


"""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			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							snake_case_          =						old_name

							if "patch_embed" in old_name:
														snake_case_    ,  snake_case_    ,  snake_case_          =						old_name.split("""."""			)

														if layer == "0":
																					snake_case_          =						old_name.replace("""0"""			,	"""convolution1"""			)
														elif layer == "1":
																					snake_case_          =						old_name.replace("""1"""			,	"""batchnorm_before"""			)
														elif layer == "3":
																					snake_case_          =						old_name.replace("""3"""			,	"""convolution2"""			)
														else:
																					snake_case_          =						old_name.replace("""4"""			,	"""batchnorm_after"""			)

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

																					snake_case_          =						"""last_stage.""" + trimmed_name

							elif "network" in old_name and re.search(r""".\d."""			,	_SCREAMING_SNAKE_CASE			):
														snake_case_          =						old_name.replace("""network"""			,	"""intermediate_stages"""			)

							if "fc" in new_name:
														snake_case_          =						new_name.replace("""fc"""			,	"""convolution"""			)
							elif ("norm1" in new_name) and ("layernorm1" not in new_name):
														snake_case_          =						new_name.replace("""norm1"""			,	"""batchnorm_before"""			)
							elif ("norm2" in new_name) and ("layernorm2" not in new_name):
														snake_case_          =						new_name.replace("""norm2"""			,	"""batchnorm_after"""			)
							if "proj" in new_name:
														snake_case_          =						new_name.replace("""proj"""			,	"""projection"""			)
							if "dist_head" in new_name:
														snake_case_          =						new_name.replace("""dist_head"""			,	"""distillation_classifier"""			)
							elif "head" in new_name:
														snake_case_          =						new_name.replace("""head"""			,	"""classifier"""			)
							elif "patch_embed" in new_name:
														snake_case_          =						"""efficientformer.""" + new_name
							elif new_name == "norm.weight" or new_name == "norm.bias":
														snake_case_          =						new_name.replace("""norm"""			,	"""layernorm"""			)
														snake_case_          =						"""efficientformer.""" + new_name
							else:
														snake_case_          =						"""efficientformer.encoder.""" + new_name

							return new_name







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							for key in checkpoint.copy().keys():
														snake_case_          =						checkpoint.pop(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						val

							return checkpoint







def   _a			( )      ->			Any:
							snake_case_          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_          =						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,	stream=_SCREAMING_SNAKE_CASE			).raw			)

							return image







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							snake_case_          =						torch.load(_SCREAMING_SNAKE_CASE			,	map_location="""cpu"""			)["""model"""]
							snake_case_          =						EfficientFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						EfficientFormerForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						"""_""".join(checkpoint_path.split("""/"""			)[-1].split("""."""			)[0].split("""_"""			)[:-1]			)

							snake_case_          =						config.depths[-1] - config.num_metaad_blocks + 1
							snake_case_          =						convert_torch_checkpoint(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							model.load_state_dict(_SCREAMING_SNAKE_CASE			)
							model.eval()

							snake_case_          =						{
							    """bilinear""": PILImageResampling.BILINEAR,
							    """bicubic""": PILImageResampling.BICUBIC,
							    """nearest""": PILImageResampling.NEAREST,
							}

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

							# original processing pipeline
							snake_case_          =						Compose(
							    [
							        Resize(_SCREAMING_SNAKE_CASE			,	interpolation=pillow_resamplings["""bicubic"""]			),
							        CenterCrop(_SCREAMING_SNAKE_CASE			),
							        ToTensor(),
							        Normalize(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			),
							    ]			)
							snake_case_          =						image_transforms(_SCREAMING_SNAKE_CASE			).unsqueeze(0			)

							assert torch.allclose(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							snake_case_          =						model(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						outputs.logits

							snake_case_          =						(1, 1_000)

							if "l1" in model_name:
														snake_case_          =						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:
														snake_case_          =						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:
														snake_case_          =						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__":
				__SCREAMING_SNAKE_CASE							:      List[str]						=      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)

				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      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 gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      CycleDiffusionPipeline
							__lowercase:						int							 =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    """negative_prompt""",
							    """height""",
							    """width""",
							    """negative_prompt_embeds""",
							}
							__lowercase:						str							 =      PipelineTesterMixin.required_optional_params - {"""latents"""}
							__lowercase:						Any							 =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS



							def 						lowerCAmelCase    (			self							: Dict    )   ->Any:



														"""simple docstring"""



														torch.manual_seed(0    )
														snake_case_          =						UNetaDConditionModel(
														    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,    cross_attention_dim=32						,    )
														snake_case_          =						DDIMScheduler(
														    beta_start=0.00_085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    num_train_timesteps=1_000						,    clip_sample=UpperCAmelCase_						,    set_alpha_to_one=UpperCAmelCase_						,    )
														torch.manual_seed(0    )
														snake_case_          =						AutoencoderKL(
														    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,    latent_channels=4						,    )
														torch.manual_seed(0    )
														snake_case_          =						CLIPTextConfig(
														    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1_000						,    )
														snake_case_          =						CLIPTextModel(UpperCAmelCase_    )
														snake_case_          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

														snake_case_          =						{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """safety_checker""": None,
														    """feature_extractor""": None,
														}
														return components



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: List[str]=0    )   ->str:



														"""simple docstring"""



														snake_case_          =						floats_tensor((1, 3, 32, 32)						,    rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
														snake_case_          =						image / 2 + 0.5
														if str(UpperCAmelCase_    ).startswith("""mps"""    ):
																					snake_case_          =						torch.manual_seed(UpperCAmelCase_    )
														else:
																					snake_case_          =						torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
														snake_case_          =						{
														    """prompt""": """An astronaut riding an elephant""",
														    """source_prompt""": """An astronaut riding a horse""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """eta""": 0.1,
														    """strength""": 0.8,
														    """guidance_scale""": 3,
														    """source_guidance_scale""": 1,
														    """output_type""": """numpy""",
														}
														return inputs



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														snake_case_          =						self.get_dummy_components()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]    )

														assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2



							@unittest.skipIf(torch_device != """cuda"""						,    """This test requires a GPU"""    )
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.get_dummy_components()
														for name, module in components.items():
																					if hasattr(UpperCAmelCase_						,    """half"""    ):
																												snake_case_          =						module.half()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]    )

														assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_save_load_local()



							@unittest.skip("""non-deterministic pipeline"""    )
							def 						lowerCAmelCase    (			self							: int    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_inference_batch_single_identical()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Any    )   ->Tuple:



														"""simple docstring"""



														return super().test_save_load_optional_components()



							@skip_mps
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->str:



														"""simple docstring"""



														return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Optional[int]:



														"""simple docstring"""



														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(
														    UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_						,    torch_dtype=torch.floataa						,    revision="""fp16"""    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														# the values aren't exactly equal, but the images look the same visually
														assert np.abs(image - expected_image    ).max() < 5E-1



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														assert np.abs(image - expected_image    ).max() < 2E-2



