





'''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				( lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	List[Any]:
				_a								= old_name

				if "patch_embed" in old_name:
								_a								= old_name.split("."						)

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

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

												_a								= '''last_stage.''' + trimmed_name

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

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

				return new_name






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      Optional[int]						)	->	int:
				for key in checkpoint.copy().keys():
								_a								= checkpoint.pop(lowercase__						)
								_a								= val

				return checkpoint






def  _lowerCamelCase				( )	->	List[Any]:
				_a								= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_a								= Image.open(requests.get(lowercase__    ,							stream=lowercase__						).raw						)

				return image






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Dict    ,							lowercase						:      List[Any]    ,							lowercase						:      Dict						)	->	str:
				_a								= torch.load(lowercase__    ,							map_location="cpu"						)['''model''']
				_a								= EfficientFormerConfig.from_json_file(lowercase__						)
				_a								= EfficientFormerForImageClassificationWithTeacher(lowercase__						)
				_a								= '''_'''.join(checkpoint_path.split("/"						)[-1].split("."						)[0].split("_"						)[:-1]						)

				_a								= config.depths[-1] - config.num_metaad_blocks + 1
				_a								= convert_torch_checkpoint(lowercase__    ,							lowercase__						)

				model.load_state_dict(lowercase__						)
				model.eval()

				_a								= {
				    '''bilinear''': PILImageResampling.BILINEAR,
				    '''bicubic''': PILImageResampling.BICUBIC,
				    '''nearest''': PILImageResampling.NEAREST,
				}

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

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

				assert torch.allclose(lowercase__    ,							lowercase__						)

				_a								= model(lowercase__						)
				_a								= outputs.logits

				_a								= (1, 1000)

				if "l1" in model_name:
								_a								= 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]    ,							lowercase__    ,							atol=1E-3						)
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								_a								= 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]    ,							lowercase__    ,							atol=1E-3						)
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								_a								= 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(lowercase__						).mkdir(exist_ok=lowercase__						)
				model.save_pretrained(lowercase__						)
				print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'						)
				processor.save_pretrained(lowercase__						)
				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=lowercase__    ,							)
								processor.push_to_hub(
								    repo_id=F'Bearnardd/{pytorch_dump_path}'    ,							commit_message="Add image processor"    ,							use_temp_dir=lowercase__    ,							)


if __name__ == "__main__":
					lowerCAmelCase_					:    Dict      	=			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_					:    Any      	=			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,
					)



def 		_lowercase (     lowercase__						):
      if not all(x.isalpha() for x in string						):
            raise ValueError('''String must only contain alphabetic characters.'''						)

      __lowerCAmelCase					:						int					=					sorted(string.lower()						)
      return len(lowercase__						) == len(set(lowercase__						)						)


if __name__ == "__main__":
 _UpperCamelCase 			=		input("Enter a string ").strip()

 _UpperCamelCase 			=		is_isogram(input_str)
 print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")


