



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()
lowercase_  =   logging.get_logger()
@dataclass
class  _snake_case	:
				UpperCamelCase__ :    Tuple   						=4_2
				UpperCamelCase__ :    Optional[int]   						=field(default_factory=__UpperCamelCase)
				UpperCamelCase__ :    Any   						=field(default_factory=__UpperCamelCase)
				UpperCamelCase__ :    Dict   						=field(default_factory=__UpperCamelCase)




				def 							A__					(  self    :  Tuple,    __lowercase    :  List[str],    __lowercase    :  Tensor,    __lowercase    :  Tensor,    __lowercase    :  str  ):
							lowercase__         = len(list(m.modules()  )  ) == 1 or isinstance(__lowercase,    nn.Convad  ) or isinstance(__lowercase,    nn.BatchNormad  )
							if has_not_submodules:
										self.traced.append(__lowercase  )
										lowercase__         = m




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




				@property
				def 							A__					(  self    :  str  ):
							# 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  _snake_case	(    nn.Module):




				def __init__(  self    :  Optional[Any],    __lowercase    :  nn.Module  ):
							super().__init__()

							lowercase__         = []
							# - 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}'''
										lowercase__         = len(__lowercase  ) + 1
										feature_blocks.append((F'''res{block_index}''', v)  )

							lowercase__         = nn.ModuleDict(__lowercase  )




				def 							A__					(  self    :  int,    __lowercase    :  Tensor  ):
							return get_trunk_forward_outputs(
							    __lowercase,    out_feat_keys=__lowercase,    feature_blocks=self._feature_blocks,    )
class  _snake_case	(    __UpperCamelCase):




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

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

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

						pprint(dest_tracker.nameamodule     )
						lowercase__         = Tracker(SCREAMING_SNAKE_CASE__     )
						lowercase__         = src_tracker(SCREAMING_SNAKE_CASE__     ).parametrized

			# convert the keys -> module dict to keys -> params
			def to_params_dict(SCREAMING_SNAKE_CASE_     ):
						lowercase__         = OrderedDict()
						for name, module in dict_with_modules.items():
									for param_name, param in module.state_dict().items():
												lowercase__         = param
						return params_dict

			lowercase__         = {}

			lowercase__         = to_params_dict(SCREAMING_SNAKE_CASE__     )
			lowercase__         = to_params_dict(SCREAMING_SNAKE_CASE__     )

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

			return from_to_ours_keys



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_ = None  ,     SCREAMING_SNAKE_CASE_ = True     ):
			lowercase__         = 'imagenet-1k-id2label.json'
			lowercase__         = 1000

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

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

			lowercase__         = partial(SCREAMING_SNAKE_CASE__  ,     num_labels=SCREAMING_SNAKE_CASE__  ,     idalabel=SCREAMING_SNAKE_CASE__  ,     labelaid=SCREAMING_SNAKE_CASE__     )

			lowercase__         = {
			    '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(SCREAMING_SNAKE_CASE_     ) -> Tuple[Dict, Dict]:
						lowercase__         = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__  ,     model_dir=str(SCREAMING_SNAKE_CASE__     )  ,     map_location="cpu"     )
						# check if we have a head, if yes add it
						lowercase__         = files['classy_state_dict']['base_model']['model']
						return model_state_dict["trunk"], model_state_dict["heads"]

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

			lowercase__         = get_from_to_our_keys(SCREAMING_SNAKE_CASE__     )

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

						lowercase__         = {}

						lowercase__         = list(from_state_dict.keys()     )
						lowercase__         = 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
									lowercase__         = re.sub(SCREAMING_SNAKE_CASE__  ,     ""  ,     SCREAMING_SNAKE_CASE__     )
									# 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
									lowercase__         = from_to_ours_keys[src_key]
									# store the parameter with our key
									lowercase__         = from_state_dict[key]
									not_used_keys.remove(SCREAMING_SNAKE_CASE__     )
						# check that all keys have been updated
						assert len(SCREAMING_SNAKE_CASE__     ) == 0, f'''Some keys where not used {",".join(SCREAMING_SNAKE_CASE__     )}'''

						logger.info(f'''The following keys were not used: {",".join(SCREAMING_SNAKE_CASE__     )}'''     )

						# save our state dict to disk
						torch.save(SCREAMING_SNAKE_CASE__  ,     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
						lowercase__         = names_to_config[model_name]
						lowercase__         = RegNetModel
						if "in1k" in model_name:
									lowercase__         = RegNetForImageClassification
						lowercase__         = our_model_func(SCREAMING_SNAKE_CASE__     )
						# 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
						lowercase__         = our_model.state_dict().keys()
						PreTrainedModel._load_pretrained_model_low_mem(
						    SCREAMING_SNAKE_CASE__  ,     SCREAMING_SNAKE_CASE__  ,     [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  ,     )
						lowercase__         = 384
						# we can use the convnext one
						lowercase__         = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,     size=SCREAMING_SNAKE_CASE__     )
						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__":
							lowercase_  =   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.""",
							)

							lowercase_  =   parser.parse_args()

							lowercase_  =   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():
       import torch

       from transformers import CamembertModel


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




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

