


"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :    Optional[Any]									=				logging.get_logger()



@dataclass
class 			_UpperCAmelCase :






		'''simple docstring'''

		lowerCamelCase__				=42
		lowerCamelCase__				=field(default_factory=__snake_case				)
		lowerCamelCase__				=field(default_factory=__snake_case				)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						str						=				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_					):





			'''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



@dataclass
class 			_UpperCAmelCase :






		'''simple docstring'''

		lowerCamelCase__				=42
		lowerCamelCase__				=42
		lowerCamelCase__				=1
		lowerCamelCase__				=field(default_factory=__snake_case				)
		lowerCamelCase__				=field(default_factory=__snake_case				)
		lowerCamelCase__				=True



		def __call__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				Tracker(self.dest					)(a_					).parametrized
			__snake_case	:						Optional[int]						=				Tracker(self.src					)(a_					).parametrized

			__snake_case	:						Union[str, Any]						=				list(filter(lambda a_					: type(a_					) not in self.src_skip				,       a_					)					)
			__snake_case	:						Dict						=				list(filter(lambda a_					: type(a_					) not in self.dest_skip				,       a_					)					)

			if len(a_					) != len(a_					) and self.raise_if_mismatch:
				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}"""					)



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_					):





			'''simple docstring'''

			super().__init__()

			__snake_case	:						List[Tuple[str, nn.Module]]						=				[]
			# - get the stem
			feature_blocks.append(('''conv1''', model.stem)					)
			# - get all the feature blocks
			for k, v in model.trunk_output.named_children():
				assert k.startswith('''block'''					), f"""Unexpected layer name {k}"""
				__snake_case	:						Optional[int]						=				len(a_					) + 1
				feature_blocks.append((f"""res{block_index}""", v)					)

			__snake_case	:						List[Any]						=				nn.ModuleDict(a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return get_trunk_forward_outputs(
			    a_				,       out_feat_keys=a_				,       feature_blocks=self._feature_blocks				,       )



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				x.split('''-'''					)
			return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)



		def __getitem__(self				,       a_					):





			'''simple docstring'''

			if x not in self:
				__snake_case	:						Tuple						=				self.convert_name_to_timm(a_					)
				__snake_case	:						str						=				partial(lambda: (timm.create_model(a_				,       pretrained=a_					).eval(), None)					)

			else:
				__snake_case	:						Optional[int]						=				super().__getitem__(a_					)

			return val



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __getitem__(self				,       a_					):





			'''simple docstring'''

			if "seer" in x and "in1k" not in x:
				__snake_case	:						List[Any]						=				RegNetModel
			else:
				__snake_case	:						List[str]						=				RegNetForImageClassification
			return val






def 		lowercase							(     _snake_case						:      Dict     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[Tuple[str, str]]     )     ->Optional[Any]:
	"""simple docstring"""





	for from_key, to_key in keys:
		__snake_case	:						Union[str, Any]						=				from_state_dict[from_key].clone()
		print(f"""Copied key={from_key} to={to_key}"""     )
	return to_state_dict






def 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      Callable[[], nn.Module]     ,   _snake_case						:      Callable[[], nn.Module]     ,   _snake_case						:      RegNetConfig     ,   _snake_case						:      Path     ,   _snake_case						:      bool = True     ,   )     ->Dict:
	"""simple docstring"""





	print(f"""Converting {name}..."""     )
	with torch.no_grad():
		__snake_case	,							__snake_case	:						Optional[int]						=				from_model_func()
		__snake_case	:						Tuple						=				our_model_func(_snake_case     ).eval()
		__snake_case	:						List[Any]						=				ModuleTransfer(src=_snake_case     ,   dest=_snake_case     ,   raise_if_mismatch=_snake_case     )
		__snake_case	:						Union[str, Any]						=				torch.randn((1, 3, 224, 224)     )
		module_transfer(_snake_case     )

	if from_state_dict is not None:
		__snake_case	:						int						=				[]
		# for seer - in1k finetuned we have to manually copy the head
		if "seer" in name and "in1k" in name:
			__snake_case	:						Tuple						=				[('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
		__snake_case	:						Union[str, Any]						=				manually_copy_vissl_head(_snake_case     ,   our_model.state_dict()     ,   _snake_case     )
		our_model.load_state_dict(_snake_case     )

	__snake_case	:						List[Any]						=				our_model(_snake_case     ,   output_hidden_states=_snake_case     )
	__snake_case	:						Union[str, Any]						=				(
	    our_outputs.logits if isinstance(_snake_case     ,   _snake_case     ) else our_outputs.last_hidden_state
	)

	__snake_case	:						Optional[Any]						=				from_model(_snake_case     )
	__snake_case	:						Optional[Any]						=				from_output[-1] if type(_snake_case     ) is list else from_output

	# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
	if "seer" in name and "in1k" in name:
		__snake_case	:						str						=				our_outputs.hidden_states[-1]

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

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

		__snake_case	:						Any						=				224 if '''seer''' not in name else 384
		# we can use the convnext one
		__snake_case	:						Dict						=				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''     ,   size=_snake_case     )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name     ,   commit_message='''Add image processor'''     ,   use_temp_dir=_snake_case     ,   )

		print(f"""Pushed {name}"""     )






def 		lowercase							(     _snake_case						:      Path     ,   _snake_case						:      str = None     ,   _snake_case						:      bool = True     )     ->Any:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				'''imagenet-1k-id2label.json'''
	__snake_case	:						Optional[Any]						=				1_000
	__snake_case	:						int						=				(1, num_labels)

	__snake_case	:						Optional[Any]						=				'''huggingface/label-files'''
	__snake_case	:						Optional[int]						=				num_labels
	__snake_case	:						str						=				json.load(open(cached_download(hf_hub_url(_snake_case     ,   _snake_case     ,   repo_type='''dataset'''     )     )     ,   '''r'''     )     )
	__snake_case	:						str						=				{int(_snake_case     ): v for k, v in idalabel.items()}

	__snake_case	:						Union[str, Any]						=				idalabel
	__snake_case	:						int						=				{v: k for k, v in idalabel.items()}

	__snake_case	:						int						=				partial(_snake_case     ,   num_labels=_snake_case     ,   idalabel=_snake_case     ,   labelaid=_snake_case     )

	__snake_case	:						int						=				{
	    '''regnet-x-002''': ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]     ,   hidden_sizes=[24, 56, 152, 368]     ,   groups_width=8     ,   layer_type='''x'''     ),
	    '''regnet-x-004''': ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 12]     ,   hidden_sizes=[32, 64, 160, 384]     ,   groups_width=16     ,   layer_type='''x'''     ),
	    '''regnet-x-006''': ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]     ,   hidden_sizes=[48, 96, 240, 528]     ,   groups_width=24     ,   layer_type='''x'''     ),
	    '''regnet-x-008''': ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]     ,   hidden_sizes=[64, 128, 288, 672]     ,   groups_width=16     ,   layer_type='''x'''     ),
	    '''regnet-x-016''': ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 2]     ,   hidden_sizes=[72, 168, 408, 912]     ,   groups_width=24     ,   layer_type='''x'''     ),
	    '''regnet-x-032''': ImageNetPreTrainedConfig(
	        depths=[2, 6, 15, 2]     ,   hidden_sizes=[96, 192, 432, 1_008]     ,   groups_width=48     ,   layer_type='''x'''     ),
	    '''regnet-x-040''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2]     ,   hidden_sizes=[80, 240, 560, 1_360]     ,   groups_width=40     ,   layer_type='''x'''     ),
	    '''regnet-x-064''': ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]     ,   hidden_sizes=[168, 392, 784, 1_624]     ,   groups_width=56     ,   layer_type='''x'''     ),
	    '''regnet-x-080''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1]     ,   hidden_sizes=[80, 240, 720, 1_920]     ,   groups_width=120     ,   layer_type='''x'''     ),
	    '''regnet-x-120''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]     ,   hidden_sizes=[224, 448, 896, 2_240]     ,   groups_width=112     ,   layer_type='''x'''     ),
	    '''regnet-x-160''': ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1]     ,   hidden_sizes=[256, 512, 896, 2_048]     ,   groups_width=128     ,   layer_type='''x'''     ),
	    '''regnet-x-320''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1]     ,   hidden_sizes=[336, 672, 1_344, 2_520]     ,   groups_width=168     ,   layer_type='''x'''     ),
	    # y variant
	    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]     ,   hidden_sizes=[24, 56, 152, 368]     ,   groups_width=8     ),
	    '''regnet-y-004''': ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]     ,   hidden_sizes=[48, 104, 208, 440]     ,   groups_width=8     ),
	    '''regnet-y-006''': ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]     ,   hidden_sizes=[48, 112, 256, 608]     ,   groups_width=16     ),
	    '''regnet-y-008''': ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]     ,   hidden_sizes=[64, 128, 320, 768]     ,   groups_width=16     ),
	    '''regnet-y-016''': ImageNetPreTrainedConfig(
	        depths=[2, 6, 17, 2]     ,   hidden_sizes=[48, 120, 336, 888]     ,   groups_width=24     ),
	    '''regnet-y-032''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 13, 1]     ,   hidden_sizes=[72, 216, 576, 1_512]     ,   groups_width=24     ),
	    '''regnet-y-040''': ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2]     ,   hidden_sizes=[128, 192, 512, 1_088]     ,   groups_width=64     ),
	    '''regnet-y-064''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2]     ,   hidden_sizes=[144, 288, 576, 1_296]     ,   groups_width=72     ),
	    '''regnet-y-080''': ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]     ,   hidden_sizes=[168, 448, 896, 2_016]     ,   groups_width=56     ),
	    '''regnet-y-120''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]     ,   hidden_sizes=[224, 448, 896, 2_240]     ,   groups_width=112     ),
	    '''regnet-y-160''': ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1]     ,   hidden_sizes=[224, 448, 1_232, 3_024]     ,   groups_width=112     ),
	    '''regnet-y-320''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]     ,   hidden_sizes=[232, 696, 1_392, 3_712]     ,   groups_width=232     ),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]     ,   hidden_sizes=[232, 696, 1_392, 3_712]     ,   groups_width=232     ),
	    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]     ,   hidden_sizes=[328, 984, 1_968, 4_920]     ,   groups_width=328     ),
	    '''regnet-y-1280-seer''': RegNetConfig(
	        depths=[2, 7, 17, 1]     ,   hidden_sizes=[528, 1_056, 2_904, 7_392]     ,   groups_width=264     ),
	    '''regnet-y-2560-seer''': RegNetConfig(
	        depths=[3, 7, 16, 1]     ,   hidden_sizes=[640, 1_696, 2_544, 5_088]     ,   groups_width=640     ),
	    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]     ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]     ,   groups_width=1_010     ),
	    # finetuned on imagenet
	    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]     ,   hidden_sizes=[232, 696, 1_392, 3_712]     ,   groups_width=232     ),
	    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]     ,   hidden_sizes=[328, 984, 1_968, 4_920]     ,   groups_width=328     ),
	    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]     ,   hidden_sizes=[528, 1_056, 2_904, 7_392]     ,   groups_width=264     ),
	    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1]     ,   hidden_sizes=[640, 1_696, 2_544, 5_088]     ,   groups_width=640     ),
	    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]     ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]     ,   groups_width=1_010     ),
	}

