






"""simple docstring"""


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()
_UpperCamelCase    :   List[Any]   			=						logging.get_logger()






@dataclass
class 						snake_case  :
				__magic_name__   					=					42
				__magic_name__   					=					field(default_factory=UpperCAmelCase	)
				__magic_name__   					=					field(default_factory=UpperCAmelCase	)





				def     lowerCamelCase__				(							self     : str    ,		A     : Any    ,		A     : Tensor    ,		A     : Tensor      ):


						'''simple docstring'''




						a					:      Optional[int]    =       len(list(m.modules()      )      ) == 1 or isinstance(A    ,		nn.Convad      ) or isinstance(A    ,		nn.BatchNormad      )
						if has_not_submodules:
								self.traced.append(A      )





				def __call__(							self     : List[Any]    ,		A     : Tensor      ):


						'''simple docstring'''




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





				@property
				def     lowerCamelCase__				(							self     : Optional[int]      ):


						'''simple docstring'''




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






@dataclass
class 						snake_case  :
				__magic_name__   					=					42
				__magic_name__   					=					42
				__magic_name__   					=					1
				__magic_name__   					=					field(default_factory=UpperCAmelCase	)
				__magic_name__   					=					field(default_factory=UpperCAmelCase	)
				__magic_name__   					=					True





				def __call__(							self     : List[str]    ,		A     : Tensor      ):


						'''simple docstring'''




						a					:      Any    =       Tracker(self.dest      )(A      ).parametrized
						a					:      Any    =       Tracker(self.src      )(A      ).parametrized

						a					:      Union[str, Any]    =       list(filter(lambda A      : type(A      ) not in self.src_skip    ,		A      )      )
						a					:      Any    =       list(filter(lambda A      : type(A      ) not in self.dest_skip    ,		A      )      )

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

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






class 						snake_case  (  nn.Module	):





				def __init__(							self     : Any    ,		A     : nn.Module      ):


						'''simple docstring'''




						super().__init__()

						a					:      List[Tuple[str, nn.Module]]    =       []
						# - get the stem
						feature_blocks.append(('conv1', model.stem)      )
						# - get all the feature blocks
						for k, v in model.trunk_output.named_children():
								assert k.startswith('block'      ), F'''Unexpected layer name {k}'''
								a					:      int    =       len(A      ) + 1
								feature_blocks.append((F'''res{block_index}''', v)      )

						a					:      Union[str, Any]    =       nn.ModuleDict(A      )





				def     lowerCamelCase__				(							self     : List[str]    ,		A     : Tensor      ):


						'''simple docstring'''




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






class 						snake_case  (  UpperCAmelCase	):





				def     lowerCamelCase__				(							self     : List[Any]    ,		A     : str      ):


						'''simple docstring'''




						a					:      List[Any]    =       x.split('-'      )
						return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )





				def __getitem__(							self     : Tuple    ,		A     : str      ):


						'''simple docstring'''




						if x not in self:
								a					:      str    =       self.convert_name_to_timm(A      )
								a					:      str    =       partial(lambda: (timm.create_model(A    ,		pretrained=A      ).eval(), None)      )

						else:
								a					:      List[str]    =       super().__getitem__(A      )

						return val






class 						snake_case  (  UpperCAmelCase	):





				def __getitem__(							self     : Optional[int]    ,		A     : str      ):


						'''simple docstring'''




						if "seer" in x and "in1k" not in x:
								a					:      List[str]    =       RegNetModel
						else:
								a					:      Optional[int]    =       RegNetForImageClassification
						return val




def       snake_case  (A_				:Union[str, Any]     ,      A_				:Dict     ,      A_				:List[Tuple[str, str]]				):







		'''simple docstring'''

		for from_key, to_key in keys:
				a					:      List[Any]    =       from_state_dict[from_key].clone()
				print(f'''Copied key={from_key} to={to_key}'''				)
		return to_state_dict




def       snake_case  (A_				:str     ,      A_				:Callable[[], nn.Module]     ,      A_				:Callable[[], nn.Module]     ,      A_				:RegNetConfig     ,      A_				:Path     ,      A_				:bool = True     ,      ):







		'''simple docstring'''

		print(f'''Converting {name}...'''				)
		with torch.no_grad():
				a					:      int    =       from_model_func()
				a					:      List[str]    =       our_model_func(A_				).eval()
				a					:      List[Any]    =       ModuleTransfer(src=A_     ,      dest=A_     ,      raise_if_mismatch=A_				)
				a					:      Tuple    =       torch.randn((1, 3, 2_2_4, 2_2_4)				)
				module_transfer(A_				)

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

		a					:      Optional[int]    =       our_model(A_     ,      output_hidden_states=A_				)
		a					:      List[Any]    =       (
		    our_outputs.logits if isinstance(A_     ,      A_				) else our_outputs.last_hidden_state
		)

		a					:      Tuple    =       from_model(A_				)
		a					:      Dict    =       from_output[-1] if type(A_				) 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:
				a					:      List[Any]    =       our_outputs.hidden_states[-1]

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

				a					:      List[str]    =       2_2_4 if 'seer' not in name else 3_8_4
				# we can use the convnext one
				a					:      Union[str, Any]    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,      size=A_				)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / name     ,      commit_message='Add image processor'     ,      use_temp_dir=A_     ,      )

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




def       snake_case  (A_				:Path     ,      A_				:str = None     ,      A_				:bool = True				):







		'''simple docstring'''

		a					:      Union[str, Any]    =       'imagenet-1k-id2label.json'
		a					:      Dict    =       1_0_0_0
		a					:      int    =       (1, num_labels)

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

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

		a					:      int    =       partial(A_     ,      num_labels=A_     ,      idalabel=A_     ,      labelaid=A_				)

		a					:      List[str]    =       {
		    '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				),
		}

		a					:      Union[str, Any]    =       NameToOurModelFuncMap()
		a					:      str    =       NameToFromModelFuncMap()
		# add seer weights logic

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

		# pretrained
		a					:      str    =       partial(
		    A_     ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'     ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,      )

		a					:      List[Any]    =       partial(
		    A_     ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'     ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,      )

		a					:      Any    =       partial(
		    A_     ,      '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()				)     ,      )

		a					:      Union[str, Any]    =       partial(
		    A_     ,      '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
		a					:      int    =       partial(
		    A_     ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,      )

		a					:      Union[str, Any]    =       partial(
		    A_     ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,      )

		a					:      int    =       partial(
		    A_     ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)     ,      )

		a					:      Tuple    =       partial(
		    A_     ,      '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(
				    A_     ,      names_to_from_model_map[model_name]     ,      names_to_ours_model_map[model_name]     ,      names_to_config[model_name]     ,      A_     ,      A_     ,      )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    A_     ,      names_to_from_model_map[model_name]     ,      names_to_ours_model_map[model_name]     ,      A_     ,      A_     ,      A_     ,      )
		return config, expected_shape


if __name__ == "__main__":
			_UpperCamelCase    :   Dict   			=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default=None,
			    type=str,
			    help=(
			        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
			        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=Path,
			    required=True,
			    help='Path to the output PyTorch model directory.',
			)
			parser.add_argument(
			    '--push_to_hub',
			    default=True,
			    type=bool,
			    required=False,
			    help='If True, push model and image processor to the hub.',
			)

			_UpperCamelCase    :   Dict   			=						parser.parse_args()

			_UpperCamelCase    :   Path   			=						args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












"""simple docstring"""


from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




