






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 :      List[str]										=				logging.get_logger()



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





     """simple docstring"""






     a_     =		42
     a_     =		field(default_factory=lowerCamelCase__						)
     a_     =		field(default_factory=lowerCamelCase__						)



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



     def __call__( self					:  Tuple							,		__A					:  Tensor	):
      for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook	)	)
      self.module(lowercase__	)
      [x.remove() for x in self.handles]
      return self






     @property
     def 	_lowercase					( self					:  Any	):
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __A	: len(list(x.state_dict().keys()	)	) > 0							,		self.traced	)	)



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





     """simple docstring"""






     a_     =		42
     a_     =		42
     a_     =		1
     a_     =		field(default_factory=lowerCamelCase__						)
     a_     =		field(default_factory=lowerCamelCase__						)
     a_     =		True



     def __call__( self					:  int							,		__A					:  Tensor	):
      snake_case__			:				str       =  Tracker(self.dest	)(lowercase__	).parametrized
      snake_case__			:				List[Any]       =  Tracker(self.src	)(lowercase__	).parametrized

      snake_case__			:				List[str]       =  list(filter(lambda __A	: type(lowercase__	) not in self.src_skip							,		lowercase__	)	)
      snake_case__			:				Tuple       =  list(filter(lambda __A	: type(lowercase__	) not in self.dest_skip							,		lowercase__	)	)

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

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



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):





     """simple docstring"""






     def __init__( self					:  Tuple							,		__A					:  nn.Module	):
      super().__init__()

      snake_case__			:				Dict       =  []
      # - 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}'''
       snake_case__			:				List[str]       =  len(lowercase__	) + 1
       feature_blocks.append((f'''res{block_index}''', v)	)

      snake_case__			:				Dict       =  nn.ModuleDict(lowercase__	)






     def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Tensor	):
      return get_trunk_forward_outputs(
          lowercase__							,		out_feat_keys=lowercase__							,		feature_blocks=self._feature_blocks							,		)



class 							SCREAMING_SNAKE_CASE__						(    lowerCamelCase__						):





     """simple docstring"""






     def 	_lowercase					( self					:  Optional[int]							,		__A					:  str	):
      snake_case__			:				Any       =  x.split("-"	)
      return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]	)






     def __getitem__( self					:  Tuple							,		__A					:  str	):
      # default to timm!
      if x not in self:
       snake_case__			:				Optional[int]       =  self.convert_name_to_timm(lowercase__	)
       snake_case__			:				Tuple       =  partial(lambda: (timm.create_model(lowercase__							,		pretrained=lowercase__	).eval(), None)	)

      else:
       snake_case__			:				int       =  super().__getitem__(lowercase__	)

      return val



class 							SCREAMING_SNAKE_CASE__						(    lowerCamelCase__						):





     """simple docstring"""






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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[str]					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):



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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Union[str, Any]					,  snake_case_ :							Dict					,  snake_case_ :							List[Any]					,  snake_case_ :							List[Any]					,  snake_case_ :							List[str] = True					,  ):
 print(F'''Converting {name}...'''       )
 with torch.no_grad():
  snake_case__,						snake_case__			:				Union[str, Any]       =  from_model_func()
  snake_case__			:				Optional[Any]       =  our_model_func(A__       ).eval()
  snake_case__			:				List[Any]       =  ModuleTransfer(src=A__					,  dest=A__					,  raise_if_mismatch=A__       )
  snake_case__			:				Optional[Any]       =  torch.randn((1, 3, 224, 224)       )
  module_transfer(A__       )

