
import collections
import os
import re
from pathlib import Path


lowerCamelCase__	=							'''src/transformers'''


# Matches is_xxx_available()
lowerCamelCase__	=							re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase__	=							re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase__	=							re.compile(R"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
lowerCamelCase__	=							re.compile(R"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase__	=							re.compile(R"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase__	=							re.compile(R"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase__	=							re.compile(R"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase__	=							re.compile(R"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
lowerCamelCase__	=							re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
lowerCamelCase__	=							re.compile(R"^\s*try:")
# Catches a line with else:
lowerCamelCase__	=							re.compile(R"^\s*else:")


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if _re_test_backend.search(lowerCAmelCase ) is None:
				return None
		_UpperCamelCase						    =	[b[0] for b in _re_backend.findall(lowerCAmelCase )]
		backends.sort()
		return "_and_".join(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		with open(lowerCAmelCase						,  """r"""						,  encoding="""utf-8"""						,  newline="""\n""" ) as f:
				_UpperCamelCase						    =	f.readlines()

		_UpperCamelCase						    =	0
		while line_index < len(lowerCAmelCase ) and not lines[line_index].startswith("""_import_structure = {""" ):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(lowerCAmelCase ):
				return None

		# First grab the objects without a specific backend in _import_structure
		_UpperCamelCase						    =	[]
		while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
				_UpperCamelCase						    =	lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(lowerCAmelCase ):
						_UpperCamelCase						    =	_re_one_line_import_struct.search(lowerCAmelCase ).groups()[0]
						_UpperCamelCase						    =	re.findall(R"""\[([^\]]+)\]"""						,  lowerCAmelCase )
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
						line_index += 1
						continue
				_UpperCamelCase						    =	_re_import_struct_key_value.search(lowerCAmelCase )
				if single_line_import_search is not None:
						_UpperCamelCase						    =	[obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(lowerCAmelCase ) > 0]
						objects.extend(lowerCAmelCase )
				elif line.startswith(""" """ * 8 + """\"""" ):
						objects.append(line[9:-3] )
				line_index += 1

		_UpperCamelCase						    =	{"""none""": objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
				# If the line is an if not is_backend_available, we grab all objects associated.
				_UpperCamelCase						    =	find_backend(lines[line_index] )
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1] ) is None:
						_UpperCamelCase						    =	None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index] ) is None:
								line_index += 1

						line_index += 1

						_UpperCamelCase						    =	[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
								_UpperCamelCase						    =	lines[line_index]
								if _re_import_struct_add_one.search(lowerCAmelCase ) is not None:
										objects.append(_re_import_struct_add_one.search(lowerCAmelCase ).groups()[0] )
								elif _re_import_struct_add_many.search(lowerCAmelCase ) is not None:
										_UpperCamelCase						    =	_re_import_struct_add_many.search(lowerCAmelCase ).groups()[0].split(""", """ )
										_UpperCamelCase						    =	[obj[1:-1] for obj in imports if len(lowerCAmelCase ) > 0]
										objects.extend(lowerCAmelCase )
								elif _re_between_brackets.search(lowerCAmelCase ) is not None:
										_UpperCamelCase						    =	_re_between_brackets.search(lowerCAmelCase ).groups()[0].split(""", """ )
										_UpperCamelCase						    =	[obj[1:-1] for obj in imports if len(lowerCAmelCase ) > 0]
										objects.extend(lowerCAmelCase )
								elif _re_quote_object.search(lowerCAmelCase ) is not None:
										objects.append(_re_quote_object.search(lowerCAmelCase ).groups()[0] )
								elif line.startswith(""" """ * 8 + """\"""" ):
										objects.append(line[9:-3] )
								elif line.startswith(""" """ * 1_2 + """\"""" ):
										objects.append(line[1_3:-3] )
								line_index += 1

						_UpperCamelCase						    =	objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		_UpperCamelCase						    =	[]
		while (
		    line_index < len(lowerCAmelCase )
		    and find_backend(lines[line_index] ) is None
		    and not lines[line_index].startswith("""else""" )
		):
				_UpperCamelCase						    =	lines[line_index]
				_UpperCamelCase						    =	_re_import.search(lowerCAmelCase )
				if single_line_import_search is not None:
						objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
				elif line.startswith(""" """ * 8 ):
						objects.append(line[8:-2] )
				line_index += 1

		_UpperCamelCase						    =	{"""none""": objects}
		# Let's continue with backend-specific objects
		while line_index < len(lowerCAmelCase ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_UpperCamelCase						    =	find_backend(lines[line_index] )
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1] ) is None:
						_UpperCamelCase						    =	None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index] ) is None:
								line_index += 1

						line_index += 1

						_UpperCamelCase						    =	[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
								_UpperCamelCase						    =	lines[line_index]
								_UpperCamelCase						    =	_re_import.search(lowerCAmelCase )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
								elif line.startswith(""" """ * 1_2 ):
										objects.append(line[1_2:-2] )
								line_index += 1

						_UpperCamelCase						    =	objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		def find_duplicates(lowerCAmelCase ):
				return [k for k, v in collections.Counter(lowerCAmelCase ).items() if v > 1]

		if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
				return ["Both sides of the init do not have the same backends!"]

