






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__		:							Dict 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							str 				=				{
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'blip_2_vision_model'




     def __init__(				self	,						_lowerCAmelCase=1408	,						_lowerCAmelCase=6144	,						_lowerCAmelCase=39	,						_lowerCAmelCase=16	,						_lowerCAmelCase=224	,						_lowerCAmelCase=14	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.0_0_0_0_1	,						_lowerCAmelCase=0.0	,						_lowerCAmelCase=1e-10	,						_lowerCAmelCase=True	,						**_lowerCAmelCase	,						):
          super().__init__(**_lowerCAmelCase		)

          UpperCAmelCase__      :  Tuple				      =			hidden_size
          UpperCAmelCase__      :  int				      =			intermediate_size
          UpperCAmelCase__      :  Optional[int]				      =			num_hidden_layers
          UpperCAmelCase__      :  Union[str, Any]				      =			num_attention_heads
          UpperCAmelCase__      :  Union[str, Any]				      =			patch_size
          UpperCAmelCase__      :  Dict				      =			image_size
          UpperCAmelCase__      :  Optional[int]				      =			initializer_range
          UpperCAmelCase__      :  Tuple				      =			attention_dropout
          UpperCAmelCase__      :  Dict				      =			layer_norm_eps
          UpperCAmelCase__      :  str				      =			hidden_act
          UpperCAmelCase__      :  Tuple				      =			qkv_bias




     @classmethod
     def   __UpperCAmelCase  (				cls	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          cls._set_token_in_kwargs(_lowerCAmelCase		)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  str				      =			cls.get_config_dict(_lowerCAmelCase	,						**_lowerCAmelCase		)

          # get the vision config dict if we are loading from Blip2Config
          if config_dict.get("""model_type"""		) == "blip-2":
               UpperCAmelCase__      :  List[Any]				      =			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(_lowerCAmelCase	,						**_lowerCAmelCase		)







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'blip_2_qformer'




     def __init__(				self	,						_lowerCAmelCase=30522	,						_lowerCAmelCase=768	,						_lowerCAmelCase=12	,						_lowerCAmelCase=12	,						_lowerCAmelCase=3072	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-12	,						_lowerCAmelCase=0	,						_lowerCAmelCase="absolute"	,						_lowerCAmelCase=2	,						_lowerCAmelCase=1408	,						**_lowerCAmelCase	,						):
          super().__init__(pad_token_id=_lowerCAmelCase	,						**_lowerCAmelCase		)

          UpperCAmelCase__      :  Any				      =			vocab_size
          UpperCAmelCase__      :  Optional[int]				      =			hidden_size
          UpperCAmelCase__      :  Optional[int]				      =			num_hidden_layers
          UpperCAmelCase__      :  str				      =			num_attention_heads
          UpperCAmelCase__      :  Optional[Any]				      =			hidden_act
          UpperCAmelCase__      :  int				      =			intermediate_size
          UpperCAmelCase__      :  int				      =			hidden_dropout_prob
          UpperCAmelCase__      :  List[Any]				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  int				      =			max_position_embeddings
          UpperCAmelCase__      :  Optional[Any]				      =			initializer_range
          UpperCAmelCase__      :  Optional[int]				      =			layer_norm_eps
          UpperCAmelCase__      :  Dict				      =			position_embedding_type
          UpperCAmelCase__      :  Tuple				      =			cross_attention_frequency
          UpperCAmelCase__      :  Optional[Any]				      =			encoder_hidden_size




     @classmethod
     def   __UpperCAmelCase  (				cls	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          cls._set_token_in_kwargs(_lowerCAmelCase		)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			cls.get_config_dict(_lowerCAmelCase	,						**_lowerCAmelCase		)

          # get the qformer config dict if we are loading from Blip2Config
          if config_dict.get("""model_type"""		) == "blip-2":
               UpperCAmelCase__      :  Any				      =			config_dict["""qformer_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(_lowerCAmelCase	,						**_lowerCAmelCase		)







