


"""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 __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Union[str, Any]  ):
				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"						,					lowerCamelCase_  ):
								SCREAMING_SNAKE_CASE__     							=				R"\b\d{2}\b"
								if bool(re.search(lowerCamelCase_						,					lowerCamelCase_  )  ):
												SCREAMING_SNAKE_CASE__     							=				re.search(R"\d\.\d\d."						,					lowerCamelCase_  ).group()
								else:
												SCREAMING_SNAKE_CASE__     							=				re.search(R"\d\.\d."						,					lowerCamelCase_  ).group()
								if int(match[0]  ) < 6:
												SCREAMING_SNAKE_CASE__     							=				old_name.replace(lowerCamelCase_						,					""  )
												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(lowerCamelCase_						,					""  )
												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."						,					lowerCamelCase_  ):
								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 __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      Any  ):
				for key in checkpoint.copy().keys():
								SCREAMING_SNAKE_CASE__     							=				checkpoint.pop(lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				val

				return checkpoint


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
				SCREAMING_SNAKE_CASE__     							=				Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_  ).raw  )

				return image


def __lowercase     (      lowerCamelCase_  :      Path						,					lowerCamelCase_  :      Path						,					lowerCamelCase_  :      Path						,					lowerCamelCase_  :      bool  ):
				SCREAMING_SNAKE_CASE__     							=				torch.load(lowerCamelCase_						,					map_location="cpu"  )["model"]
				SCREAMING_SNAKE_CASE__     							=				EfficientFormerConfig.from_json_file(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				EfficientFormerForImageClassificationWithTeacher(lowerCamelCase_  )
				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(lowerCamelCase_						,					lowerCamelCase_  )

				model.load_state_dict(lowerCamelCase_  )
				model.eval()

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

				# prepare image
				SCREAMING_SNAKE_CASE__     							=				prepare_img()
				SCREAMING_SNAKE_CASE__     							=				256
				SCREAMING_SNAKE_CASE__     							=				224
				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=lowerCamelCase_						,					return_tensors="pt"  ).pixel_values

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

				assert torch.allclose(lowerCamelCase_						,					lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				model(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				outputs.logits

				SCREAMING_SNAKE_CASE__     							=				(1, 1000)

				if "l1" in model_name:
								SCREAMING_SNAKE_CASE__     							=				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:
								SCREAMING_SNAKE_CASE__     							=				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:
								SCREAMING_SNAKE_CASE__     							=				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"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  500000

_lowerCamelCase     ,     _lowerCamelCase     =  os.path.split(__file__)
_lowerCamelCase     =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.map(**lowerCamelCase_  )


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.filter(**lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE__     							=				datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
								SCREAMING_SNAKE_CASE__     							=				generate_example_dataset(
								    os.path.join(lowerCamelCase_						,					"dataset.arrow"  )						,					lowerCamelCase_						,					num_examples=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"						,					use_fast=lowerCamelCase_  )

								def tokenize(lowerCamelCase_  :      Union[str, Any]  ):
												return tokenizer(examples["text"]  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="numpy"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="pandas"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="torch"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="tensorflow"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				filter(lowerCamelCase_  )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_						,					"wb"  ) as f:
								f.write(json.dumps(lowerCamelCase_  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_map_filter()
