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()
_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger()


@dataclass
class 						lowercase    :
							lowercase__						:       nn.Module
							lowercase__						:       List[nn.Module]								=							field(default_factory=a     )
							lowercase__						:       list								=							field(default_factory=a     )




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Tensor	,							_UpperCamelCase   :					Tensor      )					->				int:



												'''simple docstring'''

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




							def __call__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Tensor      )					->				Optional[int]:



												'''simple docstring'''

												for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook      )      )
												self.module(_UpperCamelCase      )
												[x.remove() for x in self.handles]
												return self





							@property
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												return list(filter(lambda _UpperCamelCase      : len(list(x.state_dict().keys()      )      ) > 0	,							self.traced      )      )



@dataclass
class 						lowercase    :
							lowercase__						:       nn.Module
							lowercase__						:       nn.Module
							lowercase__						:       int								=							1
							lowercase__						:       List								=							field(default_factory=a     )
							lowercase__						:       List								=							field(default_factory=a     )
							lowercase__						:       bool								=							True




							def __call__(						self   :					int	,							_UpperCamelCase   :					Tensor      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Tracker(self.dest      )(_UpperCamelCase      ).parametrized
												SCREAMING_SNAKE_CASE    			=       Tracker(self.src      )(_UpperCamelCase      ).parametrized

												SCREAMING_SNAKE_CASE    			=       list(filter(lambda _UpperCamelCase      : type(_UpperCamelCase      ) not in self.src_skip	,							_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       list(filter(lambda _UpperCamelCase      : type(_UpperCamelCase      ) not in self.dest_skip	,							_UpperCamelCase      )      )

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

												for dest_m, src_m in zip(_UpperCamelCase	,							_UpperCamelCase      ):
																	dest_m.load_state_dict(src_m.state_dict()      )
																	if self.verbose == 1:
																						print(F"Transfered from={src_m} to={dest_m}"      )



class 						lowercase    (     nn.Module     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					nn.Module      )					->				Optional[Any]:



												'''simple docstring'''

												super().__init__()

												SCREAMING_SNAKE_CASE    			=       []
												# - 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}"
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) + 1
																	feature_blocks.append((F"res{block_index}", v)      )

												SCREAMING_SNAKE_CASE    			=       nn.ModuleDict(_UpperCamelCase      )





							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Tensor      )					->				str:



												'''simple docstring'''

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



class 						lowercase    (     a     ):




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					str      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       x.split("-"      )
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )





							def __getitem__(						self   :					Optional[int]	,							_UpperCamelCase   :					str      )					->				Callable[[], Tuple[nn.Module, Dict]]:



												'''simple docstring'''

												if x not in self:
																	SCREAMING_SNAKE_CASE    			=       self.convert_name_to_timm(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       partial(lambda: (timm.create_model(_UpperCamelCase	,							pretrained=_UpperCamelCase      ).eval(), None)      )

												else:
																	SCREAMING_SNAKE_CASE    			=       super().__getitem__(_UpperCamelCase      )

												return val



class 						lowercase    (     a     ):




							def __getitem__(						self   :					List[str]	,							_UpperCamelCase   :					str      )					->				Callable[[], nn.Module]:



												'''simple docstring'''

												if "seer" in x and "in1k" not in x:
																	SCREAMING_SNAKE_CASE    			=       RegNetModel
												else:
																	SCREAMING_SNAKE_CASE    			=       RegNetForImageClassification
												return val





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							List[Tuple[str, str]]  ):
					for from_key, to_key in keys:
										SCREAMING_SNAKE_CASE    			=       from_state_dict[from_key].clone()
										print(F"Copied key={from_key} to={to_key}"  )
					return to_state_dict





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Callable[[], nn.Module]      , UpperCAmelCase__     :							Callable[[], nn.Module]      , UpperCAmelCase__     :							RegNetConfig      , UpperCAmelCase__     :							Path      , UpperCAmelCase__     :							bool = True      , ):
					print(F"Converting {name}..."  )
					with torch.no_grad():
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       from_model_func()
										SCREAMING_SNAKE_CASE    			=       our_model_func(UpperCAmelCase__  ).eval()
										SCREAMING_SNAKE_CASE    			=       ModuleTransfer(src=UpperCAmelCase__      , dest=UpperCAmelCase__      , raise_if_mismatch=UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE    			=       torch.randn((1, 3, 2_2_4, 2_2_4)  )
										module_transfer(UpperCAmelCase__  )

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

					SCREAMING_SNAKE_CASE    			=       our_model(UpperCAmelCase__      , output_hidden_states=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       (
					    our_outputs.logits if isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) else our_outputs.last_hidden_state
					)

					SCREAMING_SNAKE_CASE    			=       from_model(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       from_output[-1] if type(UpperCAmelCase__  ) 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:
										SCREAMING_SNAKE_CASE    			=       our_outputs.hidden_states[-1]

					assert torch.allclose(UpperCAmelCase__      , UpperCAmelCase__  ), "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=UpperCAmelCase__      , )