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'blip-2'
     __lowerCamelCase      				=  True




     def __init__(				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=32	,						**_lowerCAmelCase		):
          super().__init__(**_lowerCAmelCase		)

          if vision_config is None:
               UpperCAmelCase__      :  int				      =			{}
               logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""		)

          if qformer_config is None:
               UpperCAmelCase__      :  str				      =			{}
               logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""		)

          if text_config is None:
               UpperCAmelCase__      :  Dict				      =			{}
               logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""		)

          UpperCAmelCase__      :  Any				      =			BlipaVisionConfig(**_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			BlipaQFormerConfig(**_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			text_config["""model_type"""] if """model_type""" in text_config else """opt"""
          UpperCAmelCase__      :  Optional[Any]				      =			CONFIG_MAPPING[text_model_type](**_lowerCAmelCase		)

          UpperCAmelCase__      :  Tuple				      =			self.text_config.tie_word_embeddings
          UpperCAmelCase__      :  str				      =			self.text_config.is_encoder_decoder

          UpperCAmelCase__      :  Optional[Any]				      =			num_query_tokens
          UpperCAmelCase__      :  Optional[Any]				      =			self.vision_config.hidden_size
          UpperCAmelCase__      :  Optional[Any]				      =			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          UpperCAmelCase__      :  List[Any]				      =			1.0
          UpperCAmelCase__      :  Optional[Any]				      =			0.0_2




     @classmethod
     def   __UpperCAmelCase  (				cls	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase	,						):
          return cls(
              vision_config=vision_config.to_dict()	,						qformer_config=qformer_config.to_dict()	,						text_config=text_config.to_dict()	,						**_lowerCAmelCase	,						)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  str				      =			copy.deepcopy(self.__dict__		)
          UpperCAmelCase__      :  str				      =			self.vision_config.to_dict()
          UpperCAmelCase__      :  Dict				      =			self.qformer_config.to_dict()
          UpperCAmelCase__      :  List[Any]				      =			self.text_config.to_dict()
          UpperCAmelCase__      :  List[Any]				      =			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					:			List[Any]							=    logging.get_logger()



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			List[nn.Module]     	= field(default_factory=a			)
  __lowerCAmelCase       :			list     	= field(default_factory=a			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						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 ,			a_  )				->					Union[str, Any]:


    """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     snake_case_      (  self  )				->					int:


    """simple docstring"""
    # 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 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			int     	= 0
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  def __call__(  self ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						Tracker(self.dest  )(a_  ).parametrized
    UpperCAmelCase    				=						Tracker(self.src  )(a_  ).parametrized

    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.src_skip ,			a_  )  )
    UpperCAmelCase    				=						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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : ResNetConfig							, SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : bool = True    ):
  print(f'''Converting {name}...'''    )
  with torch.no_grad():
    UpperCAmelCase    				=						timm.create_model(SCREAMING_SNAKE_CASE							, pretrained=SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ResNetForImageClassification(SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ModuleTransfer(src=SCREAMING_SNAKE_CASE							, dest=SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						torch.randn((1, 3, 224, 224)    )
    module_transfer(SCREAMING_SNAKE_CASE    )

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

  UpperCAmelCase    				=						f'''resnet{'-'.join(name.split('resnet'    )    )}'''
  print(SCREAMING_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=SCREAMING_SNAKE_CASE							, )

    # we can use the convnext one
    UpperCAmelCase    				=						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=SCREAMING_SNAKE_CASE							, )

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : str = None							, SCREAMING_SNAKE_CASE   : bool = True    ):
  UpperCAmelCase    				=						'imagenet-1k-id2label.json'
  UpperCAmelCase    				=						1000
  UpperCAmelCase    				=						(1, num_labels)

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

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

  UpperCAmelCase    				=						partial(SCREAMING_SNAKE_CASE							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid=SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						{
      '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(SCREAMING_SNAKE_CASE							, names_to_config[model_name]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  return config, expected_shape


if __name__ == "__main__":
  _a					:			List[str]							=    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					:			Tuple							=    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)


