"""simple docstring"""






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


__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)

__lowerCAmelCase					: List[Any]        ={
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     int													=			'instructblip_vision_model'

      def __init__(		self	, __lowerCAmelCase=1408	, __lowerCAmelCase=6144	, __lowerCAmelCase=39	, __lowerCAmelCase=16	, __lowerCAmelCase=224	, __lowerCAmelCase=14	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=1E-6	, __lowerCAmelCase=0.0	, __lowerCAmelCase=1E-10	, __lowerCAmelCase=True	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)

            lowercase														=			hidden_size
            lowercase														=			intermediate_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			patch_size
            lowercase														=			image_size
            lowercase														=			initializer_range
            lowercase														=			attention_dropout
            lowercase														=			layer_norm_eps
            lowercase														=			hidden_act
            lowercase														=			qkv_bias







      @classmethod
      def        A__ (		cls	, __lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            cls._set_token_in_kwargs(__lowerCAmelCase		)

            lowercase							,							lowercase														=			cls.get_config_dict(__lowerCAmelCase	, **__lowerCAmelCase		)

            # get the vision config dict if we are loading from InstructBlipConfig
            if config_dict.get("""model_type"""		) == "instructblip":
                  lowercase														=			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 				_A		( lowerCAmelCase			):
      snake_case__    :     Tuple													=			'instructblip_qformer'

      def __init__(		self	, __lowerCAmelCase=3_0522	, __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	, ):







            """simple docstring"""



            super().__init__(pad_token_id=__lowerCAmelCase	, **__lowerCAmelCase		)

            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			hidden_act
            lowercase														=			intermediate_size
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			initializer_range
            lowercase														=			layer_norm_eps
            lowercase														=			position_embedding_type
            lowercase														=			cross_attention_frequency
            lowercase														=			encoder_hidden_size







      @classmethod
      def        A__ (		cls	, __lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            cls._set_token_in_kwargs(__lowerCAmelCase		)

            lowercase							,							lowercase														=			cls.get_config_dict(__lowerCAmelCase	, **__lowerCAmelCase		)

            # get the qformer config dict if we are loading from InstructBlipConfig
            if config_dict.get("""model_type"""		) == "instructblip":
                  lowercase														=			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 				_A		( lowerCAmelCase			):
      snake_case__    :     Dict													=			'instructblip'
      snake_case__    :     List[Any]													=			True

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=32	, **__lowerCAmelCase		):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)

            if vision_config is None:
                  lowercase														=			{}
                  logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""		)

            if qformer_config is None:
                  lowercase														=			{}
                  logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""		)

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

            lowercase														=			InstructBlipVisionConfig(**__lowerCAmelCase		)
            lowercase														=			InstructBlipQFormerConfig(**__lowerCAmelCase		)
            lowercase														=			text_config["""model_type"""] if """model_type""" in text_config else """opt"""
            lowercase														=			CONFIG_MAPPING[text_model_type](**__lowerCAmelCase		)

            lowercase														=			self.text_config.tie_word_embeddings
            lowercase														=			self.text_config.is_encoder_decoder

            lowercase														=			num_query_tokens
            lowercase														=			self.vision_config.hidden_size
            lowercase														=			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
            lowercase														=			1.0
            lowercase														=			0.0_2

      @classmethod
      def        A__ (		cls	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase	, ):







            """simple docstring"""



            return cls(
                vision_config=vision_config.to_dict()	, qformer_config=qformer_config.to_dict()	, text_config=text_config.to_dict()	, **__lowerCAmelCase	, )







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			copy.deepcopy(self.__dict__		)
            lowercase														=			self.vision_config.to_dict()
            lowercase														=			self.qformer_config.to_dict()
            lowercase														=			self.text_config.to_dict()
            lowercase														=			self.__class__.model_type
            return output




"""simple docstring"""






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()
__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[int]	)				->					Any:
      '''simple docstring'''


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

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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			OrderedDict()
      lowercase							,							lowercase														=			0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""	):
                  lowercase														=			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
                  lowercase														=			key[: key.find("""proj"""	)]
                  lowercase														=			key.replace(lowerCAmelCase__      ,	f'patch_embeddings.{total_embed_found}.'	)
                  lowercase														=			key.replace("""proj"""      ,	"""projection"""	)
                  if key.endswith("""bias"""	):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  lowercase														=			"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc1"""      ,	"""output.conv1"""	)
            if "mlp.fc2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc2"""      ,	"""output.conv2"""	)
            if "norm1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm1"""      ,	"""before_norm"""	)
            if "norm2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm2"""      ,	"""after_norm"""	)
            if "layer_scale_1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_1"""      ,	"""layer_scale_1"""	)
            if "layer_scale_2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_2"""      ,	"""layer_scale_2"""	)
            if "head" in key:
                  lowercase														=			key.replace("""head"""      ,	"""classifier"""	)
            lowercase														=			value
      return new_state_dict


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


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

      return image


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			PoolFormerConfig()

      # set attributes based on model_name
      lowercase														=			"""huggingface/label-files"""
      lowercase														=			model_name[-3:]
      lowercase														=			1_0_0_0
      lowercase														=			"""imagenet-1k-id2label.json"""
      lowercase														=			(1, 1_0_0_0)

      # set config attributes
      lowercase														=			json.load(open(hf_hub_download(lowerCAmelCase__      ,	lowerCAmelCase__      ,	repo_type="""dataset"""	)      ,	"""r"""	)	)
      lowercase														=			{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
      lowercase														=			idalabel
      lowercase														=			{v: k for k, v in idalabel.items()}
      if size == "s12":
            lowercase														=			[2, 2, 6, 2]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s24":
            lowercase														=			[4, 4, 1_2, 4]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.9
      elif size == "m36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      elif size == "m48":
            lowercase														=			[8, 8, 2_4, 8]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      else:
            raise ValueError(f'Size {size} not supported'	)

      # load image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)

      # Prepare image
      lowercase														=			prepare_img()
      lowercase														=			image_processor(images=lowerCAmelCase__      ,	return_tensors="""pt"""	).pixel_values

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

      # load original state dict
      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location=torch.device("""cpu"""	)	)

      # rename keys
      lowercase														=			rename_keys(lowerCAmelCase__	)

      # create HuggingFace model and load state dict
      lowercase														=			PoolFormerForImageClassification(lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

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

      # forward pass
      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowercase														=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            lowercase														=			torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            lowercase														=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            lowercase														=			torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            lowercase														=			torch.tensor([0.1_167, -0.0_656, -0.3_423]	)
      else:
            raise ValueError(f'Size {size} not supported'	)

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

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


if __name__ == "__main__":
       __lowerCAmelCase					: List[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."""
       )
       __lowerCAmelCase					: int        =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



