
"""simple docstring"""

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
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.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__A							=     logging.get_logger()
@dataclass
class      lowerCamelCase__      :
			a__	: nn.Module
			a__	: List[nn.Module]        =       field(default_factory=lowerCamelCase_     )
			a__	: list        =       field(default_factory=lowerCamelCase_     )
			a__	: Dict[str, nn.Module]        =       field(default_factory=lowerCamelCase_     )


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			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							)
							snake_case						:  List[str]        =			m


			def __call__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					for name, m in self.module.named_modules():
							self.handles.append(m.register_forward_hook(partial(self._forward_hook			,	name=SCREAMING_SNAKE_CASE							)							)							)
					self.module(SCREAMING_SNAKE_CASE							)
					[x.remove() for x in self.handles]
					return self



			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}







class      lowerCamelCase__      (					nn.Module     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE							):



					"""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						:  Tuple        =			len(SCREAMING_SNAKE_CASE							) + 1
							feature_blocks.append((F'''res{block_index}''', v)							)

					snake_case						:  int        =			nn.ModuleDict(SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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







class      lowerCamelCase__      (					lowerCamelCase_     ):


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def 					UpperCamelCase__       (						lowercase__					:       str				):
		snake_case						:  Union[str, Any]        =			RegNetConfig(depths=[2, 7, 17, 1]    ,       hidden_sizes=[8, 8, 8, 8]    ,       groups_width=8				)
		if "in1k" in model_name:
				snake_case						:  str        =			RegNetForImageClassification(lowercase__				)
		else:
				snake_case						:  Optional[Any]        =			RegNetModel(lowercase__				)
		# create from model (with small weights)
		snake_case						:  str        =			FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27    ,       group_width=1010    ,       w_a=1744    ,       w_a=620.83    ,       w_m=2.52				)				)				)

		with torch.no_grad():
				snake_case						:  int        =			from_model.eval()
				snake_case						:  Optional[Any]        =			our_model.eval()

				snake_case						:  List[Any]        =			torch.randn((1, 3, 32, 32)				)
				# trace both
				snake_case						:  Tuple        =			Tracker(lowercase__				)
				snake_case						:  List[str]        =			dest_tracker(lowercase__				).parametrized

				pprint(dest_tracker.nameamodule				)
				snake_case						:  Optional[int]        =			Tracker(lowercase__				)
				snake_case						:  Optional[Any]        =			src_tracker(lowercase__				).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(lowercase__					:       Any				):
				snake_case						:  Union[str, Any]        =			OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								snake_case						:  List[str]        =			param
				return params_dict

		snake_case						:  Optional[Any]        =			{}

		snake_case						:  str        =			to_params_dict(lowercase__				)
		snake_case						:  List[Any]        =			to_params_dict(lowercase__				)

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()    ,       dst_state_dict.items()				):
				snake_case						:  Dict        =			dest_key
				logger.info(F'''{src_key} -> {dest_key}'''				)
		# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
		if "in1k" in model_name:
				snake_case						:  Optional[Any]        =			"classifier.1.weight"
				snake_case						:  int        =			"classifier.1.bias"

		return from_to_ours_keys



def 					UpperCamelCase__       (						lowercase__					:       Path    ,       lowercase__					:       str = None    ,       lowercase__					:       bool = True				):
		snake_case						:  Optional[Any]        =			"imagenet-1k-id2label.json"
		snake_case						:  List[str]        =			1000

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

		snake_case						:  int        =			idalabel
		snake_case						:  Tuple        =			{v: k for k, v in idalabel.items()}

		snake_case						:  Tuple        =			partial(lowercase__    ,       num_labels=lowercase__    ,       idalabel=lowercase__    ,       labelaid=lowercase__				)

		snake_case						:  Any        =			{
		    "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-10b-seer-in1k": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]    ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]    ,       groups_width=1010				),
		}

