



"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowercase : Optional[Any]      						=						{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Union[str, Any]	):
			"""simple docstring"""
			for attribute in key.split('''.'''	):
						lowerCamelCase__		:		Optional[int]			=getattr(__lowerCamelCase							,		__lowerCamelCase	)

			if weight_type is not None:
						lowerCamelCase__		:		Tuple			=getattr(__lowerCamelCase							,		__lowerCamelCase	).shape
			else:
						lowerCamelCase__		:		List[str]			=hf_pointer.shape

			assert hf_shape == value.shape, (
			    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
			    f''' {value.shape} for {full_name}'''
			)

			if weight_type == "weight":
						lowerCamelCase__		:		Union[str, Any]			=value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		str			=value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		Tuple			=value
			elif weight_type == "bias":
						lowerCamelCase__		:		Optional[Any]			=value
			else:
						lowerCamelCase__		:		Tuple			=value

			logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Tuple	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]			=[]
			lowerCamelCase__		:		List[Any]			=fairseq_model.state_dict()

			lowerCamelCase__		:		List[str]			=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__		:		Any			=False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		hf_model.config.feat_extract_norm == '''group'''							,		)
									lowerCamelCase__		:		Optional[int]			=True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase__		:		Any			='''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

												if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
															lowerCamelCase__		:		Dict			=True
															if "*" in mapped_key:
																		lowerCamelCase__		:		List[Any]			=name.split(__lowerCamelCase	)[0].split('''.'''	)[-2]
																		lowerCamelCase__		:		List[Any]			=mapped_key.replace('''*'''							,		__lowerCamelCase	)
															if "weight_g" in name:
																		lowerCamelCase__		:		str			='''weight_g'''
															elif "weight_v" in name:
																		lowerCamelCase__		:		Dict			='''weight_v'''
															elif "weight" in name:
																		lowerCamelCase__		:		Optional[int]			='''weight'''
															elif "bias" in name:
																		lowerCamelCase__		:		str			='''bias'''
															else:
																		lowerCamelCase__		:		int			=None
															set_recursively(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
												continue
						if not is_used:
									unused_weights.append(__lowerCamelCase	)

			logger.warning(f'''Unused weights: {unused_weights}'''	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Union[str, Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=full_name.split('''conv_layers.'''	)[-1]
			lowerCamelCase__		:		int			=name.split('''.'''	)
			lowerCamelCase__		:		Optional[Any]			=int(items[0]	)
			lowerCamelCase__		:		Optional[Any]			=int(items[1]	)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
									)
									lowerCamelCase__		:		Tuple			=value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
									)
									lowerCamelCase__		:		List[Any]			=value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
									    " found."
									)
									lowerCamelCase__		:		str			=value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
									)
									lowerCamelCase__		:		Dict			=value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
			else:
						unused_weights.append(__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Tuple	):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=SEWConfig()
			if is_finetuned:
						lowerCamelCase__		:		Optional[Any]			=model.wav_encoder.wav_model.cfg
			else:
						lowerCamelCase__		:		Optional[int]			=model.cfg

			lowerCamelCase__		:		Optional[Any]			=fs_config.conv_bias
			lowerCamelCase__		:		Tuple			=eval(fs_config.conv_feature_layers	)
			lowerCamelCase__		:		List[Any]			=[x[0] for x in conv_layers]
			lowerCamelCase__		:		List[Any]			=[x[1] for x in conv_layers]
			lowerCamelCase__		:		Union[str, Any]			=[x[2] for x in conv_layers]
			lowerCamelCase__		:		Any			='''gelu'''
			lowerCamelCase__		:		Any			='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			lowerCamelCase__		:		Optional[Any]			=0.0
			lowerCamelCase__		:		Tuple			=fs_config.activation_fn.name
			lowerCamelCase__		:		List[str]			=fs_config.encoder_embed_dim
			lowerCamelCase__		:		List[Any]			=0.02
			lowerCamelCase__		:		str			=fs_config.encoder_ffn_embed_dim
			lowerCamelCase__		:		Optional[Any]			=1e-5
			lowerCamelCase__		:		List[Any]			=fs_config.encoder_layerdrop
			lowerCamelCase__		:		Any			=fs_config.encoder_attention_heads
			lowerCamelCase__		:		str			=fs_config.conv_pos_groups
			lowerCamelCase__		:		Tuple			=fs_config.conv_pos
			lowerCamelCase__		:		Dict			=len(__lowerCamelCase	)
			lowerCamelCase__		:		List[Any]			=fs_config.encoder_layers
			lowerCamelCase__		:		Any			=fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						lowerCamelCase__		:		Any			=model.cfg
						lowerCamelCase__		:		Union[str, Any]			=fs_config.final_dropout
						lowerCamelCase__		:		Any			=fs_config.layerdrop
			lowerCamelCase__		:		str			=fs_config.activation_dropout
			lowerCamelCase__		:		Optional[int]			=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			lowerCamelCase__		:		Dict			=fs_config.attention_dropout
			lowerCamelCase__		:		Tuple			=fs_config.dropout_input
			lowerCamelCase__		:		List[str]			=fs_config.dropout
			lowerCamelCase__		:		Optional[Any]			=fs_config.mask_channel_length
			lowerCamelCase__		:		Union[str, Any]			=fs_config.mask_channel_prob
			lowerCamelCase__		:		Optional[Any]			=fs_config.mask_length
			lowerCamelCase__		:		int			=fs_config.mask_prob