		_UpperCamelCase						    =	[]
		for key in import_dict_objects.keys():
				_UpperCamelCase						    =	find_duplicates(import_dict_objects[key] )
				if duplicate_imports:
						errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
				_UpperCamelCase						    =	find_duplicates(type_hint_objects[key] )
				if duplicate_type_hints:
						errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )

				if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
						_UpperCamelCase						    =	"""base imports""" if key == """none""" else F'{key} backend'
						errors.append(F'Differences for {name}:' )
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(F'  {a} in TYPE_HINT but not in _import_structure.' )
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(F'  {a} in _import_structure but not in TYPE_HINT.' )
		return errors


def 							__A()		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		for root, _, files in os.walk(lowerCAmelCase ):
				if "__init__.py" in files:
						_UpperCamelCase						    =	os.path.join(lowerCAmelCase						,  """__init__.py""" )
						_UpperCamelCase						    =	parse_init(lowerCAmelCase )
						if objects is not None:
								_UpperCamelCase						    =	analyze_results(*lowerCAmelCase )
								if len(lowerCAmelCase ) > 0:
										_UpperCamelCase						    =	F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
										failures.append("""\n""".join(lowerCAmelCase ) )
		if len(lowerCAmelCase ) > 0:
				raise ValueError("""\n\n""".join(lowerCAmelCase ) )


def 							__A()		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		for path, directories, files in os.walk(lowerCAmelCase ):
				for folder in directories:
						# Ignore private modules
						if folder.startswith("""_""" ):
								directories.remove(lowerCAmelCase )
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(lowerCAmelCase ) / folder).glob("""*.py""" ) ) ) == 0:
								continue
						_UpperCamelCase						    =	str((Path(lowerCAmelCase ) / folder).relative_to(lowerCAmelCase ) )
						_UpperCamelCase						    =	short_path.replace(os.path.sep						,  """.""" )
						submodules.append(lowerCAmelCase )
				for fname in files:
						if fname == "__init__.py":
								continue
						_UpperCamelCase						    =	str((Path(lowerCAmelCase ) / fname).relative_to(lowerCAmelCase ) )
						_UpperCamelCase						    =	short_path.replace(""".py"""						,  """""" ).replace(os.path.sep						,  """.""" )
						if len(submodule.split(""".""" ) ) == 1:
								submodules.append(lowerCAmelCase )
		return submodules


lowerCamelCase__	=							[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]


def 							__A()		->				List[str]:

		"""simple docstring"""






		from transformers.utils import direct_transformers_import

		_UpperCamelCase						    =	direct_transformers_import(lowerCAmelCase )

		_UpperCamelCase						    =	set(transformers._import_structure.keys() )
		# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
		# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
		# (potentiall re-) add them.
		with open(os.path.join(lowerCAmelCase						,  """__init__.py""" )						,  """r""" ) as f:
				_UpperCamelCase						    =	f.read()
		import_structure_keys.update(set(re.findall(R"""import_structure\[\"([^\"]*)\"\]"""						,  lowerCAmelCase ) ) )

		_UpperCamelCase						    =	[
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
		]

		if len(lowerCAmelCase ) > 0:
				_UpperCamelCase						    =	"""\n""".join(F'- {module}' for module in module_not_registered )
				raise ValueError(
				    """The following submodules are not properly registed in the main init of Transformers:\n"""
				    F'{list_of_modules}\n'
				    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )


if __name__ == "__main__":
			check_all_inits()
			check_submodules()





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__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	len(list(m.modules()				)				) == 1 or isinstance(a       ,							nn.Convad				) or isinstance(a       ,							nn.BatchNormad				)
								if has_not_submodules:
										self.traced.append(a				)

						def __call__(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook				)				)
								self.module(a				)
								[x.remove() for x in self.handles]
								return self







						@property
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

								if len(a				) != len(a				):
										raise Exception(
										    F'Numbers of operations are different. Source module has {len(a				)} operations while'
										    F' destination module has {len(a				)}.'				)

								for dest_m, src_m in zip(a       ,							a				):
										dest_m.load_state_dict(src_m.state_dict()				)
										if self.verbose == 1:
												print(F'Transfered from={src_m} to={dest_m}'				)




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

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

		_UpperCamelCase						    =	F'resnet{"-".join(name.split("resnet" ) )}'
		print(lowerCAmelCase )

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

				# we can use the convnext one
				_UpperCamelCase						    =	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=lowerCAmelCase						,  )

				print(F'Pushed {checkpoint_name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

		_UpperCamelCase						    =	"""huggingface/label-files"""
		_UpperCamelCase						    =	num_labels
		_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}

		_UpperCamelCase						    =	idalabel
		_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		}

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


if __name__ == "__main__":
		lowerCamelCase__	=							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__	=							parser.parse_args()
		lowerCamelCase__	=							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)



