





'''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,
	)









import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "OwlViTImageProcessor"
							lowerCamelCase_							     =       ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(				self     :Optional[Any]				,					__A     :int=None				,					__A     :Optional[int]=None				,					**__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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				,					)
											SCREAMING_SNAKE_CASE__								=   kwargs.pop("""feature_extractor"""		)

									SCREAMING_SNAKE_CASE__								=   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     :str				,					__A     :Dict=None				,					__A     :List[str]=None				,					__A     :str=None				,					__A     :Optional[int]="max_length"				,					__A     :Tuple="np"				,					**__A     :int		) -> Tuple:



									"""simple docstring"""



									if text is None and query_images is None and images is None:
											raise ValueError(
											    """You have to specify at least one text or query image or image. All three cannot be none."""		)

									if text is not None:
											if isinstance(__A				,					__A		) or (isinstance(__A				,					__A		) and not isinstance(text[0]				,					__A		)):
													SCREAMING_SNAKE_CASE__								=   [self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)]

											elif isinstance(__A				,					__A		) and isinstance(text[0]				,					__A		):
													SCREAMING_SNAKE_CASE__								=   []

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE__								=   max([len(__A		) for t in text]		)

													# Pad all batch samples to max number of text queries
													for t in text:
															if len(__A		) != max_num_queries:
																	SCREAMING_SNAKE_CASE__								=   t + [""" """] * (max_num_queries - len(__A		))

															SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)
															encodings.append(__A		)
											else:
													raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""		)

											if return_tensors == "np":
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "pt" and is_torch_available():
													import torch

													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""input_ids"""] for encoding in encodings]				,					dim=0		)
													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""attention_mask"""] for encoding in encodings]				,					dim=0		)

											elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											else:
													raise ValueError("""Target return tensor type could not be returned"""		)

											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   input_ids
											SCREAMING_SNAKE_CASE__								=   attention_mask

									if query_images is not None:
											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   self.image_processor(
											    __A				,					return_tensors=__A				,					**__A		).pixel_values
											SCREAMING_SNAKE_CASE__								=   query_pixel_values

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif query_images is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None or query_images is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :List[Any]				,					*__A     :Dict				,					**__A     :Dict		) -> Optional[int]:



									"""simple docstring"""



									return self.image_processor.post_process(*__A				,					**__A		)


							def   _snake_case	(				self     :Optional[int]				,					*__A     :Dict				,					**__A     :List[str]		) -> Optional[Any]:



									"""simple docstring"""



									return self.image_processor.post_process_object_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									return self.image_processor.post_process_image_guided_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :List[str]				,					**__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :Dict				,					**__A     :List[str]		) -> str:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[int]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,					__A				,					)
									return self.image_processor_class


							@property
							def   _snake_case	(				self     :Any		) -> Optional[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,					__A				,					)
									return self.image_processor