

'''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 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]						)				->  Tuple:
						lowercase :							Union[str, Any]    		=old_name

						if "patch_embed" in old_name:
												lowercase		,	lowercase		,	lowercase :							Any    		=old_name.split('''.'''						)

												if layer == "0":
																		lowercase :							int    		=old_name.replace('''0'''   , '''convolution1'''						)
												elif layer == "1":
																		lowercase :							List[Any]    		=old_name.replace('''1'''   , '''batchnorm_before'''						)
												elif layer == "3":
																		lowercase :							Tuple    		=old_name.replace('''3'''   , '''convolution2'''						)
												else:
																		lowercase :							Optional[Any]    		=old_name.replace('''4'''   , '''batchnorm_after'''						)

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

																		lowercase :							Dict    		='''last_stage.''' + trimmed_name

						elif "network" in old_name and re.search(R'''.\d.'''   , __magic_name__						):
												lowercase :							Tuple    		=old_name.replace('''network'''   , '''intermediate_stages'''						)

						if "fc" in new_name:
												lowercase :							Union[str, Any]    		=new_name.replace('''fc'''   , '''convolution'''						)
						elif ("norm1" in new_name) and ("layernorm1" not in new_name):
												lowercase :							Optional[Any]    		=new_name.replace('''norm1'''   , '''batchnorm_before'''						)
						elif ("norm2" in new_name) and ("layernorm2" not in new_name):
												lowercase :							Tuple    		=new_name.replace('''norm2'''   , '''batchnorm_after'''						)
						if "proj" in new_name:
												lowercase :							Tuple    		=new_name.replace('''proj'''   , '''projection'''						)
						if "dist_head" in new_name:
												lowercase :							str    		=new_name.replace('''dist_head'''   , '''distillation_classifier'''						)
						elif "head" in new_name:
												lowercase :							str    		=new_name.replace('''head'''   , '''classifier'''						)
						elif "patch_embed" in new_name:
												lowercase :							List[str]    		='''efficientformer.''' + new_name
						elif new_name == "norm.weight" or new_name == "norm.bias":
												lowercase :							str    		=new_name.replace('''norm'''   , '''layernorm'''						)
												lowercase :							List[Any]    		='''efficientformer.''' + new_name
						else:
												lowercase :							Union[str, Any]    		='''efficientformer.encoder.''' + new_name

						return new_name






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     List[str]						)				->  Optional[int]:

						for key in checkpoint.copy().keys():
												lowercase :							Optional[Any]    		=checkpoint.pop(__magic_name__						)
												lowercase :							Any    		=val

						return checkpoint






def 				_lowerCAmelCase			(   )				->  Any:
						lowercase :							Tuple    		='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase :							str    		=Image.open(requests.get(__magic_name__   , stream=__magic_name__						).raw						)

						return image






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]   , __magic_name__						:     Dict   , __magic_name__						:     Tuple   , __magic_name__						:     Union[str, Any]						)				->  List[Any]:
						lowercase :							List[Any]    		=torch.load(__magic_name__   , map_location='''cpu'''						)['''model''']
						lowercase :							Tuple    		=EfficientFormerConfig.from_json_file(__magic_name__						)
						lowercase :							Union[str, Any]    		=EfficientFormerForImageClassificationWithTeacher(__magic_name__						)
						lowercase :							Tuple    		='''_'''.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

						lowercase :							int    		=config.depths[-1] - config.num_metaad_blocks + 1
						lowercase :							Optional[int]    		=convert_torch_checkpoint(__magic_name__   , __magic_name__						)

						model.load_state_dict(__magic_name__						)
						model.eval()

						lowercase :							List[str]    		={
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

						# prepare image
						lowercase :							Optional[int]    		=prepare_img()
						lowercase :							str    		=256
						lowercase :							Dict    		=224
						lowercase :							List[str]    		=EfficientFormerImageProcessor(
						    size={'''shortest_edge''': image_size}   , crop_size={'''height''': crop_size, '''width''': crop_size}   , resample=pillow_resamplings['''bicubic''']   , )
						lowercase :							int    		=processor(images=__magic_name__   , return_tensors='''pt'''						).pixel_values

						# original processing pipeline
						lowercase :							Optional[Any]    		=Compose(
						    [
						        Resize(__magic_name__   , interpolation=pillow_resamplings['''bicubic''']						),
						        CenterCrop(__magic_name__						),
						        ToTensor(),
						        Normalize(__magic_name__   , __magic_name__						),
						    ]						)
						lowercase :							List[Any]    		=image_transforms(__magic_name__						).unsqueeze(0						)

						assert torch.allclose(__magic_name__   , __magic_name__						)

						lowercase :							Dict    		=model(__magic_name__						)
						lowercase :							List[str]    		=outputs.logits

						lowercase :							Optional[int]    		=(1, 1000)

						if "l1" in model_name:
												lowercase :							Dict    		=torch.Tensor(
												    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]						)
												assert torch.allclose(logits[0, :10]   , __magic_name__   , atol=1E-3						)
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												lowercase :							Any    		=torch.Tensor(
												    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]						)
												assert torch.allclose(logits[0, :10]   , __magic_name__   , atol=1E-3						)
												assert logits.shape == expected_shape
						elif "l7" in model_name:
												lowercase :							str    		=torch.Tensor(
												    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]						)
												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(__magic_name__						).mkdir(exist_ok=__magic_name__						)
						model.save_pretrained(__magic_name__						)
						print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)
						processor.save_pretrained(__magic_name__						)
						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=__magic_name__   , )
												processor.push_to_hub(
												    repo_id=f'''Bearnardd/{pytorch_dump_path}'''   , commit_message='''Add image processor'''   , use_temp_dir=__magic_name__   , )


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,
							)












import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
    import torch.distributed.checkpoint as dist_cp
    from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
    from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase_												=	get_logger(__name__)






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         UpperCAmelCase__						=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         UpperCAmelCase__						=os.path.join(A				,							A     )
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCAmelCase__						=(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Saving model to {output_model_file}"""     )
         torch.save(A				,							A     )
         logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving model to {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": state_dict}

         dist_cp.save_state_dict(
             state_dict=A				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Model saved to {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if type(A     ) != FSDP and accelerator.process_index != 0:
            if not fsdp_plugin.sync_module_states:
               raise ValueError(
                   "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
                   "initializing FSDP object"     )
            return
         UpperCAmelCase__						=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCAmelCase__						=(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=(
             os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
             if F"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading model from {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=A				,							storage_reader=dist_cp.FileSystemReader(A     )				,							planner=DefaultLoadPlanner()				,							)
         UpperCAmelCase__						=state_dict["model"]
         logger.info(F"""Model loaded from {ckpt_dir}"""     )
      model.load_state_dict(A     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=FSDP.optim_state_dict(A				,							A     )
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            UpperCAmelCase__						=(
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            UpperCAmelCase__						=os.path.join(A				,							A     )
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
         dist_cp.save_state_dict(
             state_dict={"optimizer": optim_state}				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         UpperCAmelCase__						=None
         # below check should work but currently it isn't working (mostly opytorch issue),
         # in the meantime disabling it at the cost of excess memory usage
         # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
         UpperCAmelCase__						=(
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=(
             os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
             if F"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading Optimizer from {ckpt_dir}"""     )
         UpperCAmelCase__						=load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()				,							optimizer_key="optimizer"				,							storage_reader=dist_cp.FileSystemReader(A     )				,							)
         UpperCAmelCase__						=optim_state["optimizer"]
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
      UpperCAmelCase__						=FSDP.optim_state_dict_to_load(A				,							A				,							A     )
      optimizer.load_state_dict(A     )



