



'''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 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				List[Any]				):
							SCREAMING_SNAKE_CASE					:      str     					=			old_name

							if "patch_embed" in old_name:
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			old_name.split('''.'''				)

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

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

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

							elif "network" in old_name and re.search(r'''.\d.'''					,		_SCREAMING_SNAKE_CASE				):
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			old_name.replace('''network'''					,		'''intermediate_stages'''				)

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

							return new_name
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				Union[str, Any]				):



							for key in checkpoint.copy().keys():
														SCREAMING_SNAKE_CASE					:      str     					=			checkpoint.pop(_SCREAMING_SNAKE_CASE				)
														SCREAMING_SNAKE_CASE					:      Any     					=			val

							return checkpoint
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE					:      Any     					=			Image.open(requests.get(_SCREAMING_SNAKE_CASE					,		stream=_SCREAMING_SNAKE_CASE				).raw				)

							return image
def 					UpperCAmelCase   (						A  :				Path					,		A  :				Path					,		A  :				Path					,		A  :				bool				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.load(_SCREAMING_SNAKE_CASE					,		map_location='''cpu'''				)['''model''']
							SCREAMING_SNAKE_CASE					:      Tuple     					=			EfficientFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			EfficientFormerForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''_'''.join(checkpoint_path.split('''/'''				)[-1].split('''.'''				)[0].split('''_'''				)[:-1]				)

							SCREAMING_SNAKE_CASE					:      Any     					=			config.depths[-1] - config.num_metaad_blocks + 1
							SCREAMING_SNAKE_CASE					:      Any     					=			convert_torch_checkpoint(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE				)

							model.load_state_dict(_SCREAMING_SNAKE_CASE				)
							model.eval()

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

							# prepare image
							SCREAMING_SNAKE_CASE					:      Dict     					=			prepare_img()
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			256
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			224
							SCREAMING_SNAKE_CASE					:      int     					=			EfficientFormerImageProcessor(
							    size={'''shortest_edge''': image_size}					,		crop_size={'''height''': crop_size, '''width''': crop_size}					,		resample=pillow_resamplings['''bicubic''']					,		)
							SCREAMING_SNAKE_CASE					:      int     					=			processor(images=_SCREAMING_SNAKE_CASE					,		return_tensors='''pt'''				).pixel_values

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

							assert torch.allclose(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(_SCREAMING_SNAKE_CASE				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			outputs.logits

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			(1, 1000)

							if "l1" in model_name:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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]					,		_SCREAMING_SNAKE_CASE					,		atol=1e-3				)
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														SCREAMING_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]					,		_SCREAMING_SNAKE_CASE					,		atol=1e-3				)
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														SCREAMING_SNAKE_CASE					:      List[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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)
							print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""				)
							processor.save_pretrained(_SCREAMING_SNAKE_CASE				)
							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=_SCREAMING_SNAKE_CASE					,		)
														processor.push_to_hub(
														    repo_id=F"""Bearnardd/{pytorch_dump_path}"""					,		commit_message='''Add image processor'''					,		use_temp_dir=_SCREAMING_SNAKE_CASE					,		)


if __name__ == "__main__":
							lowerCAmelCase_     :       Dict								=						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_     :       Dict								=						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'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




