





import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate





class 						_A( _a       ):


				"""simple docstring"""


				UpperCamelCase   :  Dict    =     CONFIG_NAME



				@register_to_config
				def __init__(      self					, _A = True					, _A = 8					, _A = "lanczos"					, _A = True					, _A = False					, ):
										super().__init__()



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										if images.ndim == 3:
																__A     :						Optional[int]          =    images[None, ...]
										__A     :						Optional[Any]          =    (images * 255).round().astype('uint8'					)
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																__A     :						Dict          =    [Image.fromarray(image.squeeze()					, mode='L'					) for image in images]
										else:
																__A     :						Any          =    [Image.fromarray(SCREAMING_SNAKE_CASE__					) for image in images]

										return pil_images



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										if not isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					):
																__A     :						Any          =    [images]
										__A     :						str          =    [np.array(SCREAMING_SNAKE_CASE__					).astype(np.floataa					) / 2_5_5.0 for image in images]
										__A     :						List[Any]          =    np.stack(SCREAMING_SNAKE_CASE__					, axis=0					)

										return images



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										if images.ndim == 3:
																__A     :						Optional[int]          =    images[..., None]

										__A     :						Any          =    torch.from_numpy(images.transpose(0					, 3					, 1					, 2					)					)
										return images



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										__A     :						int          =    images.cpu().permute(0					, 2					, 3					, 1					).float().numpy()
										return images



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										return 2.0 * images - 1.0



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										return (images / 2 + 0.5).clamp(0					, 1					)



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										__A     :						Optional[Any]          =    image.convert('RGB'					)
										return image



				def  UpperCAmelCase_		(      self					, _A					, _A = None					, _A = None					, ):
										if height is None:
																__A     :						Any          =    image.height
										if width is None:
																__A     :						Optional[Any]          =    image.width

										__A     :						Union[str, Any]          =    (
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										__A     :						str          =    image.resize((width, height)					, resample=PIL_INTERPOLATION[self.config.resample]					)
										return image



				def  UpperCAmelCase_		(      self					, _A					, _A = None					, _A = None					, ):
										__A     :						Optional[Any]          =    (PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					):
																__A     :						Any          =    [image]
										elif not (isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					) and all(isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					) for i in image					)):
																raise ValueError(
																    F"""Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE__					) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE__					)}"""					)

										if isinstance(image[0]					, PIL.Image.Image					):
																if self.config.do_convert_rgb:
																						__A     :						str          =    [self.convert_to_rgb(SCREAMING_SNAKE_CASE__					) for i in image]
																if self.config.do_resize:
																						__A     :						Union[str, Any]          =    [self.resize(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					) for i in image]
																__A     :						List[Any]          =    self.pil_to_numpy(SCREAMING_SNAKE_CASE__					)  # to np
																__A     :						Optional[Any]          =    self.numpy_to_pt(SCREAMING_SNAKE_CASE__					)  # to pt

										elif isinstance(image[0]					, np.ndarray					):
																__A     :						Any          =    np.concatenate(SCREAMING_SNAKE_CASE__					, axis=0					) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE__					, axis=0					)
																__A     :						Optional[Any]          =    self.numpy_to_pt(SCREAMING_SNAKE_CASE__					)
																__A     :						Optional[Any]          =    image.shape
																if self.config.do_resize and (
																    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																):
																						raise ValueError(
																						    F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																						    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""					)

										elif isinstance(image[0]					, torch.Tensor					):
																__A     :						Optional[Any]          =    torch.cat(SCREAMING_SNAKE_CASE__					, axis=0					) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__					, axis=0					)
																__A     :						str          =    image.shape

																# don't need any preprocess if the image is latents
																if channel == 4:
																						return image

																if self.config.do_resize and (
																    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																):
																						raise ValueError(
																						    F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																						    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""					)

																 # expected range [0,1], normalize to [-1,1]
										__A     :						int          =    self.config.do_normalize
										if image.min() < 0:
																warnings.warn(
																    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
																    F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""					, SCREAMING_SNAKE_CASE__					, )
																__A     :						Union[str, Any]          =    False

										if do_normalize:
																__A     :						List[str]          =    self.normalize(SCREAMING_SNAKE_CASE__					)

										return image





				def  UpperCAmelCase_		(      self					, _A					, _A = "pil"					, _A = None					, ):
										if not isinstance(SCREAMING_SNAKE_CASE__					, torch.Tensor					):
																raise ValueError(
																    F"""Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE__					)}. We only support pytorch tensor"""					)
										if output_type not in ["latent", "pt", "np", "pil"]:
																__A     :						List[Any]          =    (
																    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																    "`pil`, `np`, `pt`, `latent`"
																)
																deprecate('Unsupported output_type'					, '1.0.0'					, SCREAMING_SNAKE_CASE__					, standard_warn=SCREAMING_SNAKE_CASE__					)
																__A     :						List[str]          =    "np"

										if output_type == "latent":
																return image

										if do_denormalize is None:
																__A     :						Union[str, Any]          =    [self.config.do_normalize] * image.shape[0]

