





"""simple docstring"""

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def        __a							(			A ,	A )       ->						int:



   '''simple docstring'''



   A__											=					old_name

   if "patch_embed" in old_name:
      A__											=					old_name.split("." )

      if layer == "0":
         A__											=					old_name.replace("0" ,	"convolution1" )
      elif layer == "1":
         A__											=					old_name.replace("1" ,	"batchnorm_before" )
      elif layer == "3":
         A__											=					old_name.replace("3" ,	"convolution2" )
      else:
         A__											=					old_name.replace("4" ,	"batchnorm_after" )

   if "network" in old_name and re.search(R"\d\.\d" ,	__A ):
      A__											=					R'''\b\d{2}\b'''
      if bool(re.search(__A ,	__A ) ):
         A__											=					re.search(R"\d\.\d\d." ,	__A ).group()
      else:
         A__											=					re.search(R"\d\.\d." ,	__A ).group()
      if int(match[0] ) < 6:
         A__											=					old_name.replace(__A ,	"" )
         A__											=					trimmed_name.replace("network" ,	match[0] + ".meta4D_layers.blocks." + match[2:-1] )
         A__											=					'''intermediate_stages.''' + trimmed_name
      else:
         A__											=					old_name.replace(__A ,	"" )
         if int(match[2] ) < num_meta4D_last_stage:
            A__											=					trimmed_name.replace("network" ,	"meta4D_layers.blocks." + match[2] )
         else:
            A__											=					str(int(match[2] ) - num_meta4D_last_stage )
            A__											=					trimmed_name.replace("network" ,	"meta3D_layers.blocks." + layer_index )
            if "norm1" in old_name:
               A__											=					trimmed_name.replace("norm1" ,	"layernorm1" )
            elif "norm2" in old_name:
               A__											=					trimmed_name.replace("norm2" ,	"layernorm2" )
            elif "fc1" in old_name:
               A__											=					trimmed_name.replace("fc1" ,	"linear_in" )
            elif "fc2" in old_name:
               A__											=					trimmed_name.replace("fc2" ,	"linear_out" )

         A__											=					'''last_stage.''' + trimmed_name

   elif "network" in old_name and re.search(R".\d." ,	__A ):
      A__											=					old_name.replace("network" ,	"intermediate_stages" )

   if "fc" in new_name:
      A__											=					new_name.replace("fc" ,	"convolution" )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      A__											=					new_name.replace("norm1" ,	"batchnorm_before" )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      A__											=					new_name.replace("norm2" ,	"batchnorm_after" )
   if "proj" in new_name:
      A__											=					new_name.replace("proj" ,	"projection" )
   if "dist_head" in new_name:
      A__											=					new_name.replace("dist_head" ,	"distillation_classifier" )
   elif "head" in new_name:
      A__											=					new_name.replace("head" ,	"classifier" )
   elif "patch_embed" in new_name:
      A__											=					'''efficientformer.''' + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      A__											=					new_name.replace("norm" ,	"layernorm" )
      A__											=					'''efficientformer.''' + new_name
   else:
      A__											=					'''efficientformer.encoder.''' + new_name

   return new_name



def        __a							(			A ,	A )       ->						Dict:



   '''simple docstring'''



   for key in checkpoint.copy().keys():
      A__											=					checkpoint.pop(__A )
      A__											=					val

   return checkpoint



def        __a							(			)       ->						int:



   '''simple docstring'''



   A__											=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   A__											=					Image.open(requests.get(__A ,	stream=__A ).raw )

   return image



def        __a							(			A ,	A ,	A ,	A )       ->						int:



   '''simple docstring'''



   A__											=					torch.load(__A ,	map_location="cpu" )['''model''']
   A__											=					EfficientFormerConfig.from_json_file(__A )
   A__											=					EfficientFormerForImageClassificationWithTeacher(__A )
   A__											=					'''_'''.join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )

   A__											=					config.depths[-1] - config.num_metaad_blocks + 1
   A__											=					convert_torch_checkpoint(__A ,	__A )

   model.load_state_dict(__A )
   model.eval()

   A__											=					{
       '''bilinear''': PILImageResampling.BILINEAR,
       '''bicubic''': PILImageResampling.BICUBIC,
       '''nearest''': PILImageResampling.NEAREST,
   }

   # prepare image
   A__											=					prepare_img()
   A__											=					256
   A__											=					224
   A__											=					EfficientFormerImageProcessor(
       size={"shortest_edge": image_size} ,	crop_size={"height": crop_size, "width": crop_size} ,	resample=pillow_resamplings["bicubic"] ,	)
   A__											=					processor(images=__A ,	return_tensors="pt" ).pixel_values

   # original processing pipeline
   A__											=					Compose(
       [
           Resize(__A ,	interpolation=pillow_resamplings["bicubic"] ),
           CenterCrop(__A ),
           ToTensor(),
           Normalize(__A ,	__A ),
       ] )
   A__											=					image_transforms(__A ).unsqueeze(0 )

   assert torch.allclose(__A ,	__A )

   A__											=					model(__A )
   A__											=					outputs.logits

   A__											=					(1, 1_000)

   if "l1" in model_name:
      A__											=					torch.Tensor(
          [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28] )
      assert torch.allclose(logits[0, :10] ,	__A ,	atol=1E-3 )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      A__											=					torch.Tensor(
          [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27] )
      assert torch.allclose(logits[0, :10] ,	__A ,	atol=1E-3 )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      A__											=					torch.Tensor(
          [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78] )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )

