




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}






class  lowercase(					UpperCAmelCase_			):







 '''simple docstring'''





 lowercase__			 = """git_vision_model"""


 def __init__(    self:		Optional[int],				a_:		Dict=768,				a_:		Tuple=3_072,				a_:		List[str]=12,				a_:		int=12,				a_:		Any=3,				a_:		int=224,				a_:		Dict=16,				a_:		Optional[int]="quick_gelu",				a_:		Any=1E-5,				a_:		List[Any]=0.0,				a_:		Dict=0.02,				**a_:		int,				):




   '''simple docstring'''



   super().__init__(**__lowercase		)

   _snake_case							:		str								=  hidden_size
   _snake_case							:		List[str]								=  intermediate_size
   _snake_case							:		str								=  num_hidden_layers
   _snake_case							:		Any								=  num_attention_heads
   _snake_case							:		int								=  num_channels
   _snake_case							:		Optional[int]								=  patch_size
   _snake_case							:		Any								=  image_size
   _snake_case							:		List[str]								=  initializer_range
   _snake_case							:		Tuple								=  attention_dropout
   _snake_case							:		str								=  layer_norm_eps
   _snake_case							:		Union[str, Any]								=  hidden_act


 @classmethod
 def 			UpperCamelCase_       (    cls:		Any,				a_:		Tuple,				**a_:		Optional[Any]		):




   '''simple docstring'''



   cls._set_token_in_kwargs(__lowercase		)

   _snake_case							:		Optional[int]								=  cls.get_config_dict(__lowercase,				**__lowercase		)

   # get the vision config dict if we are loading from GITConfig
   if config_dict.get("""model_type"""		) == "git":
     _snake_case							:		Optional[int]								=  config_dict['''vision_config''']

   if "model_type" in config_dict and hasattr(cls,				"""model_type"""		) and config_dict["model_type"] != cls.model_type:
     logger.warning(
         f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
         f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."		)

   return cls.from_dict(__lowercase,				**__lowercase		)







class  lowercase(					UpperCAmelCase_			):







 '''simple docstring'''





 lowercase__			 = """git"""


 def __init__(    self:		int,				a_:		Union[str, Any]=None,				a_:		Any=30_522,				a_:		str=768,				a_:		Dict=6,				a_:		int=12,				a_:		int=3_072,				a_:		str="gelu",				a_:		Tuple=0.1,				a_:		List[str]=0.1,				a_:		Dict=1_024,				a_:		Dict=0.02,				a_:		Dict=1E-12,				a_:		Optional[Any]=0,				a_:		Dict="absolute",				a_:		Union[str, Any]=True,				a_:		Any=False,				a_:		Optional[Any]=101,				a_:		List[str]=102,				a_:		Optional[int]=None,				**a_:		int,				):




   '''simple docstring'''



   super().__init__(bos_token_id=__lowercase,				eos_token_id=__lowercase,				pad_token_id=__lowercase,				**__lowercase		)

   if vision_config is None:
     _snake_case							:		Union[str, Any]								=  {}
     logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""		)

   _snake_case							:		Optional[Any]								=  GitVisionConfig(**__lowercase		)
   _snake_case							:		Optional[int]								=  vocab_size
   _snake_case							:		List[str]								=  hidden_size
   _snake_case							:		int								=  num_hidden_layers
   _snake_case							:		int								=  num_attention_heads
   _snake_case							:		List[Any]								=  hidden_act
   _snake_case							:		List[str]								=  intermediate_size
   _snake_case							:		List[str]								=  hidden_dropout_prob
   _snake_case							:		List[Any]								=  attention_probs_dropout_prob
   _snake_case							:		Tuple								=  max_position_embeddings
   _snake_case							:		int								=  initializer_range
   _snake_case							:		Any								=  layer_norm_eps
   _snake_case							:		Optional[int]								=  position_embedding_type
   _snake_case							:		Union[str, Any]								=  use_cache
   _snake_case							:		str								=  tie_word_embeddings
   _snake_case							:		Optional[Any]								=  num_image_with_embedding

   _snake_case							:		str								=  bos_token_id
   _snake_case							:		Tuple								=  eos_token_id


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		Optional[int]								=  self.vision_config.to_dict()
   _snake_case							:		str								=  self.__class__.model_type
   return output








"""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  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Union[str, Any],				a_:		Tensor,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  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 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



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







@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = 0
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def __call__(    self:		Dict,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  Tracker(self.dest		)(a_		).parametrized
   _snake_case							:		int								=  Tracker(self.src		)(a_		).parametrized

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

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







def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: ResNetConfig		,       snake_case__							: Path		,       snake_case__							: bool = True   ):
  """simple docstring"""

  print(F"Converting {name}..."   )
  with torch.no_grad():
    _snake_case							:		Dict								=  timm.create_model(snake_case__		,       pretrained=snake_case__   ).eval()
    _snake_case							:		List[Any]								=  ResNetForImageClassification(snake_case__   ).eval()
    _snake_case							:		List[str]								=  ModuleTransfer(src=snake_case__		,       dest=snake_case__   )
    _snake_case							:		Optional[Any]								=  torch.randn((1, 3, 2_24, 2_24)   )
    module_transfer(snake_case__   )

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

  _snake_case							:		Optional[int]								=  F"resnet{'-'.join(name.split('resnet'   )   )}"
  print(snake_case__   )

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

    # we can use the convnext one
    _snake_case							:		Union[str, Any]								=  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=snake_case__		,       )

    print(F"Pushed {checkpoint_name}"   )


def  UpperCAmelCase__	(snake_case__							: Path		,       snake_case__							: str = None		,       snake_case__							: bool = True   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  """imagenet-1k-id2label.json"""
  _snake_case							:		Optional[Any]								=  10_00
  _snake_case							:		str								=  (1, num_labels)

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

  _snake_case							:		str								=  idalabel
  _snake_case							:		Union[str, Any]								=  {v: k for k, v in idalabel.items()}

  _snake_case							:		Tuple								=  partial(snake_case__		,       num_labels=snake_case__		,       idalabel=snake_case__		,       labelaid=snake_case__   )

  _snake_case							:		Optional[int]								=  {
      """resnet18""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet26""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet34""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet50""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet101""": ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet152""": ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
  }

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


