

'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :		Dict		     =	logging.get_logger()



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					nn.Module
	_lowerCamelCase   :					List[nn.Module]			      = field(default_factory=a							)
	_lowerCamelCase   :					list			      = field(default_factory=a							)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= len(list(m.modules()    )    ) == 1 or isinstance(snake_case__ ,					nn.Convad    ) or isinstance(snake_case__ ,					nn.BatchNormad    )
		if has_not_submodules:
			self.traced.append(snake_case__    )



	def __call__(					self ,					snake_case__    ):
		for m in self.module.modules():
			self.handles.append(m.register_forward_hook(self._forward_hook    )    )
		self.module(snake_case__    )
		[x.remove() for x in self.handles]
		return self



	@property
	def 				lowercase     (					self    ):
		# check the len of the state_dict keys to see if we have learnable params
		return list(filter(lambda snake_case__    : len(list(x.state_dict().keys()    )    ) > 0 ,					self.traced    )    )



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					nn.Module
	_lowerCamelCase   :					nn.Module
	_lowerCamelCase   :					int			      = 0
	_lowerCamelCase   :					List			      = field(default_factory=a							)
	_lowerCamelCase   :					List			      = field(default_factory=a							)



	def __call__(					self ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= Tracker(self.dest    )(snake_case__    ).parametrized
		lowerCAmelCase					:    Optional[Any]      							= Tracker(self.src    )(snake_case__    ).parametrized

		lowerCAmelCase					:    Optional[Any]      							= list(filter(lambda snake_case__    : type(snake_case__    ) not in self.src_skip ,					snake_case__    )    )
		lowerCAmelCase					:    List[Any]      							= list(filter(lambda snake_case__    : type(snake_case__    ) not in self.dest_skip ,					snake_case__    )    )

		if len(snake_case__    ) != len(snake_case__    ):
			raise Exception(
			    f"Numbers of operations are different. Source module has {len(snake_case__    )} operations while"
			    f" destination module has {len(snake_case__    )}."    )

		for dest_m, src_m in zip(snake_case__ ,					snake_case__    ):
			dest_m.load_state_dict(src_m.state_dict()    )
			if self.verbose == 1:
				print(f"Transfered from={src_m} to={dest_m}"    )


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




	"""simple docstring"""





	print(F"Converting {name}..."				)
	with torch.no_grad():
		lowerCAmelCase					:    Any      							= timm.create_model(_A					,   pretrained=_A				).eval()
		lowerCAmelCase					:    Optional[int]      							= ResNetForImageClassification(_A				).eval()
		lowerCAmelCase					:    Optional[Any]      							= ModuleTransfer(src=_A					,   dest=_A				)
		lowerCAmelCase					:    List[Any]      							= torch.randn((1, 3, 2_24, 2_24)				)
		module_transfer(_A				)

	assert torch.allclose(from_model(_A				)					,   our_model(_A				).logits				), "The model logits don't match the original one."

	lowerCAmelCase					:    Union[str, Any]      							= F"resnet{'-'.join(name.split('resnet'				)				)}"
	print(_A				)

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name					,   commit_message='Add model'					,   use_temp_dir=_A					,   )

		# we can use the convnext one
		lowerCAmelCase					:    Dict      							= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name					,   commit_message='Add image processor'					,   use_temp_dir=_A					,   )

		print(F"Pushed {checkpoint_name}"				)

def 			__UpperCamelCase    (    _A    :				Path					,   _A    :				str = None					,   _A    :				bool = True				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    Dict      							= 10_00
	lowerCAmelCase					:    List[Any]      							= (1, num_labels)

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

	lowerCAmelCase					:    Tuple      							= idalabel
	lowerCAmelCase					:    Dict      							= {v: k for k, v in idalabel.items()}

	lowerCAmelCase					:    Dict      							= partial(_A					,   num_labels=_A					,   idalabel=_A					,   labelaid=_A				)

	lowerCAmelCase					:    str      							= {
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]					,   hidden_sizes=[64, 1_28, 2_56, 5_12]					,   layer_type='basic'				),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]					,   hidden_sizes=[64, 1_28, 2_56, 5_12]					,   layer_type='basic'				),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	}

	if model_name:
		convert_weight_and_push(_A					,   names_to_config[model_name]					,   _A					,   _A				)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(_A					,   _A					,   _A					,   _A				)
	return config, expected_shape


if __name__ == "__main__":
				_lowerCAmelCase  :		Any		     =	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default=None,
				    type=str,
				    help=(
				        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
				        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=Path,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    default=True,
				    type=bool,
				    required=False,
				    help='If True, push model and image processor to the hub.',
				)

				_lowerCAmelCase  :		List[Any]		     =	parser.parse_args()
				_lowerCAmelCase  :		Path		     =	args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, 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)
