



"""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()
UpperCamelCase     =							logging.get_logger()






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					nn.Module
			A__	:					List[nn.Module]							  =     field(default_factory=_lowerCAmelCase      )
			A__	:					list							  =     field(default_factory=_lowerCAmelCase      )


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


			def __call__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				for m in self.module.modules():
					self.handles.append(m.register_forward_hook(self._forward_hook		)		)
				self.module(SCREAMING_SNAKE_CASE__		)
				[x.remove() for x in self.handles]
				return self







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






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					nn.Module
			A__	:					nn.Module
			A__	:					int							  =     1
			A__	:					List							  =     field(default_factory=_lowerCAmelCase      )
			A__	:					List							  =     field(default_factory=_lowerCAmelCase      )
			A__	:					bool							  =     True


			def __call__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					Tracker(self.dest		)(SCREAMING_SNAKE_CASE__		).parametrized
				A__											=					Tracker(self.src		)(SCREAMING_SNAKE_CASE__		).parametrized

				A__											=					list(filter(lambda SCREAMING_SNAKE_CASE__		: type(SCREAMING_SNAKE_CASE__		) not in self.src_skip						,	SCREAMING_SNAKE_CASE__		)		)
				A__											=					list(filter(lambda SCREAMING_SNAKE_CASE__		: type(SCREAMING_SNAKE_CASE__		) not in self.dest_skip						,	SCREAMING_SNAKE_CASE__		)		)

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

				for dest_m, src_m in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					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						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				super().__init__()

				A__											=					[]
				# - 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}"""
					A__											=					len(SCREAMING_SNAKE_CASE__		) + 1
					feature_blocks.append((f"""res{block_index}""", v)		)

				A__											=					nn.ModuleDict(SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return get_trunk_forward_outputs(
				    SCREAMING_SNAKE_CASE__						,	out_feat_keys=SCREAMING_SNAKE_CASE__						,	feature_blocks=self._feature_blocks						,	)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					x.split("-"		)
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)







			def __getitem__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Callable[[], Tuple[nn.Module, Dict]]:
				# default to timm!
				if x not in self:
					A__											=					self.convert_name_to_timm(SCREAMING_SNAKE_CASE__		)
					A__											=					partial(lambda: (timm.create_model(SCREAMING_SNAKE_CASE__						,	pretrained=SCREAMING_SNAKE_CASE__		).eval(), None)		)

				else:
					A__											=					super().__getitem__(SCREAMING_SNAKE_CASE__		)

				return val






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __getitem__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Callable[[], nn.Module]:
				if "seer" in x and "in1k" not in x:
					A__											=					RegNetModel
				else:
					A__											=					RegNetForImageClassification
				return val



def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       List[Tuple[str, str]]     )	-> int:






	"""simple docstring"""





	for from_key, to_key in keys:
		A__											=					from_state_dict[from_key].clone()
		print(F"""Copied key={from_key} to={to_key}"""     )
	return to_state_dict

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Callable[[], nn.Module],	UpperCAmelCase_ :       Callable[[], nn.Module],	UpperCAmelCase_ :       RegNetConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True,	)	-> List[Any]:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )
	with torch.no_grad():
		A__							,							A__											=					from_model_func()
		A__											=					our_model_func(UpperCAmelCase_     ).eval()
		A__											=					ModuleTransfer(src=UpperCAmelCase_,	dest=UpperCAmelCase_,	raise_if_mismatch=UpperCAmelCase_     )
		A__											=					torch.randn((1, 3, 224, 224)     )
		module_transfer(UpperCAmelCase_     )

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

	A__											=					our_model(UpperCAmelCase_,	output_hidden_states=UpperCAmelCase_     )
	A__											=					(
	    our_outputs.logits if isinstance(UpperCAmelCase_,	UpperCAmelCase_     ) else our_outputs.last_hidden_state
	)

	A__											=					from_model(UpperCAmelCase_     )
	A__											=					from_output[-1] if type(UpperCAmelCase_     ) 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:
		A__											=					our_outputs.hidden_states[-1]

	assert torch.allclose(UpperCAmelCase_,	UpperCAmelCase_     ), "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=UpperCAmelCase_,	)