										SCREAMING_SNAKE_CASE    			=       2_2_4 if "seer" not in name else 3_8_4
										# we can use the convnext one
										SCREAMING_SNAKE_CASE    			=       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"      , size=UpperCAmelCase__  )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / name      , commit_message="Add image processor"      , use_temp_dir=UpperCAmelCase__      , )

										print(F"Pushed {name}"  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Path      , UpperCAmelCase__     :							str = None      , UpperCAmelCase__     :							bool = True  ):
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       1_0_0_0
					SCREAMING_SNAKE_CASE    			=       (1, num_labels)

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

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

					SCREAMING_SNAKE_CASE    			=       partial(UpperCAmelCase__      , num_labels=UpperCAmelCase__      , idalabel=UpperCAmelCase__      , labelaid=UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       {
					    "regnet-x-002": ImageNetPreTrainedConfig(
					        depths=[1, 1, 4, 7]      , hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]      , groups_width=8      , layer_type="x"  ),
					    "regnet-x-004": ImageNetPreTrainedConfig(
					        depths=[1, 2, 7, 1_2]      , hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]      , groups_width=1_6      , layer_type="x"  ),
					    "regnet-x-006": ImageNetPreTrainedConfig(
					        depths=[1, 3, 5, 7]      , hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]      , groups_width=2_4      , layer_type="x"  ),
					    "regnet-x-008": ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 5]      , hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]      , groups_width=1_6      , layer_type="x"  ),
					    "regnet-x-016": ImageNetPreTrainedConfig(
					        depths=[2, 4, 1_0, 2]      , hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]      , groups_width=2_4      , layer_type="x"  ),
					    "regnet-x-032": ImageNetPreTrainedConfig(
					        depths=[2, 6, 1_5, 2]      , hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]      , groups_width=4_8      , layer_type="x"  ),
					    "regnet-x-040": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_4, 2]      , hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]      , groups_width=4_0      , layer_type="x"  ),
					    "regnet-x-064": ImageNetPreTrainedConfig(
					        depths=[2, 4, 1_0, 1]      , hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]      , groups_width=5_6      , layer_type="x"  ),
					    "regnet-x-080": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_5, 1]      , hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]      , groups_width=1_2_0      , layer_type="x"  ),
					    "regnet-x-120": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_1, 1]      , hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]      , groups_width=1_1_2      , layer_type="x"  ),
					    "regnet-x-160": ImageNetPreTrainedConfig(
					        depths=[2, 6, 1_3, 1]      , hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]      , groups_width=1_2_8      , layer_type="x"  ),
					    "regnet-x-320": ImageNetPreTrainedConfig(
					        depths=[2, 7, 1_3, 1]      , hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]      , groups_width=1_6_8      , layer_type="x"  ),
					    # y variant
					    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]      , hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]      , groups_width=8  ),
					    "regnet-y-004": ImageNetPreTrainedConfig(
					        depths=[1, 3, 6, 6]      , hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]      , groups_width=8  ),
					    "regnet-y-006": ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 4]      , hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]      , groups_width=1_6  ),
					    "regnet-y-008": ImageNetPreTrainedConfig(
					        depths=[1, 3, 8, 2]      , hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]      , groups_width=1_6  ),
					    "regnet-y-016": ImageNetPreTrainedConfig(
					        depths=[2, 6, 1_7, 2]      , hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]      , groups_width=2_4  ),
					    "regnet-y-032": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_3, 1]      , hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]      , groups_width=2_4  ),
					    "regnet-y-040": ImageNetPreTrainedConfig(
					        depths=[2, 6, 1_2, 2]      , hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]      , groups_width=6_4  ),
					    "regnet-y-064": ImageNetPreTrainedConfig(
					        depths=[2, 7, 1_4, 2]      , hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]      , groups_width=7_2  ),
					    "regnet-y-080": ImageNetPreTrainedConfig(
					        depths=[2, 4, 1_0, 1]      , hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]      , groups_width=5_6  ),
					    "regnet-y-120": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_1, 1]      , hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]      , groups_width=1_1_2  ),
					    "regnet-y-160": ImageNetPreTrainedConfig(
					        depths=[2, 4, 1_1, 1]      , hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]      , groups_width=1_1_2  ),
					    "regnet-y-320": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_2, 1]      , hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]      , groups_width=2_3_2  ),
					    # models created by SEER -> https://arxiv.org/abs/2202.08360
					    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 1_2, 1]      , hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]      , groups_width=2_3_2  ),
					    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 1_2, 1]      , hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]      , groups_width=3_2_8  ),
					    "regnet-y-1280-seer": RegNetConfig(
					        depths=[2, 7, 1_7, 1]      , hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]      , groups_width=2_6_4  ),
					    "regnet-y-2560-seer": RegNetConfig(
					        depths=[3, 7, 1_6, 1]      , hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]      , groups_width=6_4_0  ),
					    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
					        depths=[2, 7, 1_7, 1]      , hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]      , groups_width=1_0_1_0  ),
					    # finetuned on imagenet
					    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_2, 1]      , hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]      , groups_width=2_3_2  ),
					    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 5, 1_2, 1]      , hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]      , groups_width=3_2_8  ),
					    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 1_7, 1]      , hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]      , groups_width=2_6_4  ),
					    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[3, 7, 1_6, 1]      , hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]      , groups_width=6_4_0  ),
					    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 1_7, 1]      , hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]      , groups_width=1_0_1_0  ),
					}

