



"""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__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				old_name

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

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

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

                  UpperCAmelCase_     					=				"last_stage." + trimmed_name

      elif "network" in old_name and re.search(r".\d."      ,     lowerCAmelCase__ ):
            UpperCAmelCase_     					=				old_name.replace("network"      ,     "intermediate_stages" )

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

      return new_name
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      for key in checkpoint.copy().keys():
            UpperCAmelCase_     					=				checkpoint.pop(lowerCAmelCase__ )
            UpperCAmelCase_     					=				val

      return checkpoint
def   a__      ( ):
      UpperCAmelCase_     					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_     					=				Image.open(requests.get(lowerCAmelCase__      ,     stream=lowerCAmelCase__ ).raw )

      return image
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				torch.load(lowerCAmelCase__      ,     map_location="cpu" )["model"]
      UpperCAmelCase_     					=				EfficientFormerConfig.from_json_file(lowerCAmelCase__ )
      UpperCAmelCase_     					=				EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__ )
      UpperCAmelCase_     					=				"_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )

      UpperCAmelCase_     					=				config.depths[-1] - config.num_metaad_blocks + 1
      UpperCAmelCase_     					=				convert_torch_checkpoint(lowerCAmelCase__      ,     lowerCAmelCase__ )

      model.load_state_dict(lowerCAmelCase__ )
      model.eval()

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

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

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

      assert torch.allclose(lowerCAmelCase__      ,     lowerCAmelCase__ )

      UpperCAmelCase_     					=				model(lowerCAmelCase__ )
      UpperCAmelCase_     					=				outputs.logits

      UpperCAmelCase_     					=				(1, 1000)

      if "l1" in model_name:
            UpperCAmelCase_     					=				torch.Tensor(
                [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
            assert torch.allclose(logits[0, :10]      ,     lowerCAmelCase__      ,     atol=1e-3 )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            UpperCAmelCase_     					=				torch.Tensor(
                [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
            assert torch.allclose(logits[0, :10]      ,     lowerCAmelCase__      ,     atol=1e-3 )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            UpperCAmelCase_     					=				torch.Tensor(
                [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
            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(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
      model.save_pretrained(lowerCAmelCase__ )
      print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
      processor.save_pretrained(lowerCAmelCase__ )
      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=lowerCAmelCase__      ,     )
            processor.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}"""      ,     commit_message="Add image processor"      ,     use_temp_dir=lowerCAmelCase__      ,     )


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 __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=3   ,      snake_case_=32   ,      snake_case_=3   ,      snake_case_=10   ,      snake_case_=[10, 20, 30, 40]   ,      snake_case_=[1, 1, 2, 1]   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="relu"   ,      snake_case_=3   ,      snake_case_=None   ,      ):







										'''simple docstring'''


										A__       :     Tuple								=			parent
										A__       :     int								=			batch_size
										A__       :     List[Any]								=			image_size
										A__       :     Any								=			num_channels
										A__       :     Tuple								=			embeddings_size
										A__       :     Union[str, Any]								=			hidden_sizes
										A__       :     Tuple								=			depths
										A__       :     Union[str, Any]								=			is_training
										A__       :     Any								=			use_labels
										A__       :     Optional[Any]								=			hidden_act
										A__       :     str								=			num_labels
										A__       :     Optional[Any]								=			scope
										A__       :     List[Any]								=			len(snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										A__       :     str								=			None
										if self.use_labels:
																	A__       :     Tuple								=			ids_tensor([self.batch_size]   ,      self.num_labels							)

										A__       :     Dict								=			self.get_config()
										return config, pixel_values, labels





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return RegNetConfig(
										    num_channels=self.num_channels   ,      embeddings_size=self.embeddings_size   ,      hidden_sizes=self.hidden_sizes   ,      depths=self.depths   ,      hidden_act=self.hidden_act   ,      num_labels=self.num_labels   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			TFRegNetModel(config=snake_case_							)
										A__       :     Optional[int]								=			model(snake_case_   ,      training=snake_case_							)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.num_labels
										A__       :     Tuple								=			TFRegNetForImageClassification(snake_case_							)
										A__       :     Optional[int]								=			model(snake_case_   ,      labels=snake_case_   ,      training=snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.prepare_config_and_inputs()
										A__				,							A__				,							A__       :     Optional[Any]								=			config_and_inputs
										A__       :     str								=			{"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_tf
class 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
			_UpperCamelCase  :						Tuple					     =				(
			    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			_UpperCamelCase  :						str					     =				False
			_UpperCamelCase  :						int					     =				False
			_UpperCamelCase  :						Tuple					     =				False
			_UpperCamelCase  :						Any					     =				False
			_UpperCamelCase  :						Union[str, Any]					     =				False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			TFRegNetModelTester(self							)
										A__       :     str								=			ConfigTester(self   ,      config_class=snake_case_   ,      has_text_modality=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return





