


'''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 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= old_name

						if "patch_embed" in old_name:
												__lowerCAmelCase											= old_name.split('''.''')

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

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

																		__lowerCAmelCase											= '''last_stage.''' + trimmed_name

						elif "network" in old_name and re.search(r'''.\d.''', a_):
												__lowerCAmelCase											= old_name.replace('''network''', '''intermediate_stages''')

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

						return new_name

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						for key in checkpoint.copy().keys():
												__lowerCAmelCase											= checkpoint.pop(a_)
												__lowerCAmelCase											= val

						return checkpoint

def 							__magic_name__(  ):
						__lowerCAmelCase											= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase											= Image.open(requests.get(a_, stream=a_).raw)

						return image

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= torch.load(a_, map_location='''cpu''')['''model''']
						__lowerCAmelCase											= EfficientFormerConfig.from_json_file(a_)
						__lowerCAmelCase											= EfficientFormerForImageClassificationWithTeacher(a_)
						__lowerCAmelCase											= '''_'''.join(checkpoint_path.split('''/''')[-1].split('''.''')[0].split('''_''')[:-1])

						__lowerCAmelCase											= config.depths[-1] - config.num_metaad_blocks + 1
						__lowerCAmelCase											= convert_torch_checkpoint(a_, a_)

						model.load_state_dict(a_)
						model.eval()

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

						# prepare image
						__lowerCAmelCase											= prepare_img()
						__lowerCAmelCase											= 2_5_6
						__lowerCAmelCase											= 2_2_4
						__lowerCAmelCase											= EfficientFormerImageProcessor(
						    size={'''shortest_edge''': image_size}, crop_size={'''height''': crop_size, '''width''': crop_size}, resample=pillow_resamplings['''bicubic'''], )
						__lowerCAmelCase											= processor(images=a_, return_tensors='''pt''').pixel_values

						# original processing pipeline
						__lowerCAmelCase											= Compose(
						    [
						        Resize(a_, interpolation=pillow_resamplings['''bicubic''']),
						        CenterCrop(a_),
						        ToTensor(),
						        Normalize(a_, a_),
						    ])
						__lowerCAmelCase											= image_transforms(a_).unsqueeze(0)

						assert torch.allclose(a_, a_)

						__lowerCAmelCase											= model(a_)
						__lowerCAmelCase											= outputs.logits

						__lowerCAmelCase											= (1, 1_0_0_0)

						if "l1" in model_name:
												__lowerCAmelCase											= 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, :1_0], a_, atol=1E-3)
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												__lowerCAmelCase											= 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, :1_0], a_, atol=1E-3)
												assert logits.shape == expected_shape
						elif "l7" in model_name:
												__lowerCAmelCase											= 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 :       Any										= 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 :       Any										= 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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_								=		{
    "configuration_blip_2": [
        "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Blip2Config",
        "Blip2QFormerConfig",
        "Blip2VisionConfig",
    ],
    "processing_blip_2": ["Blip2Processor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Blip2Model",
      "Blip2QFormerModel",
      "Blip2PreTrainedModel",
      "Blip2ForConditionalGeneration",
      "Blip2VisionModel",
  ]

if TYPE_CHECKING:
  from .configuration_blip_a import (
      BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlipaConfig,
      BlipaQFormerConfig,
      BlipaVisionConfig,
  )
  from .processing_blip_a import BlipaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip_a import (
        BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipaForConditionalGeneration,
        BlipaModel,
        BlipaPreTrainedModel,
        BlipaQFormerModel,
        BlipaVisionModel,
    )

else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)