
import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = "cpu"					,  SCREAMING_SNAKE_CASE_ = "openai/clip-vit-large-patch14"):


   '''simple docstring'''




   lowercase__  :   int    		=      device
   lowercase__  :   List[Any]    		=      CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
   lowercase__  :   Tuple    		=      [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
   lowercase__  :   Any    		=      torchvision.transforms.Normalize(self.image_mean					,  self.image_std)
   lowercase__  :   Union[str, Any]    		=      torchvision.transforms.Resize(2_24)
   lowercase__  :   List[Any]    		=      torchvision.transforms.CenterCrop(2_24)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      self.resize(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      self.center_crop(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      self.normalize(SCREAMING_SNAKE_CASE_)
   return images



 def __call__(					self					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   int    		=      self.tokenizer(text=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      self.preprocess_img(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      {key: value.to(self.device) for (key, value) in encoding.items()}
   return encoding



class       _snake_case   (	nn.Module							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=10					,  SCREAMING_SNAKE_CASE_=0.0_1					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_="image"					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=False					,  ):


   '''simple docstring'''




   super().__init__()
   lowercase__  :   Any    		=      None
   lowercase__  :   Dict    		=      device if device else get_device()
   if vqgan:
     lowercase__  :   Tuple    		=      vqgan
   else:
     lowercase__  :   Any    		=      load_vqgan(self.device					,  conf_path=SCREAMING_SNAKE_CASE_					,  ckpt_path=SCREAMING_SNAKE_CASE_)
   self.vqgan.eval()
   if clip:
     lowercase__  :   str    		=      clip
   else:
     lowercase__  :   Dict    		=      CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""")
   self.clip.to(self.device)
   lowercase__  :   Any    		=      ProcessorGradientFlow(device=self.device)

   lowercase__  :   int    		=      iterations
   lowercase__  :   Any    		=      lr
   lowercase__  :   Any    		=      log
   lowercase__  :   List[str]    		=      make_grid
   lowercase__  :   Optional[Any]    		=      return_val
   lowercase__  :   Optional[int]    		=      quantize
   lowercase__  :   Union[str, Any]    		=      self.vqgan.decoder.z_shape


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=5					,  SCREAMING_SNAKE_CASE_=True):


   '''simple docstring'''




   lowercase__  :   int    		=      []
   if output_path is None:
     lowercase__  :   Union[str, Any]    		=      """./animation.gif"""
   if input_path is None:
     lowercase__  :   List[str]    		=      self.save_path
   lowercase__  :   Optional[int]    		=      sorted(glob(input_path + """/*"""))
   if not len(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
         """ function?)""")
   if len(SCREAMING_SNAKE_CASE_) == 1:
     print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""")
   lowercase__  :   Any    		=      total_duration / len(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      [frame_duration] * len(SCREAMING_SNAKE_CASE_)
   if extend_frames:
     lowercase__  :   Any    		=      1.5
     lowercase__  :   Union[str, Any]    		=      3
   for file_name in paths:
     if file_name.endswith(""".png"""):
       images.append(imageio.imread(SCREAMING_SNAKE_CASE_))
   imageio.mimsave(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  duration=SCREAMING_SNAKE_CASE_)
   print(f'gif saved to {output_path}')


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   if not (path or img):
     raise ValueError("""Input either path or tensor""")
   if img is not None:
     raise NotImplementedError
   lowercase__  :   Optional[int]    		=      preprocess(Image.open(SCREAMING_SNAKE_CASE_)					,  target_image_size=2_56).to(self.device)
   lowercase__  :   List[Any]    		=      preprocess_vqgan(SCREAMING_SNAKE_CASE_)
   lowercase__		,    *lowercase__  :   List[Any]    		=      self.vqgan.encode(SCREAMING_SNAKE_CASE_)
   return z


