





import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase		:  List[Any]			       =logging.get_logger(__name__)

set_seed(770)


_lowercase		:  Any			       ={
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


_lowercase		:  int			       ={
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

_lowercase		:  Optional[Any]			       =os.path.dirname(os.path.abspath(__file__))
_lowercase		:  int			       =os.path.join(os.path.expanduser("""~"""), """.cache""")
_lowercase		:  int			       =os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=False						):
		lowerCamelCase_	:     Union[str, Any] 						=      model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase__							,REMOTE_MODEL_PATHS[key]['file_name']						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		os.makedirs(lowerCAmelCase__							,exist_ok=lowerCAmelCase__						)
		hf_hub_download(repo_id=lowerCAmelCase__							,filename=lowerCAmelCase__							,local_dir=lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=False							,lowerCAmelCase__="text"						):
		if model_type == "text":
				lowerCamelCase_	:     Dict 						=      BarkSemanticModel
				lowerCamelCase_	:     int 						=      BarkSemanticConfig
				lowerCamelCase_	:     Optional[int] 						=      BarkSemanticGenerationConfig
		elif model_type == "coarse":
				lowerCamelCase_	:     List[str] 						=      BarkCoarseModel
				lowerCamelCase_	:     Optional[int] 						=      BarkCoarseConfig
				lowerCamelCase_	:     Dict 						=      BarkCoarseGenerationConfig
		elif model_type == "fine":
				lowerCamelCase_	:     Dict 						=      BarkFineModel
				lowerCamelCase_	:     Tuple 						=      BarkFineConfig
				lowerCamelCase_	:     Dict 						=      BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		lowerCamelCase_	:     int 						=      F"{model_type}_small" if use_small else model_type
		lowerCamelCase_	:     List[Any] 						=      REMOTE_MODEL_PATHS[model_key]
		if not os.path.exists(lowerCAmelCase__						):
				logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."						)
				_download(model_info['repo_id']							,model_info['file_name']						)
		lowerCamelCase_	:     List[Any] 						=      torch.load(lowerCAmelCase__							,map_location=lowerCAmelCase__						)
		# this is a hack
		lowerCamelCase_	:     Dict 						=      checkpoint['model_args']
		if "input_vocab_size" not in model_args:
				lowerCamelCase_	:     Optional[int] 						=      model_args['vocab_size']
				lowerCamelCase_	:     Union[str, Any] 						=      model_args['vocab_size']
				del model_args["vocab_size"]

		# convert Bark model arguments to HF Bark model arguments
		lowerCamelCase_	:     List[Any] 						=      model_args.pop('n_head'						)
		lowerCamelCase_	:     str 						=      model_args.pop('n_embd'						)
		lowerCamelCase_	:     Any 						=      model_args.pop('n_layer'						)

		lowerCamelCase_	:     Dict 						=      ConfigClass(**checkpoint['model_args']						)
		lowerCamelCase_	:     str 						=      ModelClass(config=lowerCAmelCase__						)
		lowerCamelCase_	:     Union[str, Any] 						=      GenerationConfigClass()

		lowerCamelCase_	:     Union[str, Any] 						=      model_generation_config
		lowerCamelCase_	:     Tuple 						=      checkpoint['model']
		# fixup checkpoint
		lowerCamelCase_	:     Optional[Any] 						=      '_orig_mod.'
		for k, v in list(state_dict.items()						):
				if k.startswith(lowerCAmelCase__						):
						# replace part of the key with corresponding layer name in HF implementation
						lowerCamelCase_	:     List[Any] 						=      k[len(lowerCAmelCase__						) :]
						for old_layer_name in new_layer_name_dict:
								lowerCamelCase_	:     Tuple 						=      new_k.replace(lowerCAmelCase__							,new_layer_name_dict[old_layer_name]						)

						lowerCamelCase_	:     Dict 						=      state_dict.pop(lowerCAmelCase__						)

