





'''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    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      str							      =     old_name

			if "patch_embed" in old_name:
						__snake_case      ,     __snake_case      ,     __snake_case					:      List[Any]							      =     old_name.split(""".""" )

						if layer == "0":
									__snake_case					:      int							      =     old_name.replace("""0"""    ,	"""convolution1""" )
						elif layer == "1":
									__snake_case					:      List[str]							      =     old_name.replace("""1"""    ,	"""batchnorm_before""" )
						elif layer == "3":
									__snake_case					:      List[str]							      =     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					:      str							      =     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[Any]							      =     re.search(R"""\d\.\d."""    ,	_lowerCamelCase ).group()
						if int(match[0] ) < 6:
									__snake_case					:      Dict							      =     old_name.replace(_lowerCamelCase    ,	"""""" )
									__snake_case					:      Any							      =     trimmed_name.replace("""network"""    ,	match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
									__snake_case					:      Optional[int]							      =     """intermediate_stages.""" + trimmed_name
						else:
									__snake_case					:      Dict							      =     old_name.replace(_lowerCamelCase    ,	"""""" )
									if int(match[2] ) < num_meta4D_last_stage:
												__snake_case					:      Optional[Any]							      =     trimmed_name.replace("""network"""    ,	"""meta4D_layers.blocks.""" + match[2] )
									else:
												__snake_case					:      Union[str, Any]							      =     str(int(match[2] ) - num_meta4D_last_stage )
												__snake_case					:      Optional[int]							      =     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					:      Optional[int]							      =     trimmed_name.replace("""norm2"""    ,	"""layernorm2""" )
												elif "fc1" in old_name:
															__snake_case					:      List[Any]							      =     trimmed_name.replace("""fc1"""    ,	"""linear_in""" )
												elif "fc2" in old_name:
															__snake_case					:      List[Any]							      =     trimmed_name.replace("""fc2"""    ,	"""linear_out""" )

									__snake_case					:      Any							      =     """last_stage.""" + trimmed_name

			elif "network" in old_name and re.search(R""".\d."""    ,	_lowerCamelCase ):
						__snake_case					:      int							      =     old_name.replace("""network"""    ,	"""intermediate_stages""" )

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

			return new_name







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			for key in checkpoint.copy().keys():
						__snake_case					:      int							      =     checkpoint.pop(_lowerCamelCase )
						__snake_case					:      int							      =     val

			return checkpoint







def 					_a						(       ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[str]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )

			return image







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      List[Any]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )["""model"""]
			__snake_case					:      Union[str, Any]							      =     EfficientFormerConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Dict							      =     EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     """_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )

			__snake_case					:      Dict							      =     config.depths[-1] - config.num_metaad_blocks + 1
			__snake_case					:      Any							      =     convert_torch_checkpoint(_lowerCamelCase    ,	_lowerCamelCase )

			model.load_state_dict(_lowerCamelCase )
			model.eval()

			__snake_case					:      Optional[int]							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

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

			# original processing pipeline
			__snake_case					:      int							      =     Compose(
			    [
			        Resize(_lowerCamelCase    ,	interpolation=pillow_resamplings["""bicubic"""] ),
			        CenterCrop(_lowerCamelCase ),
			        ToTensor(),
			        Normalize(_lowerCamelCase    ,	_lowerCamelCase ),
			    ] )
			__snake_case					:      int							      =     image_transforms(_lowerCamelCase ).unsqueeze(0 )

			assert torch.allclose(_lowerCamelCase    ,	_lowerCamelCase )

			__snake_case					:      Dict							      =     model(_lowerCamelCase )
			__snake_case					:      Dict							      =     outputs.logits

			__snake_case					:      List[str]							      =     (1, 1000)

			if "l1" in model_name:
						__snake_case					:      Dict							      =     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]    ,	_lowerCamelCase    ,	atol=1E-3 )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__snake_case					:      int							      =     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]    ,	_lowerCamelCase    ,	atol=1E-3 )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__snake_case					:      int							      =     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(_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__":
	__UpperCamelCase					    =	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					    =	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'''






import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__UpperCamelCase					    =	"http://www.mocksite.com/file1.txt"
__UpperCamelCase					    =	"\"text\": [\"foo\", \"foo\"]"
__UpperCamelCase					    =	"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"




class  _A :
		lowercase__:   str				     =   200
		lowercase__:   List[str]				     =   {'''Content-Length''': '''100'''}
		lowercase__:   Union[str, Any]				     =   {}

		def 	lowercase__     (   self					:   Any  ,      **__magic_name__					:   List[Any]			)					->    Dict:


					"""simple docstring"""

					return [bytes(__magic_name__  ,      """utf-8"""			)]







def 					_a						(       *_lowerCamelCase    ,	**_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			return MockResponse()







@pytest.mark.parametrize("""urls_type"""    ,	[str, list, dict] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			import requests

			monkeypatch.setattr(_lowerCamelCase    ,	"""request"""    ,	_lowerCamelCase )