			lowerCamelCase__		:		Any			='''Wav2Vec2FeatureExtractor'''
			lowerCamelCase__		:		Optional[int]			='''Wav2Vec2CTCTokenizer'''

			return config






@torch.no_grad()
def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    Any=None							,		__lowerCamelCase				:    Optional[int]=None							,		__lowerCamelCase				:    Any=True	):
			"""simple docstring"""

			if is_finetuned:
						lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]							,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
			else:
						lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

			if config_path is not None:
						lowerCamelCase__		:		Union[str, Any]			=SEWConfig.from_pretrained(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		int			=convert_config(model[0]							,		__lowerCamelCase	)
			lowerCamelCase__		:		str			=model[0].eval()

			lowerCamelCase__		:		Optional[int]			=True if config.feat_extract_norm == '''layer''' else False
			lowerCamelCase__		:		int			=WavaVecaFeatureExtractor(
			    feature_size=1							,		sampling_rate=16000							,		padding_value=0							,		do_normalize=__lowerCamelCase							,		return_attention_mask=__lowerCamelCase							,		)

			if is_finetuned:
						if dict_path:
									lowerCamelCase__		:		Optional[Any]			=Dictionary.load(__lowerCamelCase	)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase__		:		int			=target_dict.pad_index
									lowerCamelCase__		:		Optional[Any]			=target_dict.bos_index
									lowerCamelCase__		:		Any			=target_dict.pad_index
									lowerCamelCase__		:		Union[str, Any]			=target_dict.bos_index
									lowerCamelCase__		:		Any			=target_dict.eos_index
									lowerCamelCase__		:		Union[str, Any]			=len(target_dict.symbols	)
									lowerCamelCase__		:		Optional[int]			=os.path.join(__lowerCamelCase							,		'''vocab.json'''	)
									if not os.path.isdir(__lowerCamelCase	):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCamelCase	)	)
												return
									os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase	)
									with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''	) as vocab_handle:
												json.dump(target_dict.indices							,		__lowerCamelCase	)
									lowerCamelCase__		:		List[str]			=WavaVecaCTCTokenizer(
									    __lowerCamelCase							,		unk_token=target_dict.unk_word							,		pad_token=target_dict.pad_word							,		bos_token=target_dict.bos_word							,		eos_token=target_dict.eos_word							,		word_delimiter_token='''|'''							,		do_lower_case=__lowerCamelCase							,		)
									lowerCamelCase__		:		Dict			=WavaVecaProcessor(feature_extractor=__lowerCamelCase							,		tokenizer=__lowerCamelCase	)
									processor.save_pretrained(__lowerCamelCase	)

						lowerCamelCase__		:		Dict			=SEWForCTC(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		Optional[int]			=SEWModel(__lowerCamelCase	)
						feature_extractor.save_pretrained(__lowerCamelCase	)

			recursively_load_weights(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			hf_model.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
		_lowercase : List[Any]      						=						argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		parser.add_argument(
		    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
		)
		_lowercase : Union[str, Any]      						=						parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)









"""simple docstring"""






import argparse
import os
import re


_lowercase : str      						=						"src/diffusers"