		# add seer weights logic
		def load_using_classy_vision(lowercase__					:       str				) -> Tuple[Dict, Dict]:
				snake_case						:  List[Any]        =			torch.hub.load_state_dict_from_url(lowercase__    ,       model_dir=str(lowercase__				)    ,       map_location="cpu"				)
				# check if we have a head, if yes add it
				snake_case						:  Optional[Any]        =			files["classy_state_dict"]["base_model"]["model"]
				return model_state_dict["trunk"], model_state_dict["heads"]

		snake_case						:  Optional[Any]        =			{
		    "regnet-y-10b-seer": partial(
		        lowercase__    ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"    ,       ),
		    "regnet-y-10b-seer-in1k": partial(
		        lowercase__    ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"    ,       ),
		}

		snake_case						:  List[str]        =			get_from_to_our_keys(lowercase__				)

		if not (save_directory / F'''{model_name}.pth''').exists():
				logger.info("Loading original state_dict."				)
				snake_case						:  Union[str, Any]        =			names_to_from_model[model_name]()
				snake_case						:  Optional[int]        =			from_state_dict_trunk
				if "in1k" in model_name:
						# add the head
						snake_case						:  List[Any]        =			{**from_state_dict_trunk, **from_state_dict_head}
				logger.info("Done!"				)

				snake_case						:  Any        =			{}

				snake_case						:  Dict        =			list(from_state_dict.keys()				)
				snake_case						:  Optional[int]        =			R"\.block.-part."
				# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
				for key in from_state_dict.keys():
						# remove the weird "block[0,1]-part" from the key
						snake_case						:  Optional[Any]        =			re.sub(lowercase__    ,       ""    ,       lowercase__				)
						# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
						snake_case						:  int        =			from_to_ours_keys[src_key]
						# store the parameter with our key
						snake_case						:  Any        =			from_state_dict[key]
						not_used_keys.remove(lowercase__				)
				# check that all keys have been updated
				assert len(lowercase__				) == 0, F'''Some keys where not used {','.join(lowercase__				)}'''

				logger.info(F'''The following keys were not used: {','.join(lowercase__				)}'''				)

				# save our state dict to disk
				torch.save(lowercase__    ,       save_directory / F'''{model_name}.pth'''				)

				del converted_state_dict
		else:
				logger.info("The state_dict was already stored on disk."				)
		if push_to_hub:
				logger.info(F'''Token is {os.environ['HF_TOKEN']}'''				)
				logger.info("Loading our model."				)
				# create our model
				snake_case						:  Dict        =			names_to_config[model_name]
				snake_case						:  Tuple        =			RegNetModel
				if "in1k" in model_name:
						snake_case						:  int        =			RegNetForImageClassification
				snake_case						:  Any        =			our_model_func(lowercase__				)
				# place our model to the meta device (so remove all the weights)
				our_model.to(torch.device("meta"				)				)
				logger.info("Loading state_dict in our model."				)
				# load state dict
				snake_case						:  Optional[Any]        =			our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    lowercase__    ,       lowercase__    ,       [save_directory / F'''{model_name}.pth''']				)
				logger.info("Finally, pushing!"				)
				# push it to hub
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / model_name    ,       commit_message="Add model"    ,       output_dir=save_directory / model_name    ,       )
				snake_case						:  Dict        =			384
				# we can use the convnext one
				snake_case						:  str        =			AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"    ,       size=lowercase__				)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / model_name    ,       commit_message="Add image processor"    ,       output_dir=save_directory / model_name    ,       )


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

							__A							=     parser.parse_args()

							__A							=     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, is_vision_available


__A							=     {
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     ["FlavaFeatureExtractor"]
							__A							=     ["FlavaImageProcessor"]
							__A							=     ["FlavaProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "FlavaForPreTraining",
							    "FlavaImageCodebook",
							    "FlavaImageModel",
							    "FlavaModel",
							    "FlavaMultimodalModel",
							    "FlavaPreTrainedModel",
							    "FlavaTextModel",
							]

if TYPE_CHECKING:
							from .configuration_flava import (
							    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    FlavaConfig,
							    FlavaImageCodebookConfig,
							    FlavaImageConfig,
							    FlavaMultimodalConfig,
							    FlavaTextConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_flava import FlavaFeatureExtractor
														from .image_processing_flava import FlavaImageProcessor
														from .processing_flava import FlavaProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flava import (
														    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlavaForPreTraining,
														    FlavaImageCodebook,
														    FlavaImageModel,
														    FlavaModel,
														    FlavaMultimodalModel,
														    FlavaPreTrainedModel,
														    FlavaTextModel,
														)

else:
							import sys

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