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.latent.detach().requires_grad_()
   lowercase__  :   Optional[Any]    		=      base_latent + transform_vector
   if self.quantize:
     lowercase__		,    *lowercase__  :   Dict    		=      self.vqgan.quantize(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   List[str]    		=      trans_latent
   return self.vqgan.decode(SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      self.clip_preprocessor(text=SCREAMING_SNAKE_CASE_					,  images=SCREAMING_SNAKE_CASE_					,  return_tensors="""pt"""					,  padding=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      self.clip(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      clip_outputs.logits_per_image
   if weights is not None:
     lowercase__  :   Dict    		=      similarity_logits * weights
   return similarity_logits.sum()


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      self._get_clip_similarity(pos_prompts["""prompts"""]					,  SCREAMING_SNAKE_CASE_					,  weights=(1 / pos_prompts["""weights"""]))
   if neg_prompts:
     lowercase__  :   int    		=      self._get_clip_similarity(neg_prompts["""prompts"""]					,  SCREAMING_SNAKE_CASE_					,  weights=neg_prompts["""weights"""])
   else:
     lowercase__  :   Any    		=      torch.tensor([1]					,  device=self.device)
   lowercase__  :   Union[str, Any]    		=      -torch.log(SCREAMING_SNAKE_CASE_) + torch.log(SCREAMING_SNAKE_CASE_)
   return loss


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Any    		=      torch.randn_like(self.latent					,  requires_grad=SCREAMING_SNAKE_CASE_					,  device=self.device)
   lowercase__  :   Optional[int]    		=      torch.optim.Adam([vector]					,  lr=self.lr)

   for i in range(self.iterations):
     optim.zero_grad()
     lowercase__  :   Union[str, Any]    		=      self._add_vector(SCREAMING_SNAKE_CASE_)
     lowercase__  :   Tuple    		=      loop_post_process(SCREAMING_SNAKE_CASE_)
     lowercase__  :   int    		=      self._get_CLIP_loss(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     print("""CLIP loss"""					,  SCREAMING_SNAKE_CASE_)
     if self.log:
       wandb.log({"""CLIP Loss""": clip_loss})
     clip_loss.backward(retain_graph=SCREAMING_SNAKE_CASE_)
     optim.step()
     if self.return_val == "image":
       yield custom_to_pil(transformed_img[0])
     else:
       yield vector


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   wandb.init(reinit=SCREAMING_SNAKE_CASE_					,  project="""face-editor""")
   wandb.config.update({"""Positive Prompts""": positive_prompts})
   wandb.config.update({"""Negative Prompts""": negative_prompts})
   wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations})
   if image_path:
     lowercase__  :   Union[str, Any]    		=      Image.open(SCREAMING_SNAKE_CASE_)
     lowercase__  :   Tuple    		=      image.resize((2_56, 2_56))
     wandb.log("""Original Image"""					,  wandb.Image(SCREAMING_SNAKE_CASE_))


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if not prompts:
     return []
   lowercase__  :   Tuple    		=      []
   lowercase__  :   Union[str, Any]    		=      []
   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
     lowercase__  :   List[str]    		=      [prompt.strip() for prompt in prompts.split("""|""")]
   for prompt in prompts:
     if isinstance(SCREAMING_SNAKE_CASE_					,  (tuple, list)):
       lowercase__  :   Any    		=      prompt[0]
       lowercase__  :   int    		=      float(prompt[1])
     elif ":" in prompt:
       lowercase__		,    lowercase__  :   List[Any]    		=      prompt.split(""":""")
       lowercase__  :   Any    		=      float(SCREAMING_SNAKE_CASE_)
     else:
       lowercase__  :   Any    		=      prompt
       lowercase__  :   Tuple    		=      1.0
     processed_prompts.append(SCREAMING_SNAKE_CASE_)
     weights.append(SCREAMING_SNAKE_CASE_)
   return {
       "prompts": processed_prompts,
       "weights": torch.tensor(SCREAMING_SNAKE_CASE_					,  device=self.device),
   }



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=None					,  ):