			@unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""							)							) == 0   ,      reason="""TF does not support backprop for grouped convolutions on CPU."""   ,      )
			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().test_keras_fit()





			@unittest.skip(reason="""RegNet does not support input and output embeddings"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     int								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__       :     int								=			model_class(snake_case_							)
																	A__       :     Optional[int]								=			inspect.signature(model.call							)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__       :     Union[str, Any]								=			[*signature.parameters.keys()]

																	A__       :     Tuple								=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										def check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							):
																	A__       :     str								=			model_class(snake_case_							)
																	A__       :     Union[str, Any]								=			model(**self._prepare_for_class(snake_case_   ,      snake_case_							)   ,      training=snake_case_							)

																	A__       :     Optional[int]								=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	A__       :     Union[str, Any]								=			self.model_tester.num_stages
																	self.assertEqual(len(snake_case_							)   ,      expected_num_stages + 1							)

																	# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]							)   ,      [self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,      )

										A__				,							A__       :     int								=			self.model_tester.prepare_config_and_inputs_for_common()
										A__       :     Optional[int]								=			["""basic""", """bottleneck"""]
										for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																								A__       :     Optional[Any]								=			layer_type
																								A__       :     Dict								=			True
																								check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								A__       :     List[Any]								=			True

																								check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Optional[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_={}							):
																	A__       :     List[str]								=			model(snake_case_   ,      return_dict=snake_case_   ,      **snake_case_							)
																	A__       :     Union[str, Any]								=			model(snake_case_   ,      return_dict=snake_case_   ,      **snake_case_							).to_tuple()

																	def recursive_check(snake_case_   ,      snake_case_							):
																								if isinstance(snake_case_   ,      (List, Tuple)							):
																															for tuple_iterable_value, dict_iterable_value in zip(snake_case_   ,      snake_case_							):
																																						recursive_check(snake_case_   ,      snake_case_							)
																								elif tuple_object is None:
																															return
																								else:
																															self.assertTrue(
																															    all(tf.equal(snake_case_   ,      snake_case_							)							)   ,      msg=(
																															        """Tuple and dict output are not equal. Difference:"""
																															        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}'''
																															    )   ,      )

																								recursive_check(snake_case_   ,      snake_case_							)

										for model_class in self.all_model_classes:
																	A__       :     Any								=			model_class(snake_case_							)

																	A__       :     Dict								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	A__       :     List[Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_							)

																	A__       :     Tuple								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     List[str]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_							)

																	A__       :     Optional[int]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	A__       :     List[str]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      {"""output_hidden_states""": True}							)

																	A__       :     Dict								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     Union[str, Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      {"""output_hidden_states""": True}							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*snake_case_							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__       :     Any								=			TFRegNetModel.from_pretrained(snake_case_							)
																	self.assertIsNotNone(snake_case_							)





def  _A(		):
							A__       :     Optional[int]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image



@require_tf
@require_vision
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return (
										    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
										    if is_vision_available()
										    else None
										)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

										A__       :     str								=			self.default_image_processor
										A__       :     Any								=			prepare_img()
										A__       :     Tuple								=			image_processor(images=snake_case_   ,      return_tensors="""tf"""							)

										# forward pass
										A__       :     List[Any]								=			model(**snake_case_   ,      training=snake_case_							)

										# verify the logits
										A__       :     Tuple								=			tf.TensorShape((1, 1_000)							)
										self.assertEqual(outputs.logits.shape   ,      snake_case_							)

										A__       :     Tuple								=			tf.constant([-0.41_80, -1.50_51, -3.48_36]							)

										tf.debugging.assert_near(outputs.logits[0, :3]   ,      snake_case_   ,      atol=1E-4							)