		lowerCamelCase_	:     List[str] 						=      set(state_dict.keys()						) - set(model.state_dict().keys()						)
		lowerCamelCase_	:     str 						=      {k for k in extra_keys if not k.endswith('.attn.bias'						)}
		lowerCamelCase_	:     List[Any] 						=      set(model.state_dict().keys()						) - set(state_dict.keys()						)
		lowerCamelCase_	:     Dict 						=      {k for k in missing_keys if not k.endswith('.attn.bias'						)}
		if len(lowerCAmelCase__						) != 0:
				raise ValueError(F"extra keys found: {extra_keys}"						)
		if len(lowerCAmelCase__						) != 0:
				raise ValueError(F"missing keys: {missing_keys}"						)
		model.load_state_dict(lowerCAmelCase__							,strict=lowerCAmelCase__						)
		lowerCamelCase_	:     List[str] 						=      model.num_parameters(exclude_embeddings=lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      checkpoint['best_val_loss'].item()
		logger.info(F"model loaded: {round(n_params/1e6							,1						)}M params, {round(lowerCAmelCase__							,3						)} loss"						)
		model.eval()
		model.to(lowerCAmelCase__						)
		del checkpoint, state_dict

		return model


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=False							,lowerCAmelCase__="text"						):
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		lowerCamelCase_	:     List[Any] 						=      'cpu'  # do conversion on cpu

		lowerCamelCase_	:     List[str] 						=      _get_ckpt_path(lowerCAmelCase__							,use_small=lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      _load_model(lowerCAmelCase__							,lowerCAmelCase__							,model_type=lowerCAmelCase__							,use_small=lowerCAmelCase__						)

		# load bark initial model
		lowerCamelCase_	:     Any 						=      _bark_load_model(lowerCAmelCase__							,'cpu'							,model_type=lowerCAmelCase__							,use_small=lowerCAmelCase__						)

		if model_type == "text":
				lowerCamelCase_	:     List[Any] 						=      bark_model['model']

		if model.num_parameters(exclude_embeddings=lowerCAmelCase__						) != bark_model.get_num_params():
				raise ValueError('initial and new models don\'t have the same number of parameters'						)

		# check if same output as the bark model
		lowerCamelCase_	:     Optional[Any] 						=      5
		lowerCamelCase_	:     int 						=      10

		if model_type in ["text", "coarse"]:
				lowerCamelCase_	:     Tuple 						=      torch.randint(2_56							,(batch_size, sequence_length)							,dtype=torch.int						)
				lowerCamelCase_	:     Optional[int] 						=      bark_model(lowerCAmelCase__						)[0]

				lowerCamelCase_	:     int 						=      model(lowerCAmelCase__						)

				# take last logits
				lowerCamelCase_	:     Optional[Any] 						=      output_new_model_total.logits[:, [-1], :]

		else:
				lowerCamelCase_	:     str 						=      3
				lowerCamelCase_	:     str 						=      8
				lowerCamelCase_	:     Any 						=      torch.randint(2_56							,(batch_size, sequence_length, n_codes_total)							,dtype=torch.int						)

				lowerCamelCase_	:     Dict 						=      model(lowerCAmelCase__							,lowerCAmelCase__						)
				lowerCamelCase_	:     Tuple 						=      bark_model(lowerCAmelCase__							,lowerCAmelCase__						)

				lowerCamelCase_	:     Tuple 						=      output_new_model_total.logits

		# output difference should come from the difference of self-attention implementation design
		if output_new_model.shape != output_old_model.shape:
				raise ValueError('initial and new outputs don\'t have the same shape'						)
		if (output_new_model - output_old_model).abs().max().item() > 1e-3:
				raise ValueError('initial and new outputs are not equal'						)