										__A     :						Union[str, Any]          =    torch.stack(
										    [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

										if output_type == "pt":
																return image

										__A     :						Tuple          =    self.pt_to_numpy(SCREAMING_SNAKE_CASE__					)

										if output_type == "np":
																return image

										if output_type == "pil":
																return self.numpy_to_pil(SCREAMING_SNAKE_CASE__					)





class 						_A( _a       ):


				"""simple docstring"""


				UpperCamelCase   :  Any    =     CONFIG_NAME



				@register_to_config
				def __init__(      self					, _A = True					, _A = 8					, _A = "lanczos"					, _A = True					, ):
										super().__init__()



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										if images.ndim == 3:
																__A     :						Tuple          =    images[None, ...]
										__A     :						Union[str, Any]          =    (images * 255).round().astype('uint8'					)
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																__A     :						str          =    [Image.fromarray(image.squeeze()					, mode='L'					) for image in images]
										else:
																__A     :						Tuple          =    [Image.fromarray(image[:, :, :3]					) for image in images]

										return pil_images



				@staticmethod
				def  UpperCAmelCase_		(      _A					):
										return image[:, :, 1] * 2**8 + image[:, :, 2]



				def  UpperCAmelCase_		(      self					, _A					):
										if images.ndim == 3:
																__A     :						Union[str, Any]          =    images[None, ...]
										__A     :						Optional[Any]          =    images[:, :, :, 3:]
										if images.shape[-1] == 6:
																__A     :						Optional[int]          =    (images_depth * 255).round().astype('uint8'					)
																__A     :						Tuple          =    [
																    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE__					)					, mode='I;16'					) for image_depth in images_depth
																]
										elif images.shape[-1] == 4:
																__A     :						List[str]          =    (images_depth * 65535.0).astype(np.uintaa					)
																__A     :						Dict          =    [Image.fromarray(SCREAMING_SNAKE_CASE__					, mode='I;16'					) for image_depth in images_depth]
										else:
																raise Exception('Not supported'					)

										return pil_images





				def  UpperCAmelCase_		(      self					, _A					, _A = "pil"					, _A = None					, ):
										if not isinstance(SCREAMING_SNAKE_CASE__					, torch.Tensor					):
																raise ValueError(
																    F"""Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE__					)}. We only support pytorch tensor"""					)
										if output_type not in ["latent", "pt", "np", "pil"]:
																__A     :						Union[str, Any]          =    (
																    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																    "`pil`, `np`, `pt`, `latent`"
																)
																deprecate('Unsupported output_type'					, '1.0.0'					, SCREAMING_SNAKE_CASE__					, standard_warn=SCREAMING_SNAKE_CASE__					)
																__A     :						List[str]          =    "np"

										if do_denormalize is None:
																__A     :						List[Any]          =    [self.config.do_normalize] * image.shape[0]

										__A     :						Optional[Any]          =    torch.stack(
										    [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

										__A     :						Union[str, Any]          =    self.pt_to_numpy(SCREAMING_SNAKE_CASE__					)

										if output_type == "np":
																if image.shape[-1] == 6:
																						__A     :						int          =    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]					) for im in image]					, axis=0					)
																else:
																						__A     :						Tuple          =    image[:, :, :, 3:]
																return image[:, :, :, :3], image_depth

										if output_type == "pil":
																return self.numpy_to_pil(SCREAMING_SNAKE_CASE__					), self.numpy_to_depth(SCREAMING_SNAKE_CASE__					)
										else:
																raise Exception(F"""This type {output_type} is not supported"""					)











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()
UpperCAmelCase						:       Tuple        =					logging.get_logger()





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase   :  nn.Module
				UpperCamelCase   :  List[nn.Module]    =     field(default_factory=snake_case__       )
				UpperCamelCase   :  list    =     field(default_factory=snake_case__       )
				UpperCamelCase   :  Dict[str, nn.Module]    =     field(default_factory=snake_case__       )



				def  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					):
									__A     :						Tuple          =    len(list(m.modules()					)					) == 1 or isinstance(_A					, nn.Convad					) or isinstance(_A					, nn.BatchNormad					)
									if has_not_submodules:
														self.traced.append(_A					)
														__A     :						str          =    m