   '''simple docstring'''




   if image_path:
     lowercase__  :   Optional[int]    		=      self._get_latent(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   Dict    		=      torch.randn(self.latent_dim					,  device=self.device)
   if self.log:
     self._init_logging(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   assert pos_prompts, "You must provide at least one positive prompt."
   lowercase__  :   Dict    		=      self.process_prompts(SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      self.process_prompts(SCREAMING_SNAKE_CASE_)
   if save_final and save_path is None:
     lowercase__  :   List[str]    		=      os.path.join("""./outputs/"""					,  """_""".join(pos_prompts["""prompts"""]))
   if not os.path.exists(SCREAMING_SNAKE_CASE_):
     os.makedirs(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   Union[str, Any]    		=      save_path + """_""" + get_timestamp()
     os.makedirs(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      save_path

   lowercase__  :   List[str]    		=      self.vqgan.decode(self.latent)[0]
   if show_intermediate:
     print("""Original Image""")
     show_pil(custom_to_pil(SCREAMING_SNAKE_CASE_))

   lowercase__  :   str    		=      loop_post_process(SCREAMING_SNAKE_CASE_)
   for iter, transformed_img in enumerate(self._optimize_CLIP(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)):
     if show_intermediate:
       show_pil(SCREAMING_SNAKE_CASE_)
     if save_intermediate:
       transformed_img.save(os.path.join(self.save_path					,  f'iter_{iter:03d}.png'))
     if self.log:
       wandb.log({"""Image""": wandb.Image(SCREAMING_SNAKE_CASE_)})
   if show_final:
     show_pil(SCREAMING_SNAKE_CASE_)
   if save_final:
     transformed_img.save(os.path.join(self.save_path					,  f'iter_{iter:03d}_final.png'))








import argparse
import os
import re


lowerCamelCase__    :		List[Any]          =   """src/transformers"""

# Pattern that looks at the indentation in a line.
lowerCamelCase__    :		Union[str, Any]          =   re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__    :		Any          =   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__    :		List[Any]          =   re.compile(R"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__    :		str          =   re.compile(R"""\[([^\]]+)\]""")

def 		UpperCamelCase	(      lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   Optional[Any]    		=      _re_indent.search(lowercase_   )
  return "" if search is None else search.groups()[0]

def 		UpperCamelCase	(      lowercase_ , lowercase_="" , lowercase_=None , lowercase_=None   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      0
  lowercase__  :   List[Any]    		=      code.split("""\n"""   )
  if start_prompt is not None:
    while not lines[index].startswith(lowercase_   ):
      index += 1
    lowercase__  :   List[str]    		=      ["""\n""".join(lines[:index]   )]
  else:
    lowercase__  :   Optional[Any]    		=      []

  # We split into blocks until we get to the `end_prompt` (or the end of the block).
  lowercase__  :   List[Any]    		=      [lines[index]]
  index += 1
  while index < len(lowercase_   ) and (end_prompt is None or not lines[index].startswith(lowercase_   )):
    if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
      if len(lowercase_   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + """ """   ):
        current_block.append(lines[index]   )
        blocks.append("""\n""".join(lowercase_   )   )
        if index < len(lowercase_   ) - 1:
          lowercase__  :   str    		=      [lines[index + 1]]
          index += 1
        else:
          lowercase__  :   Union[str, Any]    		=      []
      else:
        blocks.append("""\n""".join(lowercase_   )   )
        lowercase__  :   Tuple    		=      [lines[index]]
    else:
      current_block.append(lines[index]   )
    index += 1

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

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

  return blocks

def 		UpperCamelCase	(      lowercase_   )		->	List[Any]:


  '''simple docstring'''






  def _inner(lowercase_   ):
    return key(lowercase_   ).lower().replace("""_""" , """"""   )

  return _inner

def 		UpperCamelCase	(      lowercase_ , lowercase_=None   )		->	Optional[Any]:


  '''simple docstring'''






  def noop(lowercase_   ):
    return x

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

  lowercase__  :   Tuple    		=      ignore_underscore(lowercase_   )
  return sorted(lowercase_ , key=lowercase_   ) + sorted(lowercase_ , key=lowercase_   ) + sorted(lowercase_ , key=lowercase_   )

def 		UpperCamelCase	(      lowercase_   )		->	Tuple:


