import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip_text_model"""

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str=3_05_24  ,				__SCREAMING_SNAKE_CASE			:   Any=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Tuple=30_72  ,				__SCREAMING_SNAKE_CASE			:   Any=7_68  ,				__SCREAMING_SNAKE_CASE			:   Tuple=12  ,				__SCREAMING_SNAKE_CASE			:   Dict=8  ,				__SCREAMING_SNAKE_CASE			:   int=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   int=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.0  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.0  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=3_05_22  ,				__SCREAMING_SNAKE_CASE			:   Any=2  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1_02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   super().__init__(
       pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				sep_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	encoder_hidden_size
   __a							=	intermediate_size
   __a							=	projection_dim
   __a							=	hidden_dropout_prob
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	max_position_embeddings
   __a							=	layer_norm_eps
   __a							=	hidden_act
   __a							=	initializer_range
   __a							=	attention_probs_dropout_prob
   __a							=	is_decoder
   __a							=	use_cache

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the text config dict if we are loading from BlipConfig
   if config_dict.get("model_type"							) == "blip":
    __a							=	config_dict["text_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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip_vision_model"""

  def __init__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str=7_68  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=30_72  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=3_84  ,				__SCREAMING_SNAKE_CASE			:   str=16  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-10  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	projection_dim
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	patch_size
   __a							=	image_size
   __a							=	initializer_range
   __a							=	attention_dropout
   __a							=	layer_norm_eps
   __a							=	hidden_act

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the vision config dict if we are loading from BlipConfig
   if config_dict.get("model_type"							) == "blip":
    __a							=	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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   int=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Any=2_56  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   if text_config is None:
    __a							=	{}
    logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."							)

   if vision_config is None:
    __a							=	{}
    logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."							)

   __a							=	BlipTextConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	BlipVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	self.vision_config.hidden_size

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	1.0
   __a							=	0.02
   __a							=	image_text_hidden_size

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Any  ,				__SCREAMING_SNAKE_CASE			:   BlipTextConfig  ,				__SCREAMING_SNAKE_CASE			:   BlipVisionConfig  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   return cls(text_config=text_config.to_dict()  ,				vision_config=vision_config.to_dict()  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.text_config.to_dict()
   __a							=	self.vision_config.to_dict()
   __a							=	self.__class__.model_type
   return output



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)



def 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	original_name.split("."       )[0]
 __a							=	key.split("."       )
 __a							=	int(key_list[key_list.index(_A       ) - 2]       )
 __a							=	int(key_list[key_list.index(_A       ) - 1]       )
 __a							=	orig_block_num - offset

 __a							=	key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,					f"""block.{new_block_num}.{layer_num}.{new_name}"""       )
 return key



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	OrderedDict()
 __a							,  __a							=	0, 0
 for key, value in state_dict.items():
  if key.startswith("network"       ):
   __a							=	key.replace("network"				,					"poolformer.encoder"       )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith("bias"       ) and "patch_embed" not in key:
    patch_emb_offset += 1
   __a							=	key[: key.find("proj"       )]
   __a							=	key.replace(_A				,					f"""patch_embeddings.{total_embed_found}."""       )
   __a							=	key.replace("proj"				,					"projection"       )
   if key.endswith("bias"       ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   __a							=	"poolformer.encoder." + key
  if "mlp.fc1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"mlp.fc1"				,					"output.conv1"       )
  if "mlp.fc2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"mlp.fc2"				,					"output.conv2"       )
  if "norm1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"norm1"				,					"before_norm"       )
  if "norm2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"norm2"				,					"after_norm"       )
  if "layer_scale_1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"layer_scale_1"				,					"layer_scale_1"       )
  if "layer_scale_2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"layer_scale_2"				,					"layer_scale_2"       )
  if "head" in key:
   __a							=	key.replace("head"				,					"classifier"       )
  __a							=	value
 return new_state_dict



def 	__A		(      ):



 """simple docstring"""


 __a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __a							=	Image.open(requests.get(_A				,					stream=_A       ).raw       )

 return image



@torch.no_grad()
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	PoolFormerConfig()

 # set attributes based on model_name
 __a							=	"huggingface/label-files"
 __a							=	model_name[-3:]
 __a							=	1000
 __a							=	"imagenet-1k-id2label.json"
 __a							=	(1, 1000)

 # set config attributes
 __a							=	json.load(open(hf_hub_download(_A				,					_A				,					repo_type="dataset"       )				,					"r"       )       )
 __a							=	{int(_A       ): v for k, v in idalabel.items()}
 __a							=	idalabel
 __a							=	{v: k for k, v in idalabel.items()}
 if size == "s12":
  __a							=	[2, 2, 6, 2]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	0.9
 elif size == "s24":
  __a							=	[4, 4, 12, 4]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	0.9
 elif size == "s36":
  __a							=	[6, 6, 18, 6]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.9
 elif size == "m36":
  __a							=	[6, 6, 18, 6]
  __a							=	[96, 192, 384, 768]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.95
 elif size == "m48":
  __a							=	[8, 8, 24, 8]
  __a							=	[96, 192, 384, 768]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.95
 else:
  raise ValueError(f"""Size {size} not supported"""       )

 # load image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )

 # Prepare image
 __a							=	prepare_img()
 __a							=	image_processor(images=_A				,					return_tensors="pt"       ).pixel_values

 logger.info(f"""Converting model {model_name}..."""       )

 # load original state dict
 __a							=	torch.load(_A				,					map_location=torch.device("cpu"       )       )

 # rename keys
 __a							=	rename_keys(_A       )

 # create HuggingFace model and load state dict
 __a							=	PoolFormerForImageClassification(_A       )
 model.load_state_dict(_A       )
 model.eval()

 # Define image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )
 __a							=	image_processor(images=prepare_img()				,					return_tensors="pt"       ).pixel_values

 # forward pass
 __a							=	model(_A       )
 __a							=	outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  __a							=	torch.tensor([-0.3045, -0.6758, -0.4869]       )
 elif size == "s24":
  __a							=	torch.tensor([0.4402, -0.1374, -0.8045]       )
 elif size == "s36":
  __a							=	torch.tensor([-0.6080, -0.5133, -0.5898]       )
 elif size == "m36":
  __a							=	torch.tensor([0.3952, 0.2263, -1.2668]       )
 elif size == "m48":
  __a							=	torch.tensor([0.1167, -0.0656, -0.3423]       )
 else:
  raise ValueError(f"""Size {size} not supported"""       )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]				,					_A				,					atol=1E-2       )

 # finally, save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
 Path(_A       ).mkdir(exist_ok=_A       )
 model.save_pretrained(_A       )
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
 image_processor.save_pretrained(_A       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""poolformer_s12""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


