

'''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__						:       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'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       Dict										=						argparse.ArgumentParser()

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					parser.add_argument(
					    """--txt2img_unclip""",
					    default="""kakaobrain/karlo-v1-alpha""",
					    type=str,
					    required=False,
					    help="""The pretrained txt2img unclip.""",
					)

					lowerCAmelCase__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)



