

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     				=						logging.get_logger()



@dataclass
class 	lowerCamelCase      :







					'''simple docstring'''







					lowerCAmelCase_				:    nn.Module
					lowerCAmelCase_				:    List[nn.Module]									=  field(default_factory=lowercase__  )
					lowerCAmelCase_				:    list									=  field(default_factory=lowercase__  )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				len(list(m.modules()     )     ) == 1 or isinstance(lowerCAmelCase    ,	nn.Convad     ) or isinstance(lowerCAmelCase    ,	nn.BatchNormad     )
							if has_not_submodules:
									self.traced.append(lowerCAmelCase     )



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



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





@dataclass
class 	lowerCamelCase      :







					'''simple docstring'''







					lowerCAmelCase_				:    nn.Module
					lowerCAmelCase_				:    nn.Module
					lowerCAmelCase_				:    int									=  1
					lowerCAmelCase_				:    List									=  field(default_factory=lowercase__  )
					lowerCAmelCase_				:    List									=  field(default_factory=lowercase__  )
					lowerCAmelCase_				:    bool									=  True



					def __call__(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				Tracker(self.dest     )(lowerCAmelCase     ).parametrized
							UpperCAmelCase_						=				Tracker(self.src     )(lowerCAmelCase     ).parametrized

							UpperCAmelCase_						=				list(filter(lambda lowerCAmelCase     : type(lowerCAmelCase     ) not in self.src_skip    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				list(filter(lambda lowerCAmelCase     : type(lowerCAmelCase     ) not in self.dest_skip    ,	lowerCAmelCase     )     )

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

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





class 	lowerCamelCase      (						nn.Module  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase     ):
							super().__init__()

							UpperCAmelCase_						=				[]
							# - 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}'''
									UpperCAmelCase_						=				len(lowerCAmelCase     ) + 1
									feature_blocks.append((f'''res{block_index}''', v)     )

							UpperCAmelCase_						=				nn.ModuleDict(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							return get_trunk_forward_outputs(
							    lowerCAmelCase    ,	out_feat_keys=lowerCAmelCase    ,	feature_blocks=self._feature_blocks    ,	)





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				x.split("-"     )
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]     )



					def __getitem__(			self    ,	lowerCAmelCase     ):
							# default to timm!
							if x not in self:
									UpperCAmelCase_						=				self.convert_name_to_timm(lowerCAmelCase     )
									UpperCAmelCase_						=				partial(lambda: (timm.create_model(lowerCAmelCase    ,	pretrained=lowerCAmelCase     ).eval(), None)     )

							else:
									UpperCAmelCase_						=				super().__getitem__(lowerCAmelCase     )

							return val





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __getitem__(			self    ,	lowerCAmelCase     ):
							if "seer" in x and "in1k" not in x:
									UpperCAmelCase_						=				RegNetModel
							else:
									UpperCAmelCase_						=				RegNetForImageClassification
							return val







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:
		for from_key, to_key in keys:
				UpperCAmelCase_						=				from_state_dict[from_key].clone()
				print(f'''Copied key={from_key} to={to_key}''' )
		return to_state_dict







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = True			,     )		->							Optional[Any]:
		print(f'''Converting {name}...''' )
		with torch.no_grad():
				UpperCAmelCase_					,						UpperCAmelCase_						=				from_model_func()
				UpperCAmelCase_						=				our_model_func(__SCREAMING_SNAKE_CASE ).eval()
				UpperCAmelCase_						=				ModuleTransfer(src=__SCREAMING_SNAKE_CASE			,     dest=__SCREAMING_SNAKE_CASE			,     raise_if_mismatch=__SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				torch.randn((1, 3, 224, 224) )
				module_transfer(__SCREAMING_SNAKE_CASE )

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

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

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

		assert torch.allclose(__SCREAMING_SNAKE_CASE			,     __SCREAMING_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=__SCREAMING_SNAKE_CASE			,     )

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

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = True )		->							int:
		UpperCAmelCase_						=				"imagenet-1k-id2label.json"
		UpperCAmelCase_						=				1000
		UpperCAmelCase_						=				(1, num_labels)

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

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

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

		UpperCAmelCase_						=				{
		    "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, 1_1110, 2_8280]			,     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, 1_1110, 2_8280]			,     groups_width=1010 ),
		}

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

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

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

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

		UpperCAmelCase_						=				partial(
		    __SCREAMING_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() )			,     )

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

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

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

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

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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     				=						parser.parse_args()

			SCREAMING_SNAKE_CASE     				=						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)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