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





				@property
				def  UpperCAmelCase_		(      self					):
									# check the len of the state_dict keys to see if we have learnable params
									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()					)					) > 0}





class 						_A( nn.Module       ):


				"""simple docstring"""


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

									__A     :						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}"""
														__A     :						Any          =    len(_A					) + 1
														feature_blocks.append((F"""res{block_index}""", v)					)

									__A     :						List[str]          =    nn.ModuleDict(_A					)





				def  UpperCAmelCase_		(      self					, _A					):
									return get_trunk_forward_outputs(
									    _A					, out_feat_keys=_A					, feature_blocks=self._feature_blocks					, )





class 						_A( snake_case__       ):


				"""simple docstring"""


				def  UpperCAmelCase_		(      self					):
									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 			_SCREAMING_SNAKE_CASE    (       a  )				->				Dict[str, str]:
					__A     :						int          =    RegNetConfig(depths=[2, 7, 17, 1] ,						hidden_sizes=[8, 8, 8, 8] ,						groups_width=8  )
					if "in1k" in model_name:
										__A     :						Any          =    RegNetForImageClassification(a  )
					else:
										__A     :						Tuple          =    RegNetModel(a  )
					# create from model (with small weights)
					__A     :						Union[str, Any]          =    FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27 ,						group_width=10_10 ,						w_a=17_44 ,						w_a=620.83 ,						w_m=2.52  )  )  )

					with torch.no_grad():
										__A     :						List[str]          =    from_model.eval()
										__A     :						Tuple          =    our_model.eval()

										__A     :						str          =    torch.randn((1, 3, 32, 32)  )
										# trace both
										__A     :						Union[str, Any]          =    Tracker(a  )
										__A     :						Optional[int]          =    dest_tracker(a  ).parametrized

										pprint(dest_tracker.nameamodule  )
										__A     :						Optional[int]          =    Tracker(a  )
										__A     :						Union[str, Any]          =    src_tracker(a  ).parametrized

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

					__A     :						Dict          =    {}

					__A     :						Dict          =    to_params_dict(a  )
					__A     :						Any          =    to_params_dict(a  )

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() ,						dst_state_dict.items()  ):
										__A     :						List[str]          =    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:
										__A     :						Optional[Any]          =    'classifier.1.weight'
										__A     :						Dict          =    'classifier.1.bias'

					return from_to_ours_keys







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = None ,						a = True  )				->				Union[str, Any]:
					__A     :						Any          =    'imagenet-1k-id2label.json'
					__A     :						Optional[int]          =    10_00

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

					__A     :						Optional[int]          =    idalabel
					__A     :						Any          =    {v: k for k, v in idalabel.items()}

					__A     :						List[Any]          =    partial(a ,						num_labels=a ,						idalabel=a ,						labelaid=a  )

					__A     :						Optional[int]          =    {
					    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,						hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] ,						groups_width=10_10  ),
					    # finetuned on imagenet
					    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,						hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] ,						groups_width=10_10  ),
					}

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

					__A     :						Dict          =    {
					    'regnet-y-10b-seer': partial(
					        a ,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' ,						),
					    'regnet-y-10b-seer-in1k': partial(
					        a ,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' ,						),
					}

					__A     :						Dict          =    get_from_to_our_keys(a  )

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

										__A     :						Optional[Any]          =    {}

										__A     :						Tuple          =    list(from_state_dict.keys()  )
										__A     :						List[str]          =    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
															__A     :						Optional[int]          =    re.sub(a ,						'' ,						a  )
															# 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
															__A     :						Union[str, Any]          =    from_to_ours_keys[src_key]
															# store the parameter with our key
															__A     :						Union[str, Any]          =    from_state_dict[key]
															not_used_keys.remove(a  )
										# check that all keys have been updated
										assert len(a  ) == 0, F"""Some keys where not used {",".join(a  )}"""

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

										# save our state dict to disk
										torch.save(a ,						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
										__A     :						Optional[int]          =    names_to_config[model_name]
										__A     :						Any          =    RegNetModel
										if "in1k" in model_name:
															__A     :						Dict          =    RegNetForImageClassification
										__A     :						str          =    our_model_func(a  )
										# 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
										__A     :						List[Any]          =    our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    a ,						a ,						[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 ,						)
										__A     :						Union[str, Any]          =    3_84
										# we can use the convnext one
										__A     :						Union[str, Any]          =    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' ,						size=a  )
										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__":
				UpperCAmelCase						:       int        =					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						:       Optional[int]        =					parser.parse_args()

				UpperCAmelCase						:       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)