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

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

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

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

					SCREAMING_SNAKE_CASE    			=       partial(
					    UpperCAmelCase__      , "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()  )      , )

					SCREAMING_SNAKE_CASE    			=       partial(
					    UpperCAmelCase__      , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"      , lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=2_7      , group_width=1_0_1_0      , w_a=1_7_4_4      , w_a=620.83      , w_m=2.52  )  )  )      , )

					# IN1K finetuned
					SCREAMING_SNAKE_CASE    			=       partial(
					    UpperCAmelCase__      , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"      , lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )      , )

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

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

					SCREAMING_SNAKE_CASE    			=       partial(
					    UpperCAmelCase__      , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"      , lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=2_7      , group_width=1_0_1_0      , w_a=1_7_4_4      , w_a=620.83      , w_m=2.52  )  )  )      , )

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


if __name__ == "__main__":
							_lowerCamelCase					:   List[Any]							=		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					:   Optional[Any]							=		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)





import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
_lowerCamelCase					:   Optional[int]							=		logging.getLogger(__name__)


_lowerCamelCase					:   Optional[int]							=		'''Hello world! cécé herlolip'''


_lowerCamelCase					:   List[Any]							=		namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       BertAbsConfig(
					    temp_dir="."      , finetune_bert=UpperCAmelCase__      , large=UpperCAmelCase__      , share_emb=UpperCAmelCase__      , use_bert_emb=UpperCAmelCase__      , encoder="bert"      , max_pos=5_1_2      , enc_layers=6      , enc_hidden_size=5_1_2      , enc_heads=8      , enc_ff_size=5_1_2      , enc_dropout=0.2      , dec_layers=6      , dec_hidden_size=7_6_8      , dec_heads=8      , dec_ff_size=2_0_4_8      , dec_dropout=0.2      , )
					SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__      , lambda UpperCAmelCase__      , UpperCAmelCase__  : storage  )
					SCREAMING_SNAKE_CASE    			=       AbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )      , UpperCAmelCase__  )
					original.eval()

					SCREAMING_SNAKE_CASE    			=       BertAbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )  )
					new_model.eval()

					# -------------------
					# Convert the weights
					# -------------------

					logging.info("convert the model"  )
					new_model.bert.load_state_dict(original.bert.state_dict()  )
					new_model.decoder.load_state_dict(original.decoder.state_dict()  )
					new_model.generator.load_state_dict(original.generator.state_dict()  )

					# ----------------------------------
					# Make sure the outpus are identical
					# ----------------------------------

					logging.info("Make sure that the models' outputs are identical"  )
					SCREAMING_SNAKE_CASE    			=       BertTokenizer.from_pretrained("bert-base-uncased"  )

					# prepare the model inputs
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample éàalj'-."  )
					encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample 3 éàalj'-."  )
					decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )

					# failsafe to make sure the weights reset does not affect the
					# loaded weights.
					assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight  )  ) == 0

					# forward pass
					SCREAMING_SNAKE_CASE    			=       encoder_input_ids
					SCREAMING_SNAKE_CASE    			=       decoder_input_ids
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None

					# The original model does not apply the geneator layer immediatly but rather in
					# the beam search (where it combines softmax + linear layer). Since we already
					# apply the softmax in our generation process we only apply the linear layer here.
					# We make sure that the outputs of the full stack are identical
					SCREAMING_SNAKE_CASE    			=       original(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       original.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       new_model(
					    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       new_model.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_model - output_original_model  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )
					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_generator - output_original_generator  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )

					SCREAMING_SNAKE_CASE    			=       torch.allclose(UpperCAmelCase__      , UpperCAmelCase__      , atol=1e-3  )
					if are_identical:
										logging.info("all weights are equal up to 1e-3"  )
					else:
										raise ValueError("the weights are different. The new model is likely different from the original one."  )

					# The model has been saved with torch.save(model) and this is bound to the exact
					# directory structure. We save the state_dict instead.
					logging.info("saving the model's state dictionary"  )
					torch.save(
					    new_model.state_dict()      , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"  )


if __name__ == "__main__":
							_lowerCamelCase					:   Union[str, Any]							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--bertabs_checkpoint_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path the official PyTorch dump.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the output PyTorch model.''',
							)
							_lowerCamelCase					:   Any							=		parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)