  '''simple docstring'''






  def _replace(lowercase_   ):
    lowercase__  :   int    		=      match.groups()[0]
    if "," not in imports:
      return F'[{imports}]'
    lowercase__  :   Union[str, Any]    		=      [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:
      lowercase__  :   int    		=      keys[:-1]
    return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_   )]   ) + "]"

  lowercase__  :   Any    		=      import_statement.split("""\n"""   )
  if len(lowercase_   ) > 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.
    lowercase__  :   Tuple    		=      2 if lines[1].strip() == """[""" else 1
    lowercase__  :   Optional[int]    		=      [(i, _re_strip_line.search(lowercase_   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
    lowercase__  :   Optional[Any]    		=      sort_objects(lowercase_ , key=lambda lowercase_   : x[1]   )
    lowercase__  :   Union[str, Any]    		=      [lines[x[0] + idx] for x in sorted_indices]
    return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
  elif len(lowercase_   ) == 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:
      lowercase__  :   List[str]    		=      _re_bracket_content.sub(_replace , lines[1]   )
    else:
      lowercase__  :   Optional[int]    		=      [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:
        lowercase__  :   Optional[int]    		=      keys[:-1]
      lowercase__  :   int    		=      get_indent(lines[1]   ) + """, """.join([F'"{k}"' for k in sort_objects(lowercase_   )]   )
    return "\n".join(lowercase_   )
  else:
    # Finally we have to deal with imports fitting on one line
    lowercase__  :   Any    		=      _re_bracket_content.sub(_replace , lowercase_   )
    return import_statement

def 		UpperCamelCase	(      lowercase_ , lowercase_=True   )		->	Optional[int]:


  '''simple docstring'''






  with open(lowercase_ , encoding="""utf-8"""   ) as f:
    lowercase__  :   str    		=      f.read()

  if "_import_structure" not in code:
    return

  # Blocks of indent level 0
  lowercase__  :   Any    		=      split_code_in_indented_blocks(
      lowercase_ , 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(lowercase_   ) - 1   ):
    # Check if the block contains some `_import_structure`s thingy to sort.
    lowercase__  :   List[str]    		=      main_blocks[block_idx]
    lowercase__  :   str    		=      block.split("""\n"""   )

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

    # Ignore beginning and last line: they don't contain anything.
    lowercase__  :   List[str]    		=      """\n""".join(block_lines[line_idx:-1]   )
    lowercase__  :   Dict    		=      get_indent(block_lines[1]   )
    # Slit the internal block into blocks of indent level 1.
    lowercase__  :   List[str]    		=      split_code_in_indented_blocks(lowercase_ , indent_level=lowercase_   )
    # We have two categories of import key: list or _import_structure[key].append/extend
    lowercase__  :   Optional[int]    		=      _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.
    lowercase__  :   Optional[Any]    		=      [(pattern.search(lowercase_   ).groups()[0] if pattern.search(lowercase_   ) is not None else None) for b in internal_blocks]
    # We only sort the lines with a key.
    lowercase__  :   List[Any]    		=      [(i, key) for i, key in enumerate(lowercase_   ) if key is not None]
    lowercase__  :   Optional[int]    		=      [x[0] for x in sorted(lowercase_ , key=lambda lowercase_   : x[1]   )]

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

        # And we put our main block back together with its first and last line.
    lowercase__  :   Union[str, Any]    		=      """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]   )

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

def 		UpperCamelCase	(      lowercase_=True   )		->	Optional[int]:


  '''simple docstring'''






  lowercase__  :   int    		=      []
  for root, _, files in os.walk(lowercase_   ):
    if "__init__.py" in files:
      lowercase__  :   Dict    		=      sort_imports(os.path.join(lowercase_ , """__init__.py"""   ) , check_only=lowercase_   )
      if result:
        lowercase__  :   List[str]    		=      [os.path.join(lowercase_ , """__init__.py"""   )]
  if len(lowercase_   ) > 0:
    raise ValueError(F'Would overwrite {len(lowercase_   )} 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__    :		List[Any]          =   parser.parse_args()

     sort_imports_in_all_inits(check_only=args.check_only)