	__snake_case	:						int						=				NameToOurModelFuncMap()
	__snake_case	:						List[str]						=				NameToFromModelFuncMap()
	# add seer weights logic

	def load_using_classy_vision(_snake_case						:      str     ,   _snake_case						:      Callable[[], nn.Module]     ) -> Tuple[nn.Module, Dict]:
		__snake_case	:						Tuple						=				torch.hub.load_state_dict_from_url(_snake_case     ,   model_dir=str(_snake_case     )     ,   map_location='''cpu'''     )
		__snake_case	:						Dict						=				model_func()
		# check if we have a head, if yes add it
		__snake_case	:						Any						=				files['''classy_state_dict''']['''base_model''']['''model''']
		__snake_case	:						Tuple						=				model_state_dict['''trunk''']
		model.load_state_dict(_snake_case     )
		return model.eval(), model_state_dict["heads"]

	# pretrained
	__snake_case	:						List[Any]						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )     ,   )

	__snake_case	:						str						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )     ,   )

	__snake_case	:						int						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''     ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )     ,   )

	__snake_case	:						Optional[Any]						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''     ,   lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27     ,   group_width=1_010     ,   w_a=1_744     ,   w_a=620.83     ,   w_m=2.52     )     )     )     ,   )

	# IN1K finetuned
	__snake_case	:						Union[str, Any]						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )     ,   )

	__snake_case	:						List[str]						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )     ,   )

	__snake_case	:						Optional[int]						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''     ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )     ,   )

	__snake_case	:						Optional[int]						=				partial(
	    _snake_case     ,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''     ,   lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27     ,   group_width=1_010     ,   w_a=1_744     ,   w_a=620.83     ,   w_m=2.52     )     )     )     ,   )

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    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 regnet* architecture,"""
			        """ currently: regnetx-*, regnety-*. 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.""",
			)

			SCREAMING_SNAKE_CASE :    List[str]									=				parser.parse_args()

			SCREAMING_SNAKE_CASE :    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'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser





def __snake_case					(      ):
 lowerCamelCase_      =  ArgumentParser("Accelerate CLI tool"					,				usage="accelerate <command> [<args>]"					,				allow_abbrev=UpperCAmelCase_		)
 lowerCamelCase_      =  parser.add_subparsers(help="accelerate command helpers"		)

 # Register commands
 get_config_parser(subparsers=UpperCAmelCase_		)
 env_command_parser(subparsers=UpperCAmelCase_		)
 launch_command_parser(subparsers=UpperCAmelCase_		)
 tpu_command_parser(subparsers=UpperCAmelCase_		)
 test_command_parser(subparsers=UpperCAmelCase_		)

 # Let's go
 lowerCamelCase_      =  parser.parse_args()

 if not hasattr(UpperCAmelCase_					,				"func"		):
  parser.print_help()
  exit(1		)

 # Run
 args.func(UpperCAmelCase_		)


if __name__ == "__main__":
     main()



