
"""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__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			Tuple    )					->							List[Any]:
       lowercase       :							List[str]									=				old_name

       if "patch_embed" in old_name:
              lowercase       :							Optional[int]									=				old_name.split("."    )

              if layer == "0":
                     lowercase       :							int									=				old_name.replace("0"	,					"convolution1"    )
              elif layer == "1":
                     lowercase       :							Optional[Any]									=				old_name.replace("1"	,					"batchnorm_before"    )
              elif layer == "3":
                     lowercase       :							List[Any]									=				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"	,					SCREAMING_SNAKE_CASE__    ):
              lowercase       :							str									=				r'\b\d{2}\b'
              if bool(re.search(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    )    ):
                     lowercase       :							Optional[int]									=				re.search(r"\d\.\d\d."	,					SCREAMING_SNAKE_CASE__    ).group()
              else:
                     lowercase       :							Optional[int]									=				re.search(r"\d\.\d."	,					SCREAMING_SNAKE_CASE__    ).group()
              if int(match[0]    ) < 6:
                     lowercase       :							Any									=				old_name.replace(SCREAMING_SNAKE_CASE__	,					""    )
                     lowercase       :							Optional[int]									=				trimmed_name.replace("network"	,					match[0] + ".meta4D_layers.blocks." + match[2:-1]    )
                     lowercase       :							Union[str, Any]									=				'intermediate_stages.' + trimmed_name
              else:
                     lowercase       :							Tuple									=				old_name.replace(SCREAMING_SNAKE_CASE__	,					""    )
                     if int(match[2]    ) < num_meta4D_last_stage:
                            lowercase       :							int									=				trimmed_name.replace("network"	,					"meta4D_layers.blocks." + match[2]    )
                     else:
                            lowercase       :							int									=				str(int(match[2]    ) - num_meta4D_last_stage    )
                            lowercase       :							List[Any]									=				trimmed_name.replace("network"	,					"meta3D_layers.blocks." + layer_index    )
                            if "norm1" in old_name:
                                   lowercase       :							Tuple									=				trimmed_name.replace("norm1"	,					"layernorm1"    )
                            elif "norm2" in old_name:
                                   lowercase       :							Tuple									=				trimmed_name.replace("norm2"	,					"layernorm2"    )
                            elif "fc1" in old_name:
                                   lowercase       :							Any									=				trimmed_name.replace("fc1"	,					"linear_in"    )
                            elif "fc2" in old_name:
                                   lowercase       :							int									=				trimmed_name.replace("fc2"	,					"linear_out"    )

                     lowercase       :							Any									=				'last_stage.' + trimmed_name

       elif "network" in old_name and re.search(r".\d."	,					SCREAMING_SNAKE_CASE__    ):
              lowercase       :							Any									=				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       :							Union[str, Any]									=				new_name.replace("norm1"	,					"batchnorm_before"    )
       elif ("norm2" in new_name) and ("layernorm2" not in new_name):
              lowercase       :							Optional[Any]									=				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       :							Union[str, Any]									=				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       :							Dict									=				'efficientformer.' + new_name
       elif new_name == "norm.weight" or new_name == "norm.bias":
              lowercase       :							Optional[int]									=				new_name.replace("norm"	,					"layernorm"    )
              lowercase       :							str									=				'efficientformer.' + new_name
       else:
              lowercase       :							Tuple									=				'efficientformer.encoder.' + new_name

       return new_name


def     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			Tuple    )					->							List[str]:





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

       return checkpoint


def     __magic_name__      ( )					->							str:
       lowercase       :							Optional[int]									=				'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase       :							int									=				Image.open(requests.get(SCREAMING_SNAKE_CASE__	,					stream=SCREAMING_SNAKE_CASE__    ).raw    )

       return image


def     __magic_name__      ( __snake_case							:			Path	,					__snake_case							:			Path	,					__snake_case							:			Path	,					__snake_case							:			bool    )					->							int:
       lowercase       :							List[Any]									=				torch.load(SCREAMING_SNAKE_CASE__	,					map_location="cpu"    )['model']
       lowercase       :							str									=				EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE__    )
       lowercase       :							Tuple									=				EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__    )
       lowercase       :							Optional[int]									=				'_'.join(checkpoint_path.split("/"    )[-1].split("."    )[0].split("_"    )[:-1]    )

       lowercase       :							str									=				config.depths[-1] - config.num_metaad_blocks + 1
       lowercase       :							List[Any]									=				convert_torch_checkpoint(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    )

       model.load_state_dict(SCREAMING_SNAKE_CASE__    )
       model.eval()

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

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

       # original processing pipeline
       lowercase       :							Dict									=				Compose(
           [
               Resize(SCREAMING_SNAKE_CASE__	,					interpolation=pillow_resamplings["bicubic"]    ),
               CenterCrop(SCREAMING_SNAKE_CASE__    ),
               ToTensor(),
               Normalize(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    ),
           ]    )
       lowercase       :							Tuple									=				image_transforms(SCREAMING_SNAKE_CASE__    ).unsqueeze(0    )

       assert torch.allclose(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    )

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

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

       if "l1" in model_name:
              lowercase       :							Dict									=				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]	,					SCREAMING_SNAKE_CASE__	,					atol=1E-3    )
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              lowercase       :							Dict									=				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]	,					SCREAMING_SNAKE_CASE__	,					atol=1E-3    )
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              lowercase       :							Union[str, Any]									=				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(SCREAMING_SNAKE_CASE__    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )
       print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""    )
       processor.save_pretrained(SCREAMING_SNAKE_CASE__    )
       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=SCREAMING_SNAKE_CASE__	,					)
              processor.push_to_hub(
                  repo_id=f"""Bearnardd/{pytorch_dump_path}"""	,					commit_message="Add image processor"	,					use_temp_dir=SCREAMING_SNAKE_CASE__	,					)


if __name__ == "__main__":
   _A     :			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)

   _A     :			str  						=			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 time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )




@require_torch
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	int					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =3
             _lowerCamelCase			:						Dict            =250

             _lowerCamelCase			:						Tuple            =ids_tensor((batch_size, length)     ,  lowercase_					)
             _lowerCamelCase			:						str            =torch.ones((batch_size, length)     ,  device=lowercase_     ,  dtype=torch.float					) / length
             return input_ids, scores






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Dict            =StoppingCriteriaList(
                 [
                     MaxLengthCriteria(max_length=10					),
                     MaxTimeCriteria(max_time=0.1					),
                 ]					)

             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Any            =MaxLengthCriteria(max_length=10					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =MaxNewTokensCriteria(start_length=5     ,  max_new_tokens=5					)

             _lowerCamelCase      ,	_lowerCamelCase			:						str            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						Optional[Any]            =StoppingCriteriaList([criteria]					)
             self.assertEqual(criteria_list.max_length     ,  10					)






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Tuple            =MaxTimeCriteria(max_time=0.1					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						int            =MaxTimeCriteria(max_time=0.1     ,  initial_timestamp=time.time() - 0.2					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  10					)

             with self.assertWarns(lowercase_					):
                    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  11					)

             _lowerCamelCase			:						Optional[Any]            =validate_stopping_criteria(StoppingCriteriaList()     ,  11					)

             self.assertEqual(len(lowercase_					)     ,  1					)