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






def      snake_case__   (		__lowerCamelCase				:    Optional[int]	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=_re_indent.search(__lowerCamelCase	)
			return "" if search is None else search.groups()[0]






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[Any]=""							,		__lowerCamelCase				:    int=None							,		__lowerCamelCase				:    Optional[int]=None	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]			=0
			lowerCamelCase__		:		Any			=code.split('''\n'''	)
			if start_prompt is not None:
						while not lines[index].startswith(__lowerCamelCase	):
									index += 1
						lowerCamelCase__		:		Dict			=['''\n'''.join(lines[:index]	)]
			else:
						lowerCamelCase__		:		Tuple			=[]

			# We split into blocks until we get to the `end_prompt` (or the end of the block).
			lowerCamelCase__		:		int			=[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__		:		str			=[lines[index + 1]]
															index += 1
												else:
															lowerCamelCase__		:		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      snake_case__   (		__lowerCamelCase				:    Any	):
			"""simple docstring"""

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

			return _inner






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    Any=None	):
			"""simple docstring"""

			# If no key is provided, we use a noop.
			def noop(__lowerCamelCase				:    List[str]	):
						return x

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

			lowerCamelCase__		:		int			=ignore_underscore(__lowerCamelCase	)
			return sorted(__lowerCamelCase							,		key=__lowerCamelCase	) + sorted(__lowerCamelCase							,		key=__lowerCamelCase	) + sorted(__lowerCamelCase							,		key=__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optional[Any]	):
			"""simple docstring"""

			# This inner function sort imports between [ ].
			def _replace(__lowerCamelCase				:    Optional[Any]	):
						lowerCamelCase__		:		Dict			=match.groups()[0]
						if "," not in imports:
									return f'''[{imports}]'''
						lowerCamelCase__		:		List[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:
									lowerCamelCase__		:		Optional[int]			=keys[:-1]
						return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase	)]	) + "]"

			lowerCamelCase__		:		List[Any]			=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__		:		Tuple			=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__		:		List[Any]			=sort_objects(__lowerCamelCase							,		key=lambda __lowerCamelCase	: x[1]	)
						lowerCamelCase__		:		Union[str, 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__		:		List[str]			=_re_bracket_content.sub(_replace							,		lines[1]	)
						else:
									lowerCamelCase__		:		str			=[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__		:		Any			=keys[:-1]
									lowerCamelCase__		:		List[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__		:		Union[str, Any]			=_re_bracket_content.sub(_replace							,		__lowerCamelCase	)
						return import_statement






def      snake_case__   (		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    List[Any]=True	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''r'''	) as f:
						lowerCamelCase__		:		Optional[int]			=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__		:		Optional[Any]			=main_blocks[block_idx]
						lowerCamelCase__		:		List[str]			=block.split('''\n'''	)

						# Get to the start of the imports.
						lowerCamelCase__		:		Any			=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__		:		Tuple			=len(__lowerCamelCase	)
									else:
												line_idx += 1
						if line_idx >= len(__lowerCamelCase	):
									continue

						# Ignore beginning and last line: they don't contain anything.
						lowerCamelCase__		:		Any			='''\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__		:		List[Any]			=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[Any]			=_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__		:		Union[str, Any]			=[(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__		:		Optional[Any]			=[(i, key) for i, key in enumerate(__lowerCamelCase	) if key is not None]
						lowerCamelCase__		:		List[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__		:		Optional[Any]			=0
						lowerCamelCase__		:		Tuple			=[]
						for i in range(len(__lowerCamelCase	)	):
									if keys[i] is None:
												reordered_blocks.append(internal_blocks[i]	)
									else:
												lowerCamelCase__		:		List[Any]			=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__		:		str			='''\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      snake_case__   (		__lowerCamelCase				:    Optional[Any]=True	):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=[]
			for root, _, files in os.walk(__lowerCamelCase	):
						if "__init__.py" in files:
									lowerCamelCase__		:		Tuple			=sort_imports(os.path.join(__lowerCamelCase							,		'''__init__.py'''	)							,		check_only=__lowerCamelCase	)
									if result:
												lowerCamelCase__		:		List[str]			=[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 : Any      						=						argparse.ArgumentParser()
		parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
		_lowercase : List[Any]      						=						parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)




