


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 (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       List[Any]								=      old_name

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

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

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

						_UpperCAmelCase		:       List[str]								=      "last_stage." + trimmed_name

		elif "network" in old_name and re.search(R".\d."      , __lowerCAmelCase   ):
				_UpperCAmelCase		:       List[str]								=      old_name.replace("network"      , "intermediate_stages"   )

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

		return new_name






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):


		for key in checkpoint.copy().keys():
				_UpperCAmelCase		:       Optional[Any]								=      checkpoint.pop(__lowerCAmelCase   )
				_UpperCAmelCase		:       Union[str, Any]								=      val

		return checkpoint






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Optional[int]								=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase		:       List[Any]								=      Image.open(requests.get(__lowerCAmelCase      , stream=__lowerCAmelCase   ).raw   )

		return image






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[Any]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )["model"]
		_UpperCAmelCase		:       List[Any]								=      EfficientFormerConfig.from_json_file(__lowerCAmelCase   )
		_UpperCAmelCase		:       List[str]								=      EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase   )
		_UpperCAmelCase		:       str								=      "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

		_UpperCAmelCase		:       List[str]								=      config.depths[-1] - config.num_metaad_blocks + 1
		_UpperCAmelCase		:       Optional[Any]								=      convert_torch_checkpoint(__lowerCAmelCase      , __lowerCAmelCase   )

		model.load_state_dict(__lowerCAmelCase   )
		model.eval()

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

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

		# original processing pipeline
		_UpperCAmelCase		:       Dict								=      Compose(
		    [
		        Resize(__lowerCAmelCase      , interpolation=pillow_resamplings["bicubic"]   ),
		        CenterCrop(__lowerCAmelCase   ),
		        ToTensor(),
		        Normalize(__lowerCAmelCase      , __lowerCAmelCase   ),
		    ]   )
		_UpperCAmelCase		:       List[str]								=      image_transforms(__lowerCAmelCase   ).unsqueeze(0   )

		assert torch.allclose(__lowerCAmelCase      , __lowerCAmelCase   )

		_UpperCAmelCase		:       Optional[int]								=      model(__lowerCAmelCase   )
		_UpperCAmelCase		:       Union[str, Any]								=      outputs.logits

		_UpperCAmelCase		:       Optional[Any]								=      (1, 1_000)

		if "l1" in model_name:
				_UpperCAmelCase		:       List[str]								=      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]      , __lowerCAmelCase      , atol=1e-3   )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_UpperCAmelCase		:       Union[str, 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]      , __lowerCAmelCase      , atol=1e-3   )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_UpperCAmelCase		:       List[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(__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'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__    	=     {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MraForMaskedLM',
						    'MraForMultipleChoice',
						    'MraForQuestionAnswering',
						    'MraForSequenceClassification',
						    'MraForTokenClassification',
						    'MraLayer',
						    'MraModel',
						    'MraPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure)



