






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_ (      _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     old_name

				if "patch_embed" in old_name:
								snake_case__							,						snake_case__							,						snake_case__									=     old_name.split('.'  )

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

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

												snake_case__									=     'last_stage.' + trimmed_name

				elif "network" in old_name and re.search(R'.\d.'      ,   _A  ):
								snake_case__									=     old_name.replace('network'      ,   'intermediate_stages'  )

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

				return new_name
def      a_ (      _A      ,   _A  )			->			Tuple:







				"""simple docstring"""
				for key in checkpoint.copy().keys():
								snake_case__									=     checkpoint.pop(_A  )
								snake_case__									=     val

				return checkpoint
def      a_ (      )			->			Tuple:







				"""simple docstring"""
				snake_case__									=     'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__									=     Image.open(requests.get(_A      ,   stream=_A  ).raw  )

				return image
def      a_ (      _A      ,   _A      ,   _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     torch.load(_A      ,   map_location='cpu'  )['model']
				snake_case__									=     EfficientFormerConfig.from_json_file(_A  )
				snake_case__									=     EfficientFormerForImageClassificationWithTeacher(_A  )
				snake_case__									=     '_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

				snake_case__									=     config.depths[-1] - config.num_metaad_blocks + 1
				snake_case__									=     convert_torch_checkpoint(_A      ,   _A  )

				model.load_state_dict(_A  )
				model.eval()

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

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

				# original processing pipeline
				snake_case__									=     Compose(
				    [
				        Resize(_A      ,   interpolation=pillow_resamplings['bicubic']  ),
				        CenterCrop(_A  ),
				        ToTensor(),
				        Normalize(_A      ,   _A  ),
				    ]  )
				snake_case__									=     image_transforms(_A  ).unsqueeze(0  )

				assert torch.allclose(_A      ,   _A  )

				snake_case__									=     model(_A  )
				snake_case__									=     outputs.logits

				snake_case__									=     (1, 1000)

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


if __name__ == "__main__":
							__UpperCamelCase					:		Optional[int]	  =						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)

							__UpperCamelCase					:		Optional[int]	  =						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,
							)












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




