAmelCase							)

      # Write model card and (optionally) push to hub
      SCREAMING_SNAKE_CASE_     						=      {
          '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(**__UpperCAmelCase							)
      else:
            trainer.create_model_card(**__UpperCAmelCase							)


if __name__ == "__main__":
   main()





import argparse
import os
import re


lowerCamelCase__			:       str         =							'src/transformers'

# Pattern that looks at the indentation in a line.
lowerCamelCase__			:       Tuple         =							re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__			:       str         =							re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'\[([^\]]+)\]')

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Any:
      SCREAMING_SNAKE_CASE_     						=      _re_indent.search(__UpperCAmelCase							)
      return "" if search is None else search.groups()[0]

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Optional[int]=""      ,      __UpperCAmelCase						:     Optional[int]=None      ,      __UpperCAmelCase						:     Dict=None							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      0
      SCREAMING_SNAKE_CASE_     						=      code.split('\n'							)
      if start_prompt is not None:
            while not lines[index].startswith(__UpperCAmelCase							):
                  index += 1
            SCREAMING_SNAKE_CASE_     						=      ['\n'.join(lines[:index]							)]
      else:
            SCREAMING_SNAKE_CASE_     						=      []

      # We split into blocks until we get to the `end_prompt` (or the end of the block).
      SCREAMING_SNAKE_CASE_     						=      [lines[index]]
      index += 1
      while index < len(__UpperCAmelCase							) and (end_prompt is None or not lines[index].startswith(__UpperCAmelCase							)):
            if len(lines[index]							) > 0 and get_indent(lines[index]							) == indent_level:
                  if len(__UpperCAmelCase							) > 0 and get_indent(current_block[-1]							).startswith(indent_level + ' '							):
                        current_block.append(lines[index]							)
                        blocks.append('\n'.join(__UpperCAmelCase							)							)
                        if index < len(__UpperCAmelCase							) - 1:
                              SCREAMING_SNAKE_CASE_     						=      [lines[index + 1]]
                              index += 1
                        else:
                              SCREAMING_SNAKE_CASE_     						=      []
                  else:
                        blocks.append('\n'.join(__UpperCAmelCase							)							)
                        SCREAMING_SNAKE_CASE_     						=      [lines[index]]
            else:
                  current_block.append(lines[index]							)
            index += 1

      # Adds current block if it's nonempty.
      if len(__UpperCAmelCase							) > 0:
            blocks.append('\n'.join(__UpperCAmelCase							)							)

      # Add final block after end_prompt if provided.
      if end_prompt is not None and index < len(__UpperCAmelCase							):
            blocks.append('\n'.join(lines[index:]							)							)

      return blocks

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict							) ->			Any:

      def _inner(__UpperCAmelCase						:     Tuple							):
            return key(__UpperCAmelCase							).lower().replace('_'      ,      ''							)

      return _inner

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Union[str, Any]=None							) ->			Dict:

      # If no key is provided, we use a noop.
      def noop(__UpperCAmelCase						:     Dict							):
            return x

      if key is None:
            SCREAMING_SNAKE_CASE_     						=      noop
      # Constants are all uppercase, they go first.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if key(__UpperCAmelCase							).isupper()]
      # Classes are not all uppercase but start with a capital, they go second.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if key(__UpperCAmelCase							)[0].isupper() and not key(__UpperCAmelCase							).isupper()]
      # Functions begin with a lowercase, they go last.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if not key(__UpperCAmelCase							)[0].isupper()]

      SCREAMING_SNAKE_CASE_     						=      ignore_underscore(__UpperCAmelCase							)
      return sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							) + sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							) + sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple							) ->			List[Any]:

      # This inner function sort imports between [ ].
      def _replace(__UpperCAmelCase						:     Optional[Any]							):
            SCREAMING_SNAKE_CASE_     						=      match.groups()[0]
            if "," not in imports:
                  return f"[{imports}]"
            SCREAMING_SNAKE_CASE_     						=      [part.strip().replace('"'      ,      ''							) for part in imports.split(','							)]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]							) == 0:
                  SCREAMING_SNAKE_CASE_     						=      keys[:-1]
            return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(__UpperCAmelCase							)]							) + "]"

      SCREAMING_SNAKE_CASE_     						=      import_statement.split('\n'							)
      if len(__UpperCAmelCase							) > 3:
            # Here we have to sort internal imports that are on several lines (one per name):
            # key: [
            #     "object1",
            #     "object2",
            #     ...
            # ]