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

 snake_case__			:				List[Any]       =  our_model(A__					,  output_hidden_states=A__       )
 snake_case__			:				Union[str, Any]       =  (
     our_outputs.logits if isinstance(A__					,  A__       ) else our_outputs.last_hidden_state
 )

 snake_case__			:				Union[str, Any]       =  from_model(A__       )
 snake_case__			:				Union[str, Any]       =  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:
  snake_case__			:				Tuple       =  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__					,  )

  snake_case__			:				Optional[int]       =  224 if "seer" not in name else 384
  # we can use the convnext one
  snake_case__			:				List[str]       =  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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							List[Any] = None					,  snake_case_ :							List[str] = True       ):
 snake_case__			:				Union[str, Any]       =  "imagenet-1k-id2label.json"
 snake_case__			:				Dict       =  1000
 snake_case__			:				Optional[Any]       =  (1, num_labels)

 snake_case__			:				Optional[Any]       =  "huggingface/label-files"
 snake_case__			:				List[Any]       =  num_labels
 snake_case__			:				Tuple       =  json.load(open(cached_download(hf_hub_url(A__					,  A__					,  repo_type="dataset"       )       )					,  "r"       )       )
 snake_case__			:				List[Any]       =  {int(A__       ): v for k, v in idalabel.items()}

 snake_case__			:				Tuple       =  idalabel
 snake_case__			:				Optional[int]       =  {v: k for k, v in idalabel.items()}

 snake_case__			:				str       =  partial(A__					,  num_labels=A__					,  idalabel=A__					,  labelaid=A__       )