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

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

def       _lowerCamelCase				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> List[Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000
	A__											=					(1, num_labels)

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

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

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	A__											=					{
	    "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, 1008],	groups_width=48,	layer_type="x"     ),
	    "regnet-x-040": ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2],	hidden_sizes=[80, 240, 560, 1360],	groups_width=40,	layer_type="x"     ),
	    "regnet-x-064": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1],	hidden_sizes=[168, 392, 784, 1624],	groups_width=56,	layer_type="x"     ),
	    "regnet-x-080": ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1],	hidden_sizes=[80, 240, 720, 1920],	groups_width=120,	layer_type="x"     ),
	    "regnet-x-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1],	hidden_sizes=[224, 448, 896, 2240],	groups_width=112,	layer_type="x"     ),
	    "regnet-x-160": ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1],	hidden_sizes=[256, 512, 896, 2048],	groups_width=128,	layer_type="x"     ),
	    "regnet-x-320": ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1],	hidden_sizes=[336, 672, 1344, 2520],	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, 1512],	groups_width=24     ),
	    "regnet-y-040": ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2],	hidden_sizes=[128, 192, 512, 1088],	groups_width=64     ),
	    "regnet-y-064": ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2],	hidden_sizes=[144, 288, 576, 1296],	groups_width=72     ),
	    "regnet-y-080": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1],	hidden_sizes=[168, 448, 896, 2016],	groups_width=56     ),
	    "regnet-y-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1],	hidden_sizes=[224, 448, 896, 2240],	groups_width=112     ),
	    "regnet-y-160": ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1],	hidden_sizes=[224, 448, 1232, 3024],	groups_width=112     ),
	    "regnet-y-320": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1],	hidden_sizes=[232, 696, 1392, 3712],	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, 1392, 3712],	groups_width=232     ),
	    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1],	hidden_sizes=[328, 984, 1968, 4920],	groups_width=328     ),
	    "regnet-y-1280-seer": RegNetConfig(
	        depths=[2, 7, 17, 1],	hidden_sizes=[528, 1056, 2904, 7392],	groups_width=264     ),
	    "regnet-y-2560-seer": RegNetConfig(
	        depths=[3, 7, 16, 1],	hidden_sizes=[640, 1696, 2544, 5088],	groups_width=640     ),
	    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1],	hidden_sizes=[2020, 4040, 11110, 28280],	groups_width=1010     ),
	    # finetuned on imagenet
	    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1],	hidden_sizes=[232, 696, 1392, 3712],	groups_width=232     ),
	    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1],	hidden_sizes=[328, 984, 1968, 4920],	groups_width=328     ),
	    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1],	hidden_sizes=[528, 1056, 2904, 7392],	groups_width=264     ),
	    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1],	hidden_sizes=[640, 1696, 2544, 5088],	groups_width=640     ),
	    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1],	hidden_sizes=[2020, 4040, 11110, 28280],	groups_width=1010     ),
	}

	A__											=					NameToOurModelFuncMap()
	A__											=					NameToFromModelFuncMap()
	# add seer weights logic

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

	# pretrained
	A__											=					partial(
	    UpperCAmelCase_,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch",	lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ),	)

	A__											=					partial(
	    UpperCAmelCase_,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch",	lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ),	)

	A__											=					partial(
	    UpperCAmelCase_,	"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()     ),	)

	A__											=					partial(
	    UpperCAmelCase_,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",	lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27,	group_width=1010,	w_a=1744,	w_a=620.83,	w_m=2.52     )     )     ),	)

	# IN1K finetuned
	A__											=					partial(
	    UpperCAmelCase_,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch",	lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ),	)

	A__											=					partial(
	    UpperCAmelCase_,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch",	lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ),	)

	A__											=					partial(
	    UpperCAmelCase_,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch",	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     ),	)

	A__											=					partial(
	    UpperCAmelCase_,	"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=1010,	w_a=1744,	w_a=620.83,	w_m=2.52     )     )     ),	)

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


if __name__ == "__main__":
			UpperCamelCase     =							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.""",
			)

			UpperCamelCase     =							parser.parse_args()

			UpperCamelCase     =							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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




