


'''simple docstring'''


def        __lowerCAmelCase       (__lowerCAmelCase = "The quick brown fox jumps over the lazy dog"					,       ):
		_UpperCAmelCase						:				Any	    =					set()

		# Replace all the whitespace in our sentence
		_UpperCAmelCase						:				List[Any]	    =					input_str.replace(" "					,       ""      )
		for alpha in input_str:
				if "a" <= alpha.lower() <= "z":
						frequency.add(alpha.lower()      )
		return len(__lowerCAmelCase      ) == 26






def        __lowerCAmelCase       (__lowerCAmelCase = "The quick brown fox jumps over the lazy dog"					,       ):
		_UpperCAmelCase						:				int	    =					[False] * 26
		for char in input_str:
				if char.islower():
						_UpperCAmelCase						:				Any	    =					True
				elif char.isupper():
						_UpperCAmelCase						:				Any	    =					True
		return all(__lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase = "The quick brown fox jumps over the lazy dog"					,       ):
		return len({char for char in input_str.lower() if char.isalpha()}      ) == 26






def        __lowerCAmelCase       ():
		from timeit import timeit

		_UpperCAmelCase						:				Any	    =					"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
		print(timeit("is_pangram()"					,       setup=__lowerCAmelCase      )      )
		print(timeit("is_pangram_faster()"					,       setup=__lowerCAmelCase      )      )
		print(timeit("is_pangram_fastest()"					,       setup=__lowerCAmelCase      )      )
		# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
		# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()







'''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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					old_name

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

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

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

						_UpperCAmelCase						:				Optional[Any]	    =					"last_stage." + trimmed_name

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

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

		return new_name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for key in checkpoint.copy().keys():
				_UpperCAmelCase						:				List[Any]	    =					checkpoint.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val

		return checkpoint






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

		return image






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

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

		model.load_state_dict(__lowerCAmelCase      )
		model.eval()

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

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

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

		assert torch.allclose(__lowerCAmelCase					,       __lowerCAmelCase      )

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

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

		if "l1" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					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						:				List[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[Any]	    =					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,
						)