 snake_case__			:				List[str]       =  {
     "regnet-x-002": ImageNetPreTrainedConfig(
         depths=[1, 1, 4, 7]					,  hidden_sizes=[24, 56, 152, 368]					,  groups_width=8					,  layer_type="x"       ),
     "regnet-x-004": ImageNetPreTrainedConfig(
         depths=[1, 2, 7, 12]					,  hidden_sizes=[32, 64, 160, 384]					,  groups_width=16					,  layer_type="x"       ),
     "regnet-x-006": ImageNetPreTrainedConfig(
         depths=[1, 3, 5, 7]					,  hidden_sizes=[48, 96, 240, 528]					,  groups_width=24					,  layer_type="x"       ),
     "regnet-x-008": ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 5]					,  hidden_sizes=[64, 128, 288, 672]					,  groups_width=16					,  layer_type="x"       ),
     "regnet-x-016": ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 2]					,  hidden_sizes=[72, 168, 408, 912]					,  groups_width=24					,  layer_type="x"       ),
     "regnet-x-032": ImageNetPreTrainedConfig(
         depths=[2, 6, 15, 2]					,  hidden_sizes=[96, 192, 432, 1008]					,  groups_width=48					,  layer_type="x"       ),
     "regnet-x-040": ImageNetPreTrainedConfig(
         depths=[2, 5, 14, 2]					,  hidden_sizes=[80, 240, 560, 1360]					,  groups_width=40					,  layer_type="x"       ),
     "regnet-x-064": ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]					,  hidden_sizes=[168, 392, 784, 1624]					,  groups_width=56					,  layer_type="x"       ),
     "regnet-x-080": ImageNetPreTrainedConfig(
         depths=[2, 5, 15, 1]					,  hidden_sizes=[80, 240, 720, 1920]					,  groups_width=120					,  layer_type="x"       ),
     "regnet-x-120": ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]					,  hidden_sizes=[224, 448, 896, 2240]					,  groups_width=112					,  layer_type="x"       ),
     "regnet-x-160": ImageNetPreTrainedConfig(
         depths=[2, 6, 13, 1]					,  hidden_sizes=[256, 512, 896, 2048]					,  groups_width=128					,  layer_type="x"       ),
     "regnet-x-320": ImageNetPreTrainedConfig(
         depths=[2, 7, 13, 1]					,  hidden_sizes=[336, 672, 1344, 2520]					,  groups_width=168					,  layer_type="x"       ),
     # y variant
     "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]					,  hidden_sizes=[24, 56, 152, 368]					,  groups_width=8       ),
     "regnet-y-004": ImageNetPreTrainedConfig(
         depths=[1, 3, 6, 6]					,  hidden_sizes=[48, 104, 208, 440]					,  groups_width=8       ),
     "regnet-y-006": ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 4]					,  hidden_sizes=[48, 112, 256, 608]					,  groups_width=16       ),
     "regnet-y-008": ImageNetPreTrainedConfig(
         depths=[1, 3, 8, 2]					,  hidden_sizes=[64, 128, 320, 768]					,  groups_width=16       ),
     "regnet-y-016": ImageNetPreTrainedConfig(
         depths=[2, 6, 17, 2]					,  hidden_sizes=[48, 120, 336, 888]					,  groups_width=24       ),
     "regnet-y-032": ImageNetPreTrainedConfig(
         depths=[2, 5, 13, 1]					,  hidden_sizes=[72, 216, 576, 1512]					,  groups_width=24       ),
     "regnet-y-040": ImageNetPreTrainedConfig(
         depths=[2, 6, 12, 2]					,  hidden_sizes=[128, 192, 512, 1088]					,  groups_width=64       ),
     "regnet-y-064": ImageNetPreTrainedConfig(
         depths=[2, 7, 14, 2]					,  hidden_sizes=[144, 288, 576, 1296]					,  groups_width=72       ),
     "regnet-y-080": ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]					,  hidden_sizes=[168, 448, 896, 2016]					,  groups_width=56       ),
     "regnet-y-120": ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]					,  hidden_sizes=[224, 448, 896, 2240]					,  groups_width=112       ),
     "regnet-y-160": ImageNetPreTrainedConfig(
         depths=[2, 4, 11, 1]					,  hidden_sizes=[224, 448, 1232, 3024]					,  groups_width=112       ),
     "regnet-y-320": ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]					,  hidden_sizes=[232, 696, 1392, 3712]					,  groups_width=232       ),
     # models created by SEER -> https://arxiv.org/abs/2202.08360
     "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]					,  hidden_sizes=[232, 696, 1392, 3712]					,  groups_width=232       ),
     "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]					,  hidden_sizes=[328, 984, 1968, 4920]					,  groups_width=328       ),
     "regnet-y-1280-seer": RegNetConfig(
         depths=[2, 7, 17, 1]					,  hidden_sizes=[528, 1056, 2904, 7392]					,  groups_width=264       ),
     "regnet-y-2560-seer": RegNetConfig(
         depths=[3, 7, 16, 1]					,  hidden_sizes=[640, 1696, 2544, 5088]					,  groups_width=640       ),
     "regnet-y-10b-seer": ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]					,  hidden_sizes=[2020, 4040, 11110, 28280]					,  groups_width=1010       ),
     # finetuned on imagenet
     "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]					,  hidden_sizes=[232, 696, 1392, 3712]					,  groups_width=232       ),
     "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]					,  hidden_sizes=[328, 984, 1968, 4920]					,  groups_width=328       ),
     "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]					,  hidden_sizes=[528, 1056, 2904, 7392]					,  groups_width=264       ),
     "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
         depths=[3, 7, 16, 1]					,  hidden_sizes=[640, 1696, 2544, 5088]					,  groups_width=640       ),
     "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]					,  hidden_sizes=[2020, 4040, 11110, 28280]					,  groups_width=1010       ),
 }

 snake_case__			:				str       =  NameToOurModelFuncMap()
 snake_case__			:				Tuple       =  NameToFromModelFuncMap()
 # add seer weights logic

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

 # pretrained
 snake_case__			:				List[str]       =  partial(
     A__					,  "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"					,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()       )					,  )

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

 snake_case__			:				Optional[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()       )					,  )

 snake_case__			:				str       =  partial(
     A__					,  "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"					,  lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27					,  group_width=1010					,  w_a=1744					,  w_a=620.83					,  w_m=2.52       )       )       )					,  )

 # IN1K finetuned
 snake_case__			:				Optional[Any]       =  partial(
     A__					,  "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"					,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()       )					,  )

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

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

 snake_case__			:				Optional[int]       =  partial(
     A__					,  "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"					,  lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27					,  group_width=1010					,  w_a=1744					,  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__":
 __lowerCamelCase :      Optional[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 :      List[str]										=				parser.parse_args()

 __lowerCamelCase :      List[Any]										=				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)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Any										=				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[Any]										=				[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



