
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()
lowercase        =  logging.get_logger()




@dataclass
class       __A:
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    field(default_factory=lowercase__       )
					SCREAMING_SNAKE_CASE			    =    field(default_factory=lowercase__       )

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ):
												lowerCamelCase_							  =					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					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												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    lowercase__      (	self					:     List[str]   ):
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda __UpperCamelCase   : len(list(x.state_dict().keys()   )   ) > 0   ,					self.traced   )   )




@dataclass
class       __A:
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    1
					SCREAMING_SNAKE_CASE			    =    field(default_factory=lowercase__       )
					SCREAMING_SNAKE_CASE			    =    field(default_factory=lowercase__       )
					SCREAMING_SNAKE_CASE			    =    True

					def __call__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					Tracker(self.dest   )(__UpperCamelCase   ).parametrized
												lowerCamelCase_							  =					Tracker(self.src   )(__UpperCamelCase   ).parametrized

												lowerCamelCase_							  =					list(filter(lambda __UpperCamelCase   : type(__UpperCamelCase   ) not in self.src_skip   ,					__UpperCamelCase   )   )
												lowerCamelCase_							  =					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       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[int]   ):
												super().__init__()

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

												lowerCamelCase_							  =					nn.ModuleDict(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ):
												return get_trunk_forward_outputs(
												    __UpperCamelCase   ,					out_feat_keys=__UpperCamelCase   ,					feature_blocks=self._feature_blocks   ,					)




class       __A(				lowercase__       ):

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Optional[int]   ):
												lowerCamelCase_							  =					x.split("""-"""   )
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]   )



					def __getitem__(	self					:     List[Any]   ,					__UpperCamelCase					:     Any   ):
												# default to timm!
												if x not in self:
																			lowerCamelCase_							  =					self.convert_name_to_timm(__UpperCamelCase   )
																			lowerCamelCase_							  =					partial(lambda: (timm.create_model(__UpperCamelCase   ,					pretrained=__UpperCamelCase   ).eval(), None)   )

												else:
																			lowerCamelCase_							  =					super().__getitem__(__UpperCamelCase   )

												return val




class       __A(				lowercase__       ):

					def __getitem__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												if "seer" in x and "in1k" not in x:
																			lowerCamelCase_							  =					RegNetModel
												else:
																			lowerCamelCase_							  =					RegNetForImageClassification
												return val



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      str       )  ->    Tuple:
							for from_key, to_key in keys:
														lowerCamelCase_							  =					from_state_dict[from_key].clone()
														print(F'''Copied key={from_key} to={to_key}'''       )
							return to_state_dict



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      List[Any] = True			,				)  ->    Optional[Any]:
							print(F'''Converting {name}...'''       )
							with torch.no_grad():
														lowerCamelCase_  ,   lowerCamelCase_							  =					from_model_func()
														lowerCamelCase_							  =					our_model_func(__SCREAMING_SNAKE_CASE       ).eval()
														lowerCamelCase_							  =					ModuleTransfer(src=__SCREAMING_SNAKE_CASE			,				dest=__SCREAMING_SNAKE_CASE			,				raise_if_mismatch=__SCREAMING_SNAKE_CASE       )
														lowerCamelCase_							  =					torch.randn((1, 3, 2_2_4, 2_2_4)       )
														module_transfer(__SCREAMING_SNAKE_CASE       )

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

							lowerCamelCase_							  =					our_model(__SCREAMING_SNAKE_CASE			,				output_hidden_states=__SCREAMING_SNAKE_CASE       )
							lowerCamelCase_							  =					(
							    our_outputs.logits if isinstance(__SCREAMING_SNAKE_CASE			,				__SCREAMING_SNAKE_CASE       ) else our_outputs.last_hidden_state
							)

							lowerCamelCase_							  =					from_model(__SCREAMING_SNAKE_CASE       )
							lowerCamelCase_							  =					from_output[-1] if type(__SCREAMING_SNAKE_CASE       ) 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:
														lowerCamelCase_							  =					our_outputs.hidden_states[-1]

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

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

														print(F'''Pushed {name}'''       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      Tuple = None			,				UpperCAmelCase__				:      Optional[int] = True       )  ->    int:
							lowerCamelCase_							  =					"""imagenet-1k-id2label.json"""
							lowerCamelCase_							  =					1_0_0_0
							lowerCamelCase_							  =					(1, num_labels)

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

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

							lowerCamelCase_							  =					partial(__SCREAMING_SNAKE_CASE			,				num_labels=__SCREAMING_SNAKE_CASE			,				idalabel=__SCREAMING_SNAKE_CASE			,				labelaid=__SCREAMING_SNAKE_CASE       )

							lowerCamelCase_							  =					{
							    """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       ),
							}

