



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCAmelCase						      =				{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_swa import GPTSwaTokenizer

else:
							import sys

							__lowerCAmelCase						      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''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,
							)




