

'''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 	_a       (				__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      Union[str, Any]     ):

		"""simple docstring"""

		snake_case__						:       int       						=  old_name

		if "patch_embed" in old_name:
				snake_case__						,       snake_case__						,       snake_case__						:       Optional[int]       						=  old_name.split('''.'''     )

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

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

						snake_case__						:       Dict       						=  '''last_stage.''' + trimmed_name

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

		if "fc" in new_name:
				snake_case__						:       Optional[int]       						=  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__						:       Tuple       						=  new_name.replace('''norm2'''   ,							'''batchnorm_after'''     )
		if "proj" in new_name:
				snake_case__						:       Optional[Any]       						=  new_name.replace('''proj'''   ,							'''projection'''     )
		if "dist_head" in new_name:
				snake_case__						:       Tuple       						=  new_name.replace('''dist_head'''   ,							'''distillation_classifier'''     )
		elif "head" in new_name:
				snake_case__						:       Any       						=  new_name.replace('''head'''   ,							'''classifier'''     )
		elif "patch_embed" in new_name:
				snake_case__						:       Optional[int]       						=  '''efficientformer.''' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				snake_case__						:       List[Any]       						=  new_name.replace('''norm'''   ,							'''layernorm'''     )
				snake_case__						:       str       						=  '''efficientformer.''' + new_name
		else:
				snake_case__						:       int       						=  '''efficientformer.encoder.''' + new_name

		return new_name




def 	_a       (				__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		for key in checkpoint.copy().keys():
				snake_case__						:       Optional[Any]       						=  checkpoint.pop(__lowerCAmelCase     )
				snake_case__						:       int       						=  val

		return checkpoint




def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Tuple       						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case__						:       List[Any]       						=  Image.open(requests.get(__lowerCAmelCase   ,							stream=__lowerCAmelCase     ).raw     )

		return image




def 	_a       (				__lowerCAmelCase		:      Path   ,							__lowerCAmelCase		:      Path   ,							__lowerCAmelCase		:      Path   ,							__lowerCAmelCase		:      bool     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  torch.load(__lowerCAmelCase   ,							map_location='''cpu'''     )['''model''']
		snake_case__						:       Optional[int]       						=  EfficientFormerConfig.from_json_file(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase     )
		snake_case__						:       Union[str, Any]       						=  '''_'''.join(checkpoint_path.split('''/'''     )[-1].split('''.'''     )[0].split('''_'''     )[:-1]     )

		snake_case__						:       Any       						=  config.depths[-1] - config.num_metaad_blocks + 1
		snake_case__						:       Tuple       						=  convert_torch_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase     )

		model.load_state_dict(__lowerCAmelCase     )
		model.eval()

		snake_case__						:       Union[str, Any]       						=  {
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		# prepare image
		snake_case__						:       Optional[Any]       						=  prepare_img()
		snake_case__						:       List[str]       						=  2_56
		snake_case__						:       List[Any]       						=  2_24
		snake_case__						:       int       						=  EfficientFormerImageProcessor(
		    size={'''shortest_edge''': image_size}   ,							crop_size={'''height''': crop_size, '''width''': crop_size}   ,							resample=pillow_resamplings['''bicubic''']   ,							)
		snake_case__						:       int       						=  processor(images=__lowerCAmelCase   ,							return_tensors='''pt'''     ).pixel_values

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

		assert torch.allclose(__lowerCAmelCase   ,							__lowerCAmelCase     )

		snake_case__						:       Optional[int]       						=  model(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  outputs.logits

		snake_case__						:       List[str]       						=  (1, 10_00)

		if "l1" in model_name:
				snake_case__						:       List[Any]       						=  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]   ,							__lowerCAmelCase   ,							atol=1e-3     )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				snake_case__						:       Union[str, Any]       						=  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]   ,							__lowerCAmelCase   ,							atol=1e-3     )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				snake_case__						:       Optional[Any]       						=  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(__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__						:       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)

					lowerCAmelCase__						:       Tuple										=						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,
					)