			__snake_case					:      Union[str, Any]							      =     URL
			if issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      str							      =     url
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Dict							      =     [url]
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Union[str, Any]							      =     {"""train""": url}
			__snake_case					:      Dict							      =     """dummy"""
			__snake_case					:      List[str]							      =     """downloads"""
			__snake_case					:      List[Any]							      =     tmp_path
			__snake_case					:      List[Any]							      =     DownloadConfig(
			    cache_dir=os.path.join(_lowerCamelCase    ,	_lowerCamelCase )    ,	use_etag=_lowerCamelCase    ,	)
			__snake_case					:      List[str]							      =     DownloadManager(dataset_name=_lowerCamelCase    ,	download_config=_lowerCamelCase )
			__snake_case					:      int							      =     dl_manager.download(_lowerCamelCase )
			__snake_case					:      Tuple							      =     urls
			for downloaded_paths in [downloaded_paths]:
						if isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									__snake_case					:      Any							      =     [downloaded_paths]
									__snake_case					:      List[Any]							      =     [urls]
						elif isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									assert "train" in downloaded_paths.keys()
									__snake_case					:      Tuple							      =     downloaded_paths.values()
									__snake_case					:      Optional[int]							      =     urls.values()
						assert downloaded_paths
						for downloaded_path, input_url in zip(_lowerCamelCase    ,	_lowerCamelCase ):
									assert downloaded_path == dl_manager.downloaded_paths[input_url]
									__snake_case					:      List[str]							      =     Path(_lowerCamelCase )
									__snake_case					:      Any							      =     downloaded_path.parts
									assert parts[-1] == HASH
									assert parts[-2] == cache_subdir
									assert downloaded_path.exists()
									__snake_case					:      Union[str, Any]							      =     downloaded_path.read_text()
									assert content == CONTENT
									__snake_case					:      List[str]							      =     downloaded_path.with_suffix(""".json""" )
									assert metadata_downloaded_path.exists()
									__snake_case					:      Union[str, Any]							      =     json.loads(metadata_downloaded_path.read_text() )
									assert metadata_content == {"url": URL, "etag": None}







@pytest.mark.parametrize("""paths_type"""    ,	[str, list, dict] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      Any							      =     str(_lowerCamelCase )
			if issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Optional[int]							      =     filename
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Tuple							      =     [filename]
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Dict							      =     {"""train""": filename}
			__snake_case					:      Optional[Any]							      =     """dummy"""
			__snake_case					:      List[Any]							      =     xz_file.parent
			__snake_case					:      int							      =     """extracted"""
			__snake_case					:      Dict							      =     DownloadConfig(
			    cache_dir=_lowerCamelCase    ,	use_etag=_lowerCamelCase    ,	)
			__snake_case					:      List[str]							      =     DownloadManager(dataset_name=_lowerCamelCase    ,	download_config=_lowerCamelCase )
			__snake_case					:      Optional[Any]							      =     dl_manager.extract(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     paths
			for extracted_paths in [extracted_paths]:
						if isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									__snake_case					:      Dict							      =     [extracted_paths]
									__snake_case					:      int							      =     [paths]
						elif isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									assert "train" in extracted_paths.keys()
									__snake_case					:      int							      =     extracted_paths.values()
									__snake_case					:      int							      =     paths.values()
						assert extracted_paths
						for extracted_path, input_path in zip(_lowerCamelCase    ,	_lowerCamelCase ):
									assert extracted_path == dl_manager.extracted_paths[input_path]
									__snake_case					:      Any							      =     Path(_lowerCamelCase )
									__snake_case					:      str							      =     extracted_path.parts
									assert parts[-1] == hash_url_to_filename(_lowerCamelCase    ,	etag=_lowerCamelCase )
									assert parts[-2] == extracted_subdir
									assert extracted_path.exists()
									__snake_case					:      Optional[int]							      =     extracted_path.read_text()
									__snake_case					:      str							      =     text_file.read_text()
									assert extracted_file_content == expected_file_content







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			assert path.endswith(""".jsonl""" )
			for num_items, line in enumerate(_lowerCamelCase    ,	start=1 ):
						__snake_case					:      Tuple							      =     json.loads(line.decode("""utf-8""" ) )
						assert item.keys() == {"col_1", "col_2", "col_3"}
			assert num_items == 4







@pytest.mark.parametrize("""archive_jsonl"""    ,	["""tar_jsonl_path""", """zip_jsonl_path"""] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			__snake_case					:      Any							      =     request.getfixturevalue(_lowerCamelCase )
			__snake_case					:      str							      =     DownloadManager()
			for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase )    ,	start=1 ):
						_test_jsonl(_lowerCamelCase    ,	_lowerCamelCase )
			assert num_jsonl == 2







@pytest.mark.parametrize("""archive_nested_jsonl"""    ,	["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      int							      =     request.getfixturevalue(_lowerCamelCase )
			__snake_case					:      List[str]							      =     DownloadManager()
			for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase )    ,	start=1 ):
						for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCamelCase )    ,	start=1 ):
									_test_jsonl(_lowerCamelCase    ,	_lowerCamelCase )
			assert num_tar == 1
			assert num_jsonl == 2







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     DownloadManager()
			for num_file, file in enumerate(dl_manager.iter_files(_lowerCamelCase )    ,	start=1 ):
						assert os.path.basename(_lowerCamelCase ) == ("test.txt" if num_file == 1 else "train.txt")
			assert num_file == 2


