






"""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 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   old_name

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

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

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

									UpperCAmelCase_	:					str     	=   "last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."      ,A__		):
						UpperCAmelCase_	:					Any     	=   old_name.replace("network"      ,"intermediate_stages"		)

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

			return new_name

def 							snake_case (  A__      ,A__		):
			for key in checkpoint.copy().keys():
						UpperCAmelCase_	:					int     	=   checkpoint.pop(A__		)
						UpperCAmelCase_	:					Tuple     	=   val

			return checkpoint

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					int     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)

			return image

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[Any]     	=   torch.load(A__      ,map_location="cpu"		)["model"]
			UpperCAmelCase_	:					Any     	=   EfficientFormerConfig.from_json_file(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   EfficientFormerForImageClassificationWithTeacher(A__		)
			UpperCAmelCase_	:					Optional[int]     	=   "_".join(checkpoint_path.split("/"		)[-1].split("."		)[0].split("_"		)[:-1]		)

			UpperCAmelCase_	:					Optional[Any]     	=   config.depths[-1] - config.num_metaad_blocks + 1
			UpperCAmelCase_	:					List[str]     	=   convert_torch_checkpoint(A__      ,A__		)

			model.load_state_dict(A__		)
			model.eval()

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

			# prepare image
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					Tuple     	=   2_56
			UpperCAmelCase_	:					Any     	=   2_24
			UpperCAmelCase_	:					str     	=   EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size}      ,crop_size={"height": crop_size, "width": crop_size}      ,resample=pillow_resamplings["bicubic"]      ,)
			UpperCAmelCase_	:					List[str]     	=   processor(images=A__      ,return_tensors="pt"		).pixel_values

			# original processing pipeline
			UpperCAmelCase_	:					List[Any]     	=   Compose(
			    [
			        Resize(A__      ,interpolation=pillow_resamplings["bicubic"]		),
			        CenterCrop(A__		),
			        ToTensor(),
			        Normalize(A__      ,A__		),
			    ]		)
			UpperCAmelCase_	:					str     	=   image_transforms(A__		).unsqueeze(0		)

			assert torch.allclose(A__      ,A__		)

			UpperCAmelCase_	:					Optional[Any]     	=   model(A__		)
			UpperCAmelCase_	:					int     	=   outputs.logits

			UpperCAmelCase_	:					Tuple     	=   (1, 10_00)

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


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,
			)







import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()




class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[Any]										=						StableUnCLIPImgaImgPipeline
	_lowerCAmelCase     :		Any										=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	_lowerCAmelCase     :		List[Any]										=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_lowerCAmelCase     :		Dict										=						frozenset(
	    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowerCAmelCase     :		str										=						frozenset([] )


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	3_2
							__UpperCAmelCase			:							Union[str, Any]	    =	embedder_hidden_size

							# image encoding components

							__UpperCAmelCase			:							Union[str, Any]	    =	CLIPImageProcessor(crop_size=3_2				,     size=3_2)

							torch.manual_seed(0)
							__UpperCAmelCase			:							List[str]	    =	CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=lowercase__				,     projection_dim=lowercase__				,     num_hidden_layers=5				,     num_attention_heads=4				,     image_size=3_2				,     intermediate_size=3_7				,     patch_size=1				,     ))

							# regular denoising components

							torch.manual_seed(0)
							__UpperCAmelCase			:							List[Any]	    =	StableUnCLIPImageNormalizer(embedding_dim=lowercase__)
							__UpperCAmelCase			:							Any	    =	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

							torch.manual_seed(0)
							__UpperCAmelCase			:							Dict	    =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							torch.manual_seed(0)
							__UpperCAmelCase			:							int	    =	CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0				,     eos_token_id=2				,     hidden_size=lowercase__				,     projection_dim=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     ))

							torch.manual_seed(0)
							__UpperCAmelCase			:							Tuple	    =	UNetaDConditionModel(
							    sample_size=3_2				,     in_channels=4				,     out_channels=4				,     down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,     up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,     block_out_channels=(3_2, 6_4)				,     attention_head_dim=(2, 4)				,     class_embed_type='''projection'''				,     projection_class_embeddings_input_dim=embedder_projection_dim * 2				,     cross_attention_dim=lowercase__				,     layers_per_block=1				,     upcast_attention=lowercase__				,     use_linear_projection=lowercase__				,     )

							torch.manual_seed(0)
							__UpperCAmelCase			:							Optional[Any]	    =	DDIMScheduler(
							    beta_schedule='''scaled_linear'''				,     beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     prediction_type='''v_prediction'''				,     set_alpha_to_one=lowercase__				,     steps_offset=1				,     )

							torch.manual_seed(0)
							__UpperCAmelCase			:							Optional[Any]	    =	AutoencoderKL()

