

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    __UpperCAmelCase (	__a      :			str		,__a      :			Dict	)			->     Optional[Any]:

					"""simple docstring"""

					_a	:    Optional[int]         =		old_name

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

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

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

															_a	:    str         =		'''last_stage.''' + trimmed_name

					elif "network" in old_name and re.search(R'''.\d.'''		,__a	):
										_a	:    Tuple         =		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	:    int         =		new_name.replace('''norm1'''		,'''batchnorm_before'''	)
					elif ("norm2" in new_name) and ("layernorm2" not in new_name):
										_a	:    List[str]         =		new_name.replace('''norm2'''		,'''batchnorm_after'''	)
					if "proj" in new_name:
										_a	:    List[str]         =		new_name.replace('''proj'''		,'''projection'''	)
					if "dist_head" in new_name:
										_a	:    Optional[int]         =		new_name.replace('''dist_head'''		,'''distillation_classifier'''	)
					elif "head" in new_name:
										_a	:    Optional[Any]         =		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	:    Union[str, Any]         =		new_name.replace('''norm'''		,'''layernorm'''	)
										_a	:    int         =		'''efficientformer.''' + new_name
					else:
										_a	:    Dict         =		'''efficientformer.encoder.''' + new_name

					return new_name



def    __UpperCAmelCase (	__a      :			Tuple		,__a      :			Union[str, Any]	)			->     int:

					"""simple docstring"""

					for key in checkpoint.copy().keys():
										_a	:    Any         =		checkpoint.pop(__a	)
										_a	:    List[str]         =		val

					return checkpoint



def    __UpperCAmelCase (	)			->     Optional[Any]:

					"""simple docstring"""

					_a	:    Any         =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a	:    int         =		Image.open(requests.get(__a		,stream=__a	).raw	)

					return image



def    __UpperCAmelCase (	__a      :			Path		,__a      :			Path		,__a      :			Path		,__a      :			bool	)			->     int:

					"""simple docstring"""

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

					_a	:    Tuple         =		config.depths[-1] - config.num_metaad_blocks + 1
					_a	:    Optional[int]         =		convert_torch_checkpoint(__a		,__a	)

					model.load_state_dict(__a	)
					model.eval()

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

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

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

					assert torch.allclose(__a		,__a	)

					_a	:    str         =		model(__a	)
					_a	:    Dict         =		outputs.logits

					_a	:    List[Any]         =		(1, 1_000)

					if "l1" in model_name:
										_a	:    List[str]         =		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]		,__a		,atol=1E-3	)
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										_a	:    Dict         =		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]		,__a		,atol=1E-3	)
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										_a	:    List[Any]         =		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(__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__":
			a__				=		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)

			a__				=		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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Optional[int]	    =    "CLIPImageProcessor"
							UpperCAmelCase__       :						Tuple	    =    ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    int         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    int         =		kwargs.pop('''feature_extractor'''       )

												_a	:    int         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    List[Any]:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    str         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    List[Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[int]:
												return self.tokenizer.decode(*_a       ,							**_a       )




							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



