




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			List[str]  )			->			List[str]:
   _SCREAMING_SNAKE_CASE	=				old_name

   if "patch_embed" in old_name:
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				old_name.split("."  )

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

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

         _SCREAMING_SNAKE_CASE	=				"last_stage." + trimmed_name

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

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

   return new_name
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[int]  )			->			Tuple:
   for key in checkpoint.copy().keys():
      _SCREAMING_SNAKE_CASE	=				checkpoint.pop(__A  )
      _SCREAMING_SNAKE_CASE	=				val

   return checkpoint
def 			SCREAMING_SNAKE_CASE_			(							)			->			str:
   _SCREAMING_SNAKE_CASE	=				"http://images.cocodataset.org/val2017/000000039769.jpg"
   _SCREAMING_SNAKE_CASE	=				Image.open(requests.get(__A	,					stream=__A  ).raw  )

   return image
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Path	,					__A    :			Path	,					__A    :			Path	,					__A    :			bool  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				torch.load(__A	,					map_location="cpu"  )["model"]
   _SCREAMING_SNAKE_CASE	=				EfficientFormerConfig.from_json_file(__A  )
   _SCREAMING_SNAKE_CASE	=				EfficientFormerForImageClassificationWithTeacher(__A  )
   _SCREAMING_SNAKE_CASE	=				"_".join(checkpoint_path.split("/"  )[-1].split("."  )[0].split("_"  )[:-1]  )

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

   model.load_state_dict(__A  )
   model.eval()

   _SCREAMING_SNAKE_CASE	=				{
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   _SCREAMING_SNAKE_CASE	=				prepare_img()
   _SCREAMING_SNAKE_CASE	=				2_56
   _SCREAMING_SNAKE_CASE	=				2_24
   _SCREAMING_SNAKE_CASE	=				EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}	,					crop_size={"height": crop_size, "width": crop_size}	,					resample=pillow_resamplings["bicubic"]	,					)
   _SCREAMING_SNAKE_CASE	=				processor(images=__A	,					return_tensors="pt"  ).pixel_values

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

   assert torch.allclose(__A	,					__A  )

   _SCREAMING_SNAKE_CASE	=				model(__A  )
   _SCREAMING_SNAKE_CASE	=				outputs.logits

   _SCREAMING_SNAKE_CASE	=				(1, 10_00)

   if "l1" in model_name:
      _SCREAMING_SNAKE_CASE	=				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]	,					__A	,					atol=1e-3  )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      _SCREAMING_SNAKE_CASE	=				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]	,					__A	,					atol=1e-3  )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      _SCREAMING_SNAKE_CASE	=				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(__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__":
 lowerCamelCase_			=						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)

 lowerCamelCase_			=						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_tf_available, is_torch_available


lowerCamelCase_			=						{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CTRLForSequenceClassification',
     'CTRLLMHeadModel',
     'CTRLModel',
     'CTRLPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCTRLForSequenceClassification',
     'TFCTRLLMHeadModel',
     'TFCTRLModel',
     'TFCTRLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .tokenization_ctrl import CTRLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ctrl import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
      CTRLPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
      TFCTRLPreTrainedModel,
  )

else:
 import sys

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