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

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

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

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

							lowerCamelCase_							  =					partial(
							    __SCREAMING_SNAKE_CASE			,				"""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()       )			,				)

							lowerCamelCase_							  =					partial(
							    __SCREAMING_SNAKE_CASE			,				"""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=6_2_0.8_3			,				w_m=2.5_2       )       )       )			,				)

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

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

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

							lowerCamelCase_							  =					partial(
							    __SCREAMING_SNAKE_CASE			,				"""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=6_2_0.8_3			,				w_m=2.5_2       )       )       )			,				)

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


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 math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					nn.Sequential(
												    nn.Linear(__UpperCamelCase   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					nn.Linear(d_model * 4   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					)

												lowerCamelCase_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.ModuleList()
												for lyr_num in range(__UpperCamelCase   ):
																			# FiLM conditional T5 decoder
																			lowerCamelCase_							  =					DecoderLayer(d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )
																			self.decoders.append(__UpperCamelCase   )

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

												# decoder_noise_time is in [0, 1), so rescale to expected timing range.
												lowerCamelCase_							  =					get_timestep_embedding(
												    decoder_noise_time * self.config.max_decoder_noise_time   ,					embedding_dim=self.config.d_model   ,					max_period=self.config.max_decoder_noise_time   ,					).to(dtype=self.dtype   )

												lowerCamelCase_							  =					self.conditioning_emb(__UpperCamelCase   ).unsqueeze(1   )

												assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

												lowerCamelCase_							  =					decoder_input_tokens.shape[1]

												# If we want to use relative positions for audio context, we can just offset
												# this sequence by the length of encodings_and_masks.
												lowerCamelCase_							  =					torch.broadcast_to(
												    torch.arange(__UpperCamelCase   ,					device=decoder_input_tokens.device   )   ,					(batch, seq_length)   ,					)

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												# decoder: No padding present.
												lowerCamelCase_							  =					torch.ones(
												    decoder_input_tokens.shape[:2]   ,					device=decoder_input_tokens.device   ,					dtype=inputs.dtype   )

												# Translate encoding masks to encoder-decoder masks.
												lowerCamelCase_							  =					[(x, self.encoder_decoder_mask(__UpperCamelCase   ,					__UpperCamelCase   )) for x, y in encodings_and_masks]

												# cross attend style: concat encodings
												lowerCamelCase_							  =					torch.cat([x[0] for x in encodings_and_encdec_masks]   ,					dim=1   )
												lowerCamelCase_							  =					torch.cat([x[1] for x in encodings_and_encdec_masks]   ,					dim=-1   )

												for lyr in self.decoders:
																			lowerCamelCase_							  =					lyr(
																			    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					encoder_attention_mask=__UpperCamelCase   ,					)[0]

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.ModuleList()

												# cond self attention: layer 0
												self.layer.append(
												    TaLayerSelfAttentionCond(d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )   )

												# cross attention: layer 1
												self.layer.append(
												    TaLayerCrossAttention(
												        d_model=__UpperCamelCase   ,					d_kv=__UpperCamelCase   ,					num_heads=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   ,					layer_norm_epsilon=__UpperCamelCase   ,					)   )

												# Film Cond MLP + dropout: last layer
												self.layer.append(
												    TaLayerFFCond(d_model=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   ,					layer_norm_epsilon=__UpperCamelCase   )   )



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												if encoder_hidden_states is not None:
																			lowerCamelCase_							  =					torch.where(encoder_attention_mask > 0   ,					0   ,					-1E10   ).to(
																			    encoder_hidden_states.dtype   )

																			lowerCamelCase_							  =					self.layer[1](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												# Apply Film Conditional Feed Forward layer
												lowerCamelCase_							  =					self.layer[-1](__UpperCamelCase   ,					__UpperCamelCase   )

												return (hidden_states,)





class       __A(				nn.Module       ):

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
												super().__init__()
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.FiLMLayer(__UpperCamelCase   ,					__UpperCamelCase   )

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ):
												super().__init__()
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					TaDenseGatedActDense(d_model=__UpperCamelCase   ,					d_ff=__UpperCamelCase   ,					dropout_rate=__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
												# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
												# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
												# half-precision inputs is done in fp32

												lowerCamelCase_							  =					hidden_states.to(torch.floataa   ).pow(2   ).mean(-1   ,					keepdim=__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

												# convert into half-precision if necessary
												if self.weight.dtype in [torch.floataa, torch.bfloataa]:
																			lowerCamelCase_							  =					hidden_states.to(self.weight.dtype   )

												return self.weight * hidden_states





class       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





