



'''simple docstring'''

from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				list[float]:
		_a	, _a					:				List[str]				=							coefficient_matrix.shape
		_a	, _a					:				List[str]				=							constant_matrix.shape

		if rowsa != colsa:
				_a					:				List[Any]				=							f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
				raise ValueError(lowerCAmelCase_  )

		if colsa != 1:
				_a					:				Tuple				=							f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
				raise ValueError(lowerCAmelCase_  )

		if rowsa != rowsa:
				_a					:				Dict				=							(
				    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
				    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
				)
				raise ValueError(lowerCAmelCase_  )

		if len(lowerCAmelCase_  ) != rowsa:
				_a					:				Any				=							(
				    'Number of initial values must be equal to number of rows in coefficient '
				    f"""matrix but received {len(lowerCAmelCase_  )} and {rowsa}"""
				)
				raise ValueError(lowerCAmelCase_  )

		if iterations <= 0:
				raise ValueError('Iterations must be at least 1'  )

		_a					:				NDArray[floataa]				=							np.concatenate(
		    (coefficient_matrix, constant_matrix)						,      axis=1  )

		_a	, _a					:				Dict				=							table.shape

		strictly_diagonally_dominant(lowerCAmelCase_  )

		# Iterates the whole matrix for given number of times
		for _ in range(lowerCAmelCase_  ):
				_a					:				List[Any]				=							[]
				for row in range(lowerCAmelCase_  ):
						_a					:				Optional[int]				=							0
						for col in range(lowerCAmelCase_  ):
								if col == row:
										_a					:				int				=							table[row][col]
								elif col == cols - 1:
										_a					:				Any				=							table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						_a					:				int				=							(temp + val) / denom
						new_val.append(lowerCAmelCase_  )
				_a					:				Any				=							new_val

		return [float(lowerCAmelCase_  ) for i in new_val]



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				bool:
		_a	, _a					:				Any				=							table.shape

		_a					:				List[Any]				=							True

		for i in range(0						,      lowerCAmelCase_  ):
				_a					:				List[Any]				=							0
				for j in range(0						,      cols - 1  ):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError('Coefficient matrix is not strictly diagonally dominant'  )

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
							import doctest

							doctest.testmod()









'''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_  )      ->				int:
		_a					:				Optional[int]				=							old_name

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

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

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

						_a					:				Dict				=							'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(r'.\d.'						,      lowerCAmelCase_  ):
				_a					:				List[str]				=							old_name.replace('network'						,      'intermediate_stages'  )

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

		return new_name



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		for key in checkpoint.copy().keys():
				_a					:				List[str]				=							checkpoint.pop(lowerCAmelCase_  )
				_a					:				List[str]				=							val

		return checkpoint



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

		return image



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

		_a					:				Dict				=							config.depths[-1] - config.num_metaad_blocks + 1
		_a					:				int				=							convert_torch_checkpoint(lowerCAmelCase_						,      lowerCAmelCase_  )

		model.load_state_dict(lowerCAmelCase_  )
		model.eval()

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

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

		# original processing pipeline
		_a					:				str				=							Compose(
		    [
		        Resize(lowerCAmelCase_						,      interpolation=pillow_resamplings['bicubic']  ),
		        CenterCrop(lowerCAmelCase_  ),
		        ToTensor(),
		        Normalize(lowerCAmelCase_						,      lowerCAmelCase_  ),
		    ]  )
		_a					:				Any				=							image_transforms(lowerCAmelCase_  ).unsqueeze(0  )

		assert torch.allclose(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				Tuple				=							model(lowerCAmelCase_  )
		_a					:				Tuple				=							outputs.logits

		_a					:				List[Any]				=							(1, 1000)

		if "l1" in model_name:
				_a					:				Optional[Any]				=							torch.Tensor(
				    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]  )
				assert torch.allclose(logits[0, :10]						,      lowerCAmelCase_						,      atol=1E-3  )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_a					:				List[Any]				=							torch.Tensor(
				    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]  )
				assert torch.allclose(logits[0, :10]						,      lowerCAmelCase_						,      atol=1E-3  )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_a					:				Tuple				=							torch.Tensor(
				    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]  )
				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,
							)




