


'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :		Any		     =	logging.get_logger(__name__)

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				int=False				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= []

	# fmt: off
	# stem:
	rename_keys.append(('cls_token', 'vit.embeddings.cls_token')				)
	rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')				)

	rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')				)
	rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')				)

	# backbone
	rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')				)
	rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')				)
	rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')				)

	for stage_idx in range(len(config.backbone_config.depths				)				):
		for layer_idx in range(config.backbone_config.depths[stage_idx]				):
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")				)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")				)

		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")				)
		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")				)
		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")				)

	# transformer encoder
	for i in range(config.num_hidden_layers				):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")				)
		rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")				)
		rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")				)
		rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")				)
		rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")				)
		rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")				)
		rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")				)
		rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")				)
		rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")				)
		rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")				)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		        ('pre_logits.fc.weight', 'pooler.dense.weight'),
		        ('pre_logits.fc.bias', 'pooler.dense.bias'),
		    ]				)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		lowerCAmelCase					:    List[Any]      							= [(pair[0], pair[1][4:]) if pair[1].startswith('vit'				) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]				)
	# fmt: on

	return rename_keys

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				Union[str, Any]=False				)    ->      str:




	"""simple docstring"""





	for i in range(config.num_hidden_layers				):
		if base_model:
			lowerCAmelCase					:    List[str]      							= ''
		else:
			lowerCAmelCase					:    Tuple      							= 'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		lowerCAmelCase					:    List[str]      							= state_dict.pop(F"blocks.{i}.attn.qkv.weight"				)
		lowerCAmelCase					:    Union[str, Any]      							= state_dict.pop(F"blocks.{i}.attn.qkv.bias"				)
		# next, add query, keys and values (in that order) to the state dict
		lowerCAmelCase					:    Tuple      							= in_proj_weight[
		    : config.hidden_size, :
		]
		lowerCAmelCase					:    int      							= in_proj_bias[: config.hidden_size]
		lowerCAmelCase					:    int      							= in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCAmelCase					:    List[str]      							= in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowerCAmelCase					:    List[str]      							= in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCAmelCase					:    Tuple      							= in_proj_bias[-config.hidden_size :]

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				List[str]					,   _A    :				str				)    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= dct.pop(_A				)
	lowerCAmelCase					:    int      							= val

def 			__UpperCamelCase    (    )    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCAmelCase					:    Union[str, Any]      							= Image.open(requests.get(_A					,   stream=_A				).raw				)
	return im

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Optional[int]					,   _A    :				Tuple=False				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= BitConfig(
	    global_padding='same'					,   layer_type='bottleneck'					,   depths=(3, 4, 9)					,   out_features=['stage3']					,   embedding_dynamic_padding=_A					,   )
	lowerCAmelCase					:    str      							= ViTHybridConfig(backbone_config=_A					,   image_size=3_84					,   num_labels=10_00				)
	lowerCAmelCase					:    Any      							= False

	# load original model from timm
	lowerCAmelCase					:    int      							= timm.create_model(_A					,   pretrained=_A				)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	lowerCAmelCase					:    Union[str, Any]      							= timm_model.state_dict()
	if base_model:
		remove_classification_head_(_A				)
	lowerCAmelCase					:    Optional[Any]      							= create_rename_keys(_A					,   _A				)
	for src, dest in rename_keys:
		rename_key(_A					,   _A					,   _A				)
	read_in_q_k_v(_A					,   _A					,   _A				)

	lowerCAmelCase					:    Optional[int]      							= 'huggingface/label-files'
	lowerCAmelCase					:    Tuple      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    List[Any]      							= json.load(open(hf_hub_download(_A					,   _A					,   repo_type='dataset'				)					,   'r'				)				)
	lowerCAmelCase					:    List[str]      							= {int(_A				): v for k, v in idalabel.items()}
	lowerCAmelCase					:    Dict      							= idalabel
	lowerCAmelCase					:    Optional[Any]      							= {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		lowerCAmelCase					:    Dict      							= ViTHybridModel(_A				).eval()
	else:
		lowerCAmelCase					:    str      							= ViTHybridForImageClassification(_A				).eval()
	model.load_state_dict(_A				)

	# create image processor
	lowerCAmelCase					:    List[str]      							= create_transform(**resolve_data_config({}					,   model=_A				)				)
	lowerCAmelCase					:    List[Any]      							= transform.transforms

	lowerCAmelCase					:    Optional[int]      							= {
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	lowerCAmelCase					:    Optional[int]      							= ViTHybridImageProcessor(
	    do_resize=_A					,   size={'shortest_edge': timm_transforms[0].size}					,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,   do_center_crop=_A					,   crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}					,   do_normalize=_A					,   image_mean=timm_transforms[-1].mean.tolist()					,   image_std=timm_transforms[-1].std.tolist()					,   )

	lowerCAmelCase					:    Union[str, Any]      							= prepare_img()
	lowerCAmelCase					:    str      							= transform(_A				).unsqueeze(0				)
	lowerCAmelCase					:    Optional[int]      							= processor(_A					,   return_tensors='pt'				).pixel_values