'''simple docstring'''


from __future__ import annotations

from typing import Any




class     a			:






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      int			,			snake_case_					:      float = 0  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Optional[Any]       						=  row, column
								snake_case__						:       Dict       						=  [[default_value for c in range(snake_case_  )] for r in range(snake_case_  )]







						def __str__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

								# Make string identifier
								snake_case__						:       List[str]       						=  0
								for row_vector in self.array:
										for obj in row_vector:
												snake_case__						:       str       						=  max(snake_case_			,			len(str(snake_case_  )  )  )
								snake_case__						:       Dict       						=  F"""%{max_element_length}s"""

								# Make string and return
								def single_line(snake_case_					:      list[float]  ) -> str:
										nonlocal string_format_identifier
										snake_case__						:       str       						=  '''['''
										line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
										line += "]"
										return line

								s += "\n".join(single_line(snake_case_  ) for row_vector in self.array  )
								return s







						def __repr__(							self					:      Optional[int]  ):



								'''simple docstring'''



								return str(self  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								if not (isinstance(snake_case_			,			(list, tuple)  ) and len(snake_case_  ) == 2):
										return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
										return False
								else:
										return True







						def __getitem__(							self					:      List[str]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								return self.array[loc[0]][loc[1]]







						def __setitem__(							self					:      int			,			snake_case_					:      tuple[int, int]			,			snake_case_					:      float  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								snake_case__						:       Optional[Any]       						=  value







						def __add__(							self					:      Dict			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  )
								assert self.row == another.row and self.column == another.column

								# Add
								snake_case__						:       str       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       Optional[Any]       						=  self[r, c] + another[r, c]
								return result







						def __neg__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       str       						=  -self[r, c]
								return result







						def __sub__(							self					:      Optional[int]			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								return self + (-another)







						def __mul__(							self					:      int			,			snake_case_					:      int | float | Matrix  ):



								'''simple docstring'''



								if isinstance(snake_case_			,			(int, float)  ):  # Scalar multiplication
										snake_case__						:       Dict       						=  Matrix(self.row			,			self.column  )
										for r in range(self.row  ):
												for c in range(self.column  ):
														snake_case__						:       Tuple       						=  self[r, c] * another
										return result
								elif isinstance(snake_case_			,			snake_case_  ):  # Matrix multiplication
										assert self.column == another.row
										snake_case__						:       List[Any]       						=  Matrix(self.row			,			another.column  )
										for r in range(self.row  ):
												for c in range(another.column  ):
														for i in range(self.column  ):
																result[r, c] += self[r, i] * another[i, c]
										return result
								else:
										snake_case__						:       Union[str, Any]       						=  F"""Unsupported type given for another ({type(snake_case_  )})"""
										raise TypeError(snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  Matrix(self.column			,			self.row  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       List[Any]       						=  self[r, c]
								return result







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Matrix			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  ) and isinstance(snake_case_			,			snake_case_  )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								snake_case__						:       Tuple       						=  v.transpose()
								snake_case__						:       Optional[Any]       						=  (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
										return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




					def 	_a       (				):

							"""simple docstring"""

							snake_case__						:       Tuple       						=  Matrix(3   ,							3   ,							0     )
							for i in range(3     ):
									snake_case__						:       Any       						=  1
							print(F"""a^(-1) is {ainv}"""     )
							# u, v
							snake_case__						:       int       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  1, 2, -3
							snake_case__						:       Any       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       Optional[int]       						=  4, -2, 5
							print(F"""u is {u}"""     )
							print(F"""v is {v}"""     )
							print(F"""uv^T is {u * v.transpose()}"""     )
							# Sherman Morrison
							print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase   ,							__lowerCAmelCase     )}"""     )




					def 	_a       (				):

							"""simple docstring"""

							import doctest

							doctest.testmod()





					testa()