		Path(lowerCAmelCase__						).mkdir(exist_ok=lowerCAmelCase__						)
		model.save_pretrained(lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,):
		lowerCamelCase_	:     Optional[Any] 						=      os.path.join(lowerCAmelCase__							,lowerCAmelCase__						)

		lowerCamelCase_	:     Tuple 						=      BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase__							,'config.json'						)						)
		lowerCamelCase_	:     str 						=      BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase__							,'config.json'						)						)
		lowerCamelCase_	:     Dict 						=      BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase__							,'config.json'						)						)
		lowerCamelCase_	:     int 						=      EncodecConfig.from_pretrained('facebook/encodec_24khz'						)

		lowerCamelCase_	:     List[str] 						=      BarkSemanticModel.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     List[Any] 						=      BarkCoarseModel.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      BarkFineModel.from_pretrained(lowerCAmelCase__						)
		lowerCamelCase_	:     Tuple 						=      EncodecModel.from_pretrained('facebook/encodec_24khz'						)

		lowerCamelCase_	:     Optional[Any] 						=      BarkConfig.from_sub_model_configs(
		    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)

		lowerCamelCase_	:     Optional[Any] 						=      BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config							,coarseAcoustic.generation_config							,fineAcoustic.generation_config						)

		lowerCamelCase_	:     str 						=      BarkModel(lowerCAmelCase__						)

		lowerCamelCase_	:     Optional[Any] 						=      semantic
		lowerCamelCase_	:     Union[str, Any] 						=      coarseAcoustic
		lowerCamelCase_	:     Dict 						=      fineAcoustic
		lowerCamelCase_	:     Tuple 						=      codec

		lowerCamelCase_	:     Tuple 						=      bark_generation_config

		Path(lowerCAmelCase__						).mkdir(exist_ok=lowerCAmelCase__						)
		bark.save_pretrained(lowerCAmelCase__							,repo_id=lowerCAmelCase__							,push_to_hub=lowerCAmelCase__						)


if __name__ == "__main__":
		_lowercase		:  List[str]			       =argparse.ArgumentParser()
		# Required parameters

		parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
		parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

		_lowercase		:  int			       =parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)







import argparse
import os
import re


_lowercase		:  List[str]			       ="""src/diffusers"""

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      _re_indent.search(lowerCAmelCase__						)
		return "" if search is None else search.groups()[0]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=""							,lowerCAmelCase__=None							,lowerCAmelCase__=None						):
		lowerCamelCase_	:     Optional[Any] 						=      0
		lowerCamelCase_	:     Union[str, Any] 						=      code.split('\n'						)
		if start_prompt is not None:
				while not lines[index].startswith(lowerCAmelCase__						):
						index += 1
				lowerCamelCase_	:     Any 						=      ['\n'.join(lines[:index]						)]
		else:
				lowerCamelCase_	:     str 						=      []

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

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

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

		return blocks


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

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

		return _inner


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=None						):

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

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

		lowerCamelCase_	:     Optional[Any] 						=      ignore_underscore(lowerCAmelCase__						)
		return sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		# This inner function sort imports between [ ].
		def _replace(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      match.groups()[0]
				if "," not in imports:
						return F"[{imports}]"
				lowerCamelCase_	:     Optional[int] 						=      [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:
						lowerCamelCase_	:     str 						=      keys[:-1]
				return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						) + "]"

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=True						):
		with open(lowerCAmelCase__							,'r'						) as f:
				lowerCamelCase_	:     Any 						=      f.read()

		if "_import_structure" not in code:
				return

		# Blocks of indent level 0
		lowerCamelCase_	:     int 						=      split_code_in_indented_blocks(
		    lowerCAmelCase__							,start_prompt='_import_structure = {'							,end_prompt='if TYPE_CHECKING:'						)

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

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

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

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

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

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__=True						):
		lowerCamelCase_	:     Dict 						=      []
		for root, _, files in os.walk(lowerCAmelCase__						):
				if "__init__.py" in files:
						lowerCamelCase_	:     Optional[int] 						=      sort_imports(os.path.join(lowerCAmelCase__							,'__init__.py'						)							,check_only=lowerCAmelCase__						)
						if result:
								lowerCamelCase_	:     Dict 						=      [os.path.join(lowerCAmelCase__							,'__init__.py'						)]
		if len(lowerCAmelCase__						) > 0:
				raise ValueError(F"Would overwrite {len(lowerCAmelCase__						)} files, run `make style`."						)


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

		sort_imports_in_all_inits(check_only=args.check_only)
