

'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case_       =       logging.get_logger(__name__)


if is_vision_available():
      import PIL
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							["pixel_values"]




    def __init__(					self				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = PILImageResampling.BILINEAR				,     lowercase__ = True				,     lowercase__ = False				,     lowercase__ = True				,     lowercase__ = True				,     lowercase__ = 1 / 255				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = None				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       size if size is not None else {"height": 2560, "width": 1920}
          if isinstance(lowercase__				,     (tuple, list)			):
                # The previous feature extractor size parameter was in (width, height) format
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       size[::-1]
          SCREAMING_SNAKE_CASE_      :			int     =       get_size_dict(lowercase__			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       do_resize
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       size
          SCREAMING_SNAKE_CASE_      :			Tuple     =       resample
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       do_thumbnail
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       do_align_long_axis
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       do_pad
          SCREAMING_SNAKE_CASE_      :			List[str]     =       do_rescale
          SCREAMING_SNAKE_CASE_      :			Tuple     =       rescale_factor
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       do_normalize
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          SCREAMING_SNAKE_CASE_      :			int     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			int     =       get_image_size(lowercase__			)
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Optional[int]     =       size["height"], size["width"]

          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
                SCREAMING_SNAKE_CASE_      :			int     =       np.rotaa(lowercase__				,     3			)

          if data_format is not None:
                SCREAMING_SNAKE_CASE_      :			Dict     =       to_channel_dimension_format(lowercase__				,     lowercase__			)

          return image




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__ = False				,     lowercase__ = None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       size["height"], size["width"]
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			int     =       get_image_size(lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       output_width - input_width
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       output_height - input_height

          if random_padding:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       np.random.randint(low=0				,     high=delta_height + 1			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       np.random.randint(low=0				,     high=delta_width + 1			)
          else:
                SCREAMING_SNAKE_CASE_      :			Dict     =       delta_height // 2
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       delta_width // 2

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       delta_height - pad_top
          SCREAMING_SNAKE_CASE_      :			Dict     =       delta_width - pad_left

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       ((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(lowercase__				,     lowercase__				,     data_format=lowercase__			)




    def   __lowerCamelCase      (					self				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."			)
          return self.pad_image(*lowercase__				,     **lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__ = PILImageResampling.BICUBIC				,     lowercase__ = None				,     **lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Tuple     =       get_image_size(lowercase__			)
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       size["height"], size["width"]

          # We always resize to the smallest of either the input or output size.
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       min(lowercase__				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       min(lowercase__				,     lowercase__			)

          if height == input_height and width == input_width:
                return image

          if input_height > input_width:
                SCREAMING_SNAKE_CASE_      :			List[str]     =       int(input_width * height / input_height			)
          elif input_width > input_height:
                SCREAMING_SNAKE_CASE_      :			Dict     =       int(input_height * width / input_width			)

          return resize(
              lowercase__				,     size=(height, width)				,     resample=lowercase__				,     reducing_gap=2.0				,     data_format=lowercase__				,     **lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__ = PILImageResampling.BICUBIC				,     lowercase__ = None				,     **lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       get_size_dict(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Any     =       min(size["height"]				,     size["width"]			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       get_resize_output_image_size(lowercase__				,     size=lowercase__				,     default_to_square=lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       resize(lowercase__				,     size=lowercase__				,     resample=lowercase__				,     data_format=lowercase__				,     **lowercase__			)
          return resized_image




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):






          """simple docstring"""






          return rescale(lowercase__				,     scale=lowercase__				,     data_format=lowercase__				,     **lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):






          """simple docstring"""






          return normalize(lowercase__				,     mean=lowercase__				,     std=lowercase__				,     data_format=lowercase__				,     **lowercase__			)







    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = False				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = ChannelDimension.FIRST				,     **lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_      :			int     =       size if size is not None else self.size
          if isinstance(lowercase__				,     (tuple, list)			):
                # Previous feature extractor had size in (width, height) format
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       size[::-1]
          SCREAMING_SNAKE_CASE_      :			str     =       get_size_dict(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_      :			Tuple     =       do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          SCREAMING_SNAKE_CASE_      :			Dict     =       do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          SCREAMING_SNAKE_CASE_      :			Tuple     =       do_pad if do_pad is not None else self.do_pad
          SCREAMING_SNAKE_CASE_      :			Dict     =       do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :			int     =       rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_      :			int     =       image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_std if image_std is not None else self.image_std

          SCREAMING_SNAKE_CASE_      :			str     =       make_list_of_images(lowercase__			)

          if not valid_images(lowercase__			):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."			)

          if do_resize and size is None:
                raise ValueError("Size must be specified if do_resize is True."			)

          if do_rescale and rescale_factor is None:
                raise ValueError("Rescale factor must be specified if do_rescale is True."			)

          if do_pad and size is None:
                raise ValueError("Size must be specified if do_pad is True."			)

          if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("Image mean and std must be specified if do_normalize is True."			)