	# verify pixel values
	assert torch.allclose(_A					,   _A				)

	# verify logits
	with torch.no_grad():
		lowerCAmelCase					:    str      							= model(_A				)
		lowerCAmelCase					:    Union[str, Any]      							= outputs.logits

	print('Predicted class:'					,   logits.argmax(-1				).item()				)
	if base_model:
		lowerCAmelCase					:    List[str]      							= timm_model.forward_features(_A				)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_A					,   outputs.pooler_output					,   atol=1e-3				)
	else:
		lowerCAmelCase					:    Dict      							= timm_model(_A				)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_A					,   outputs.logits					,   atol=1e-3				)
	print('Looks ok!'				)

	if pytorch_dump_folder_path is not None:
		Path(_A				).mkdir(exist_ok=_A				)
		print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"				)
		model.save_pretrained(_A				)
		print(F"Saving processor to {pytorch_dump_folder_path}"				)
		processor.save_pretrained(_A				)

	if push_to_hub:
		print(F"Pushing model and processor to the hub {vit_name}"				)
		model.push_to_hub(F"ybelkada/{vit_name}"				)
		processor.push_to_hub(F"ybelkada/{vit_name}"				)


if __name__ == "__main__":
				_lowerCAmelCase  :		Union[str, Any]		     =	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--vit_name',
				    default='vit_base_r50_s16_384',
				    type=str,
				    help='Name of the hybrid ViT timm model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
				)

				_lowerCAmelCase  :		Any		     =	parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




import argparse
import os
import re


_lowerCAmelCase  :		Dict		     =	'src/diffusers'

# Pattern that looks at the indentation in a line.
_lowerCAmelCase  :		str		     =	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  :		List[Any]		     =	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  :		Optional[Any]		     =	re.compile(r'\[([^\]]+)\]')

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= _re_indent.search(_A				)
	return "" if search is None else search.groups()[0]

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Any=""					,   _A    :				List[str]=None					,   _A    :				Any=None				)    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 0
	lowerCAmelCase					:    Tuple      							= code.split('\n'				)
	if start_prompt is not None:
		while not lines[index].startswith(_A				):
			index += 1
		lowerCAmelCase					:    Optional[int]      							= ['\n'.join(lines[:index]				)]
	else:
		lowerCAmelCase					:    int      							= []

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

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

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

	return blocks

def 			__UpperCamelCase    (    _A    :				Dict				)    ->      List[Any]:




	"""simple docstring"""





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

	return _inner

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				Any=None				)    ->      Optional[Any]:




	"""simple docstring"""





	def noop(_A    :				Any				):
		return x

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

	lowerCAmelCase					:    Tuple      							= ignore_underscore(_A				)
	return sorted(_A					,   key=_A				) + sorted(_A					,   key=_A				) + sorted(_A					,   key=_A				)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      int:




	"""simple docstring"""





	def _replace(_A    :				List[Any]				):
		lowerCAmelCase					:    List[Any]      							= match.groups()[0]
		if "," not in imports:
			return F"[{imports}]"
		lowerCAmelCase					:    Dict      							= [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					:    List[str]      							= keys[:-1]
		return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(_A				)]				) + "]"

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

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Tuple=True				)    ->      Optional[Any]:




	"""simple docstring"""





	with open(_A					,   'r'				) as f:
		lowerCAmelCase					:    Optional[int]      							= f.read()

	if "_import_structure" not in code:
		return

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

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

		# Ignore beginning and last line: they don't contain anything.
		lowerCAmelCase					:    str      							= '\n'.join(block_lines[line_idx:-1]				)
		lowerCAmelCase					:    str      							= get_indent(block_lines[1]				)
		# Slit the internal block into blocks of indent level 1.
		lowerCAmelCase					:    Optional[Any]      							= split_code_in_indented_blocks(_A					,   indent_level=_A				)
		# We have two categories of import key: list or _import_structure[key].append/extend
		lowerCAmelCase					:    Union[str, 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					:    int      							= [(pattern.search(_A				).groups()[0] if pattern.search(_A				) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		lowerCAmelCase					:    Dict      							= [(i, key) for i, key in enumerate(_A				) if key is not None]
		lowerCAmelCase					:    List[Any]      							= [x[0] for x in sorted(_A					,   key=lambda _A				: 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(_A				)				):
			if keys[i] is None:
				reordered_blocks.append(internal_blocks[i]				)
			else:
				lowerCAmelCase					:    str      							= sort_objects_in_import(internal_blocks[sorted_indices[count]]				)
				reordered_blocks.append(_A				)
				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(_A				):
		if check_only:
			return True
		else:
			print(F"Overwriting {file}."				)
			with open(_A					,   'w'				) as f:
				f.write('\n'.join(_A				)				)

def 			__UpperCamelCase    (    _A    :				Tuple=True				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for root, _, files in os.walk(_A				):
		if "__init__.py" in files:
			lowerCAmelCase					:    Any      							= sort_imports(os.path.join(_A					,   '__init__.py'				)					,   check_only=_A				)
			if result:
				lowerCAmelCase					:    Optional[Any]      							= [os.path.join(_A					,   '__init__.py'				)]
	if len(_A				) > 0:
		raise ValueError(F"Would overwrite {len(_A				)} files, run `make style`."				)


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

				sort_imports_in_all_inits(check_only=args.check_only)