            # We may have to ignore one or two lines on each side.
            SCREAMING_SNAKE_CASE_     						=      2 if lines[1].strip() == '[' else 1
            SCREAMING_SNAKE_CASE_     						=      [(i, _re_strip_line.search(__UpperCAmelCase							).groups()[0]) for i, line in enumerate(lines[idx:-idx]							)]
            SCREAMING_SNAKE_CASE_     						=      sort_objects(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase							: x[1]							)
            SCREAMING_SNAKE_CASE_     						=      [lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]							)
      elif len(__UpperCAmelCase							) == 3:
            # Here we have to sort internal imports that are on one separate line:
            # key: [
            #     "object1", "object2", ...
            # ]
            if _re_bracket_content.search(lines[1]							) is not None:
                  SCREAMING_SNAKE_CASE_     						=      _re_bracket_content.sub(_replace      ,      lines[1]							)
            else:
                  SCREAMING_SNAKE_CASE_     						=      [part.strip().replace('"'      ,      ''							) for part in lines[1].split(','							)]
                  # We will have a final empty element if the line finished with a comma.
                  if len(keys[-1]							) == 0:
                        SCREAMING_SNAKE_CASE_     						=      keys[:-1]
                  SCREAMING_SNAKE_CASE_     						=      get_indent(lines[1]							) + ', '.join([f"\"{k}\"" for k in sort_objects(__UpperCAmelCase							)]							)
            return "\n".join(__UpperCAmelCase							)
      else:
            # Finally we have to deal with imports fitting on one line
            SCREAMING_SNAKE_CASE_     						=      _re_bracket_content.sub(_replace      ,      __UpperCAmelCase							)
            return import_statement

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Any=True							) ->			Any:
      with open(__UpperCAmelCase      ,      encoding='utf-8'							) as f:
            SCREAMING_SNAKE_CASE_     						=      f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      SCREAMING_SNAKE_CASE_     						=      split_code_in_indented_blocks(
          __UpperCAmelCase      ,      start_prompt='_import_structure = {'      ,      end_prompt='if TYPE_CHECKING:'							)

      # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
      for block_idx in range(1      ,      len(__UpperCAmelCase							) - 1							):
            # Check if the block contains some `_import_structure`s thingy to sort.
            SCREAMING_SNAKE_CASE_     						=      main_blocks[block_idx]
            SCREAMING_SNAKE_CASE_     						=      block.split('\n'							)

            # Get to the start of the imports.
            SCREAMING_SNAKE_CASE_     						=      0
            while line_idx < len(__UpperCAmelCase							) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        SCREAMING_SNAKE_CASE_     						=      len(__UpperCAmelCase							)
                  else:
                        line_idx += 1
            if line_idx >= len(__UpperCAmelCase							):
                  continue

            # Ignore beginning and last line: they don't contain anything.
            SCREAMING_SNAKE_CASE_     						=      '\n'.join(block_lines[line_idx:-1]							)
            SCREAMING_SNAKE_CASE_     						=      get_indent(block_lines[1]							)
            # Slit the internal block into blocks of indent level 1.
            SCREAMING_SNAKE_CASE_     						=      split_code_in_indented_blocks(__UpperCAmelCase      ,      indent_level=__UpperCAmelCase							)
            # We have two categories of import key: list or _import_structure[key].append/extend
            SCREAMING_SNAKE_CASE_     						=      _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
            # Grab the keys, but there is a trap: some lines are empty or just comments.
            SCREAMING_SNAKE_CASE_     						=      [(pattern.search(__UpperCAmelCase							).groups()[0] if pattern.search(__UpperCAmelCase							) is not None else None) for b in internal_blocks]
            # We only sort the lines with a key.
            SCREAMING_SNAKE_CASE_     						=      [(i, key) for i, key in enumerate(__UpperCAmelCase							) if key is not None]
            SCREAMING_SNAKE_CASE_     						=      [x[0] for x in sorted(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase							: x[1]							)]

            # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
            SCREAMING_SNAKE_CASE_     						=      0
            SCREAMING_SNAKE_CASE_     						=      []
            for i in range(len(__UpperCAmelCase							)							):
                  if keys[i] is None:
                        reorderded_blocks.append(internal_blocks[i]							)
                  else:
                        SCREAMING_SNAKE_CASE_     						=      sort_objects_in_import(internal_blocks[sorted_indices[count]]							)
                        reorderded_blocks.append(__UpperCAmelCase							)
                        count += 1

        # And we put our main block back together with its first and last line.
            SCREAMING_SNAKE_CASE_     						=      '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]							)

      if code != "\n".join(__UpperCAmelCase							):
            if check_only:
                  return True
            else:
                  print(f"Overwriting {file}."							)
                  with open(__UpperCAmelCase      ,      'w'      ,      encoding='utf-8'							) as f:
                        f.write('\n'.join(__UpperCAmelCase							)							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]=True							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      []
      for root, _, files in os.walk(__UpperCAmelCase							):
            if "__init__.py" in files:
                  SCREAMING_SNAKE_CASE_     						=      sort_imports(os.path.join(__UpperCAmelCase      ,      '__init__.py'							)      ,      check_only=__UpperCAmelCase							)
                  if result:
                        SCREAMING_SNAKE_CASE_     						=      [os.path.join(__UpperCAmelCase      ,      '__init__.py'							)]
      if len(__UpperCAmelCase							) > 0:
            raise ValueError(f"Would overwrite {len(__UpperCAmelCase							)} files, run `make style`."							)


if __name__ == "__main__":
   lowerCamelCase__			:       int         =							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   lowerCamelCase__			:       Optional[Any]         =							parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)