

"""simple docstring"""





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_a       =							logging.get_logger()




@dataclass
class 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	4_2
 __UpperCAmelCase	:					Optional[int]   		=	field(default_factory=lowercase   )
 __UpperCAmelCase	:					Dict   		=	field(default_factory=lowercase   )


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						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							:			List[str],					UpperCAmelCase__							:			Optional[int]	):
    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							:			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 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	4_2
 __UpperCAmelCase	:					List[str]   		=	4_2
 __UpperCAmelCase	:					Dict   		=	0
 __UpperCAmelCase	:					Union[str, Any]   		=	field(default_factory=lowercase   )
 __UpperCAmelCase	:					int   		=	field(default_factory=lowercase   )


 def __call__(			self							:			Tuple,					UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						Tracker(self.dest	)(UpperCAmelCase__	).parametrized
    __lowercase								=						Tracker(self.src	)(UpperCAmelCase__	).parametrized

    __lowercase								=						list(filter(lambda UpperCAmelCase__	: type(UpperCAmelCase__	) not in self.src_skip,					UpperCAmelCase__	)	)
    __lowercase								=						list(filter(lambda UpperCAmelCase__	: type(UpperCAmelCase__	) not in self.dest_skip,					UpperCAmelCase__	)	)

    if len(UpperCAmelCase__	) != len(UpperCAmelCase__	):
       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}"""	)







def   _A  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       List[Any],			UpperCamelCase_     :       str,			UpperCamelCase_     :       List[Any] = True)	->						Union[str, Any]:
   '''simple docstring'''


   print(F"""Converting {name}...""")
   with torch.no_grad():
      __lowercase								=						timm.create_model(lowercase__,			pretrained=lowercase__).eval()
      __lowercase								=						ResNetForImageClassification(lowercase__).eval()
      __lowercase								=						ModuleTransfer(src=lowercase__,			dest=lowercase__)
      __lowercase								=						torch.randn((1, 3, 224, 224))
      module_transfer(lowercase__)

   assert torch.allclose(from_model(lowercase__),			our_model(lowercase__).logits), "The model logits don't match the original one."

   __lowercase								=						F"""resnet{"-".join(name.split("resnet"))}"""
   print(lowercase__)

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name,			commit_message="Add model",			use_temp_dir=lowercase__,			)

      # we can use the convnext one
      __lowercase								=						AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name,			commit_message="Add image processor",			use_temp_dir=lowercase__,			)

      print(F"""Pushed {checkpoint_name}""")




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       int = None,			UpperCamelCase_     :       str = True)	->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						'imagenet-1k-id2label.json'
   __lowercase								=						1000
   __lowercase								=						(1, num_labels)

   __lowercase								=						'huggingface/label-files'
   __lowercase								=						num_labels
   __lowercase								=						json.load(open(hf_hub_download(lowercase__,			lowercase__,			repo_type="dataset"),			"r"))
   __lowercase								=						{int(lowercase__): v for k, v in idalabel.items()}

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

   __lowercase								=						partial(lowercase__,			num_labels=lowercase__,			idalabel=lowercase__,			labelaid=lowercase__)

   __lowercase								=						{
       'resnet18': ImageNetPreTrainedConfig(
           depths=[2, 2, 2, 2],			hidden_sizes=[64, 128, 256, 512],			layer_type="basic"),
       'resnet26': ImageNetPreTrainedConfig(
           depths=[2, 2, 2, 2],			hidden_sizes=[256, 512, 1024, 2048],			layer_type="bottleneck"),
       'resnet34': ImageNetPreTrainedConfig(
           depths=[3, 4, 6, 3],			hidden_sizes=[64, 128, 256, 512],			layer_type="basic"),
       'resnet50': ImageNetPreTrainedConfig(
           depths=[3, 4, 6, 3],			hidden_sizes=[256, 512, 1024, 2048],			layer_type="bottleneck"),
       'resnet101': ImageNetPreTrainedConfig(
           depths=[3, 4, 23, 3],			hidden_sizes=[256, 512, 1024, 2048],			layer_type="bottleneck"),
       'resnet152': ImageNetPreTrainedConfig(
           depths=[3, 8, 36, 3],			hidden_sizes=[256, 512, 1024, 2048],			layer_type="bottleneck"),
   }

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


if __name__ == "__main__":
   _a       =							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 resnet* architecture,'
           ' currently: resnet18,26,34,50,101,152. 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       =							parser.parse_args()
   _a       =							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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""ctrl"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(  self  ,      lowercase=246534  ,      lowercase=256  ,      lowercase=1280  ,      lowercase=8192  ,      lowercase=48  ,      lowercase=16  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=1E-6  ,      lowercase=0.02  ,      lowercase=True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    vocab_size
							_lowerCamelCase     :			Dict    =    n_positions
							_lowerCamelCase     :			Optional[int]    =    n_embd
							_lowerCamelCase     :			str    =    n_layer
							_lowerCamelCase     :			Union[str, Any]    =    n_head
							_lowerCamelCase     :			Any    =    dff
							_lowerCamelCase     :			int    =    resid_pdrop
							_lowerCamelCase     :			Dict    =    embd_pdrop
							_lowerCamelCase     :			Union[str, Any]    =    layer_norm_epsilon
							_lowerCamelCase     :			Tuple    =    initializer_range

							_lowerCamelCase     :			str    =    use_cache

							super().__init__(**lowercase  )