   # Save Checkpoints
   Path(__A ).mkdir(exist_ok=__A )
   model.save_pretrained(__A )
   print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
   processor.save_pretrained(__A )
   print(f"""Processor successfuly saved at {pytorch_dump_path}""" )

   if push_to_hub:
      print("Pushing model to the hub..." )

      model.push_to_hub(
          repo_id=f"""Bearnardd/{pytorch_dump_path}""" ,	commit_message="Add model" ,	use_temp_dir=__A ,	)
      processor.push_to_hub(
          repo_id=f"""Bearnardd/{pytorch_dump_path}""" ,	commit_message="Add image processor" ,	use_temp_dir=__A ,	)


if __name__ == "__main__":
 __UpperCAmelCase        =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--pytorch_model_path""",
     default=None,
     type=str,
     required=True,
     help="""Path to EfficientFormer pytorch checkpoint.""",
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The json file for EfficientFormer model config.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )

 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
 parser.add_argument(
     """--no-push_to_hub""",
     dest="""push_to_hub""",
     action="""store_false""",
     help="""Do not push model and image processor to the hub""",
 )
 parser.set_defaults(push_to_hub=True)

 __UpperCAmelCase        =parser.parse_args()
 convert_efficientformer_checkpoint(
     checkpoint_path=args.pytorch_model_path,
     efficientformer_config_file=args.config_file,
     pytorch_dump_path=args.pytorch_dump_path,
     push_to_hub=args.push_to_hub,
 )







'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=6.0  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		Tuple="fp4"  ,   UpperCAmelCase      :		str=False  ,   **UpperCAmelCase      :		Union[str, Any]  ,   )	->	str:


       '''simple docstring'''

       lowercase       : Optional[int]						=load_in_abit
       lowercase       : Union[str, Any]						=load_in_abit
       lowercase       : Tuple						=llm_inta_threshold
       lowercase       : Optional[Any]						=llm_inta_skip_modules
       lowercase       : int						=llm_inta_enable_fpaa_cpu_offload
       lowercase       : Dict						=llm_inta_has_fpaa_weight
       lowercase       : str						=bnb_abit_quant_type
       lowercase       : int						=bnb_abit_use_double_quant

       if bnb_abit_compute_dtype is None:
        lowercase       : str						=torch.floataa
       elif isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
        lowercase       : Tuple						=getattr(UpperCAmelCase  ,   UpperCAmelCase		)
       elif isinstance(UpperCAmelCase  ,   torch.dtype		):
        lowercase       : Optional[int]						=bnb_abit_compute_dtype
       else:
        raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype'''		)

       self.post_init()






      def 					A__   (			self      :		List[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       if not isinstance(self.llm_inta_threshold  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_threshold must be a float'''		)

       if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_skip_modules must be a list of strings'''		)
       if not isinstance(self.llm_inta_enable_fpaa_cpu_offload  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean'''		)

       if not isinstance(self.llm_inta_has_fpaa_weight  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_has_fp16_weight must be a boolean'''		)

       if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype  ,   torch.dtype		):
        raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype'''		)

       if not isinstance(self.bnb_abit_quant_type  ,   UpperCAmelCase		):
        raise ValueError('''bnb_4bit_quant_type must be a string'''		)

       if not isinstance(self.bnb_abit_use_double_quant  ,   UpperCAmelCase		):
        raise ValueError('''bnb_4bit_use_double_quant must be a boolean'''		)

       if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes'''		)		) >= version.parse(
           '''0.39.0'''		):
        raise ValueError(
            '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'''		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       return self.load_in_abit or self.load_in_abit






      def 					A__   (			self      :		Tuple		)	->	Any:


       '''simple docstring'''

       if self.load_in_abit:
        return "llm_int8"
       elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
        return "fp4"
       elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
        return "nf4"
       else:
        return None






      @classmethod
      def 					A__   (			cls      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Any  ,   **UpperCAmelCase      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Dict						=cls(**UpperCAmelCase		)

       lowercase       : Dict						=[]
       for key, value in kwargs.items():
        if hasattr(UpperCAmelCase  ,   UpperCAmelCase		):
         setattr(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
         to_remove.append(UpperCAmelCase		)
       for key in to_remove:
        kwargs.pop(UpperCAmelCase  ,   UpperCAmelCase		)

       if return_unused_kwargs:
        return config, kwargs
       else:
        return config






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, os.PathLike]		)	->	Dict:


       '''simple docstring'''

       with open(UpperCAmelCase  ,   '''w'''  ,   encoding='''utf-8'''		) as writer:
        lowercase       : Any						=self.to_dict()
        lowercase       : List[Any]						=json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + '''\n'''

        writer.write(UpperCAmelCase		)






      def 					A__   (			self      :		Optional[Any]		)	->	Dict[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=copy.deepcopy(self.__dict__		)
       lowercase       : Optional[Any]						=str(output['''bnb_4bit_compute_dtype''']		).split('''.'''		)[1]

       return output






      def __repr__(			self      :		Optional[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       return f'{self.__class__.__name__} {self.to_json_string()}'






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True		)	->	str:


       '''simple docstring'''

       if use_diff is True:
        lowercase       : int						=self.to_diff_dict()
       else:
        lowercase       : List[str]						=self.to_dict()
       return json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + "\n"






      def 					A__   (			self      :		Any		)	->	Dict[str, Any]:


       '''simple docstring'''

       lowercase       : Any						=self.to_dict()

       # get the default config dict
       lowercase       : Union[str, Any]						=BitsAndBytesConfig().to_dict()

       lowercase       : int						={}

       # only serialize values that differ from the default config
       for key, value in config_dict.items():
        if value != default_config_dict[key]:
         lowercase       : Dict						=value

       return serializable_config_dict



