





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				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   old_name

					if "patch_embed" in old_name:
										_snake_case       :					Any				       =   old_name.split("."				)

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

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

															_snake_case       :					Tuple				       =   "last_stage." + trimmed_name

					elif "network" in old_name and re.search(R".\d."  ,  UpperCAmelCase				):
										_snake_case       :					Optional[int]				       =   old_name.replace("network"  ,  "intermediate_stages"				)

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

					return new_name







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					for key in checkpoint.copy().keys():
										_snake_case       :					Tuple				       =   checkpoint.pop(UpperCAmelCase				)
										_snake_case       :					str				       =   val

					return checkpoint







def    A				(							):
					_snake_case       :					Tuple				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					List[str]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)

					return image







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   torch.load(UpperCAmelCase  ,  map_location="cpu"				)["model"]
					_snake_case       :					Optional[int]				       =   EfficientFormerConfig.from_json_file(UpperCAmelCase				)
					_snake_case       :					int				       =   EfficientFormerForImageClassificationWithTeacher(UpperCAmelCase				)
					_snake_case       :					Any				       =   "_".join(checkpoint_path.split("/"				)[-1].split("."				)[0].split("_"				)[:-1]				)

					_snake_case       :					int				       =   config.depths[-1] - config.num_metaad_blocks + 1
					_snake_case       :					str				       =   convert_torch_checkpoint(UpperCAmelCase  ,  UpperCAmelCase				)

					model.load_state_dict(UpperCAmelCase				)
					model.eval()

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

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

					# original processing pipeline
					_snake_case       :					Union[str, Any]				       =   Compose(
					    [
					        Resize(UpperCAmelCase  ,  interpolation=pillow_resamplings["bicubic"]				),
					        CenterCrop(UpperCAmelCase				),
					        ToTensor(),
					        Normalize(UpperCAmelCase  ,  UpperCAmelCase				),
					    ]				)
					_snake_case       :					Optional[int]				       =   image_transforms(UpperCAmelCase				).unsqueeze(0				)

					assert torch.allclose(UpperCAmelCase  ,  UpperCAmelCase				)

					_snake_case       :					List[str]				       =   model(UpperCAmelCase				)
					_snake_case       :					List[str]				       =   outputs.logits

					_snake_case       :					Union[str, Any]				       =   (1, 1_000)

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


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    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 :Optional[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,
							)






import re







def    A				(							UpperCAmelCase				):
					_snake_case       :					Any				       =   re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"				)
					if match := re.search(UpperCAmelCase  ,  UpperCAmelCase				):
										return match.string == phone
					return False


if __name__ == "__main__":
							print(indian_phone_validator('+918827897895'))