






'''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()
_A			:    List[Any]					  =logging.get_logger()

@dataclass
class _lowercase			:
       a          = 42
       a          = field(default_factory=_lowercase	)
       a          = field(default_factory=_lowercase	)




       def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tensor    ,							UpperCamelCase__:       Tensor  ):
              lowerCamelCase__      :			List[str] 						= 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[int]    ,							UpperCamelCase__:       Tensor  ):
              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 	lowerCamelCase_       (							self:       List[Any]  ):
              # 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 _lowercase			:
       a          = 42
       a          = 42
       a          = 1
       a          = field(default_factory=_lowercase	)
       a          = field(default_factory=_lowercase	)
       a          = True




       def __call__(							self:       Union[str, Any]    ,							UpperCamelCase__:       Tensor  ):
              lowerCamelCase__      :			Dict 						= Tracker(self.dest  )(UpperCamelCase__  ).parametrized
              lowerCamelCase__      :			str 						= Tracker(self.src  )(UpperCamelCase__  ).parametrized

              lowerCamelCase__      :			Any 						= list(filter(lambda UpperCamelCase__  : type(UpperCamelCase__  ) not in self.src_skip    ,							UpperCamelCase__  )  )
              lowerCamelCase__      :			Optional[int] 						= 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:       Optional[Any]    ,							UpperCamelCase__:       nn.Module  ):
              super().__init__()

              lowerCamelCase__      :			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}'''
                     lowerCamelCase__      :			Union[str, Any] 						= len(UpperCamelCase__  ) + 1
                     feature_blocks.append((F'''res{block_index}''', v)  )

              lowerCamelCase__      :			str 						= nn.ModuleDict(UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Tensor  ):
              return get_trunk_forward_outputs(
                  UpperCamelCase__    ,							out_feat_keys=UpperCamelCase__    ,							feature_blocks=self._feature_blocks    ,							)



class _lowercase			(       _lowercase	):




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




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

              else:
                     lowerCamelCase__      :			Dict 						= super().__getitem__(UpperCamelCase__  )

              return val



class _lowercase			(       _lowercase	):




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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> List[Any]:
       for from_key, to_key in keys:
              lowerCamelCase__      :			Union[str, Any] 						= from_state_dict[from_key].clone()
              print(f'''Copied key={from_key} to={to_key}'''   )
       return to_state_dict





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase = True       ,    )   -> Dict:
       print(f'''Converting {name}...'''   )
       with torch.no_grad():
              lowerCamelCase__      :			Union[str, Any] 						= from_model_func()
              lowerCamelCase__      :			Tuple 						= our_model_func(UpperCamelCase   ).eval()
              lowerCamelCase__      :			List[str] 						= ModuleTransfer(src=UpperCamelCase       ,    dest=UpperCamelCase       ,    raise_if_mismatch=UpperCamelCase   )
              lowerCamelCase__      :			Union[str, Any] 						= torch.randn((1, 3, 224, 224)   )
              module_transfer(UpperCamelCase   )

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

       lowerCamelCase__      :			int 						= our_model(UpperCamelCase       ,    output_hidden_states=UpperCamelCase   )
       lowerCamelCase__      :			List[str] 						= (
           our_outputs.logits if isinstance(UpperCamelCase       ,    UpperCamelCase   ) else our_outputs.last_hidden_state
       )

       lowerCamelCase__      :			List[Any] 						= from_model(UpperCamelCase   )
       lowerCamelCase__      :			Optional[Any] 						= 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:
              lowerCamelCase__      :			Union[str, Any] 						= 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       ,    )

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

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

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

       lowerCamelCase__      :			int 						= partial(UpperCamelCase       ,    num_labels=UpperCamelCase       ,    idalabel=UpperCamelCase       ,    labelaid=UpperCamelCase   )

       lowerCamelCase__      :			Any 						= {
           """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   ),
       }

       lowerCamelCase__      :			List[Any] 						= NameToOurModelFuncMap()
       lowerCamelCase__      :			Any 						= NameToFromModelFuncMap()
       # add seer weights logic

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

       # pretrained
       lowerCamelCase__      :			Union[str, Any] 						= partial(
           UpperCamelCase       ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""       ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )       ,    )

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

       lowerCamelCase__      :			Optional[int] 						= 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()   )       ,    )

       lowerCamelCase__      :			str 						= partial(
           UpperCamelCase       ,    """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
       lowerCamelCase__      :			List[str] 						= partial(
           UpperCamelCase       ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )       ,    )

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

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

       lowerCamelCase__      :			Union[str, Any] 						= partial(
           UpperCamelCase       ,    """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(
                  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__":
   _A			:    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.''',
   )

   _A			:    Optional[Any]					  =parser.parse_args()

   _A			:    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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