							__UpperCAmelCase			:							Optional[Any]	    =	{
							    # image encoding components
							    '''feature_extractor''': feature_extractor,
							    '''image_encoder''': image_encoder.eval(),
							    # image noising components
							    '''image_normalizer''': image_normalizer.eval(),
							    '''image_noising_scheduler''': image_noising_scheduler,
							    # regular denoising components
							    '''tokenizer''': tokenizer,
							    '''text_encoder''': text_encoder.eval(),
							    '''unet''': unet.eval(),
							    '''scheduler''': scheduler,
							    '''vae''': vae.eval(),
							}

							return components


	def  A(    self				,     lowercase__				,     lowercase__=0				,     lowercase__=True):
							if str(lowercase__).startswith('''mps'''):
													__UpperCAmelCase			:							Union[str, Any]	    =	torch.manual_seed(lowercase__)
							else:
													__UpperCAmelCase			:							int	    =	torch.Generator(device=lowercase__).manual_seed(lowercase__)

							__UpperCAmelCase			:							Dict	    =	floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(lowercase__)).to(lowercase__)

							if pil_image:
													__UpperCAmelCase			:							int	    =	input_image * 0.5 + 0.5
													__UpperCAmelCase			:							str	    =	input_image.clamp(0				,     1)
													__UpperCAmelCase			:							Optional[int]	    =	input_image.cpu().permute(0				,     2				,     3				,     1).float().numpy()
													__UpperCAmelCase			:							Union[str, Any]	    =	DiffusionPipeline.numpy_to_pil(lowercase__)[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}


	@skip_mps
	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							__UpperCAmelCase			:							Dict	    =	self.get_dummy_components()
							__UpperCAmelCase			:							List[Any]	    =	StableUnCLIPImgaImgPipeline(**lowercase__)
							__UpperCAmelCase			:							List[str]	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Dict	    =	self.get_dummy_inputs(lowercase__)
							inputs.update({'''image_embeds''': None})
							__UpperCAmelCase			:							Any	    =	sd_pipe(**lowercase__).images
							__UpperCAmelCase			:							Union[str, Any]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							__UpperCAmelCase			:							Union[str, Any]	    =	np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	torch_device in ['''cpu''', '''mps''']

							self._test_attention_slicing_forward_pass(test_max_difference=lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	torch_device in ['''cpu''', '''mps''']

							self._test_inference_batch_single_identical(test_max_difference=lowercase__)


	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()				,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,     )
	def  A(    self):
							self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase__)






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


	def  A(    self):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

							__UpperCAmelCase			:							List[str]	    =	load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

							__UpperCAmelCase			:							Optional[int]	    =	StableUnCLIPImgaImgPipeline.from_pretrained(
							    '''fusing/stable-unclip-2-1-l-img2img'''				,     torch_dtype=torch.floataa)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__UpperCAmelCase			:							Optional[Any]	    =	torch.Generator(device='''cpu''').manual_seed(0)
							__UpperCAmelCase			:							Any	    =	pipe(lowercase__				,     '''anime turle'''				,     generator=lowercase__				,     output_type='''np''')

							__UpperCAmelCase			:							Optional[Any]	    =	output.images[0]

							assert image.shape == (7_6_8, 7_6_8, 3)

							assert_mean_pixel_difference(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

							__UpperCAmelCase			:							Union[str, Any]	    =	load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

							__UpperCAmelCase			:							Union[str, Any]	    =	StableUnCLIPImgaImgPipeline.from_pretrained(
							    '''fusing/stable-unclip-2-1-h-img2img'''				,     torch_dtype=torch.floataa)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__UpperCAmelCase			:							Dict	    =	torch.Generator(device='''cpu''').manual_seed(0)
							__UpperCAmelCase			:							int	    =	pipe(lowercase__				,     '''anime turle'''				,     generator=lowercase__				,     output_type='''np''')

							__UpperCAmelCase			:							List[str]	    =	output.images[0]

							assert image.shape == (7_6_8, 7_6_8, 3)

							assert_mean_pixel_difference(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__UpperCAmelCase			:							Optional[int]	    =	StableUnCLIPImgaImgPipeline.from_pretrained(
							    '''fusing/stable-unclip-2-1-h-img2img'''				,     torch_dtype=torch.floataa)
							__UpperCAmelCase			:							Optional[int]	    =	pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__UpperCAmelCase			:							int	    =	pipe(
							    lowercase__				,     '''anime turtle'''				,     num_inference_steps=2				,     output_type='''np'''				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 1_0**9