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :			Tuple     =       [to_numpy_array(lowercase__			) for image in images]

          if do_align_long_axis:
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       [self.align_long_axis(lowercase__				,     size=lowercase__			) for image in images]

          if do_resize:
                SCREAMING_SNAKE_CASE_      :			Any     =       [self.resize(image=lowercase__				,     size=lowercase__				,     resample=lowercase__			) for image in images]

          if do_thumbnail:
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [self.thumbnail(image=lowercase__				,     size=lowercase__			) for image in images]

          if do_pad:
                SCREAMING_SNAKE_CASE_      :			List[str]     =       [self.pad_image(image=lowercase__				,     size=lowercase__				,     random_padding=lowercase__			) for image in images]

          if do_rescale:
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [self.rescale(image=lowercase__				,     scale=lowercase__			) for image in images]

          if do_normalize:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       [self.normalize(image=lowercase__				,     mean=lowercase__				,     std=lowercase__			) for image in images]

          SCREAMING_SNAKE_CASE_      :			Tuple     =       [to_channel_dimension_format(lowercase__				,     lowercase__			) for image in images]

          SCREAMING_SNAKE_CASE_      :			Dict     =       {"pixel_values": images}
          return BatchFeature(data=lowercase__				,     tensor_type=lowercase__			)



'''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()
lowerCAmelCase :  Dict      =      logging.get_logger()


@dataclass
class        SCREAMING_SNAKE_CASE__	:
				lowerCAmelCase_			       =				42
				lowerCAmelCase_			       =				field(default_factory=snake_case_)
				lowerCAmelCase_			       =				field(default_factory=snake_case_)
				lowerCAmelCase_			       =				field(default_factory=snake_case_)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							A_			)->						str:







											'''simple docstring'''






											UpperCamelCase					       =			len(list(m.modules()			)			) == 1 or isinstance(A_		,							nn.Convad			) or isinstance(A_		,							nn.BatchNormad			)
											if has_not_submodules:
																		self.traced.append(A_			)
																		UpperCamelCase					       =			m







				def __call__(    self		,							A_			)->						int:







											'''simple docstring'''






											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			)->						Union[str, Any]:







											'''simple docstring'''






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


class        SCREAMING_SNAKE_CASE__	(			nn.Module):







				def __init__(    self		,							A_			)->						List[Any]:







											'''simple docstring'''






											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(A_			) + 1
																		feature_blocks.append((F'''res{block_index}''', v)			)

											UpperCamelCase					       =			nn.ModuleDict(A_			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






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


class        SCREAMING_SNAKE_CASE__	(			snake_case_):







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







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

							with torch.no_grad():
														UpperCamelCase					       =			from_model.eval()
														UpperCamelCase					       =			our_model.eval()

														UpperCamelCase					       =			torch.randn((1, 3, 32, 32))
														# trace both
														UpperCamelCase					       =			Tracker(A)
														UpperCamelCase					       =			dest_tracker(A).parametrized

														pprint(dest_tracker.nameamodule)
														UpperCamelCase					       =			Tracker(A)
														UpperCamelCase					       =			src_tracker(A).parametrized

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

							UpperCamelCase					       =			{}

							UpperCamelCase					       =			to_params_dict(A)
							UpperCamelCase					       =			to_params_dict(A)

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,			dst_state_dict.items()):
														UpperCamelCase					       =			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:
														UpperCamelCase					       =			'classifier.1.weight'
														UpperCamelCase					       =			'classifier.1.bias'

							return from_to_ours_keys







def 						A_(			A		:						Path						,			A		:						str = None						,			A		:						bool = True):
							UpperCamelCase					       =			'imagenet-1k-id2label.json'
							UpperCamelCase					       =			1000

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

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

							UpperCamelCase					       =			partial(A						,			num_labels=A						,			idalabel=A						,			labelaid=A)

							UpperCamelCase					       =			{
							    '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(A		:						str) -> Tuple[Dict, Dict]:
														UpperCamelCase					       =			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
														UpperCamelCase					       =			files['classy_state_dict']['base_model']['model']
														return model_state_dict["trunk"], model_state_dict["heads"]

							UpperCamelCase					       =			{
							    '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'						,			),
							}

							UpperCamelCase					       =			get_from_to_our_keys(A)

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

														UpperCamelCase					       =			{}

														UpperCamelCase					       =			list(from_state_dict.keys())
														UpperCamelCase					       =			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
																					UpperCamelCase					       =			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
																					UpperCamelCase					       =			from_to_ours_keys[src_key]
																					# store the parameter with our key
																					UpperCamelCase					       =			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
														UpperCamelCase					       =			names_to_config[model_name]
														UpperCamelCase					       =			RegNetModel
														if "in1k" in model_name:
																					UpperCamelCase					       =			RegNetForImageClassification
														UpperCamelCase					       =			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
														UpperCamelCase					       =			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						,			)
														UpperCamelCase					       =			384
														# we can use the convnext one
														UpperCamelCase					       =			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__":
						lowerCAmelCase :  List[str]      =      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.',
						)

						lowerCAmelCase :  int      =      parser.parse_args()

						lowerCAmelCase :  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)




