
'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 			UpperCAmelCase__    (							A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		ConsistencyModelPipeline
    lowerCAmelCase_					     =		UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    lowerCAmelCase_					     =		UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

    # Override required_optional_params to remove num_images_per_prompt
    lowerCAmelCase_					     =		frozenset(
        [
            'num_inference_steps',
            'generator',
            'latents',
            'output_type',
            'return_dict',
            'callback',
            'callback_steps',
        ]			)



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: str		    = UNetaDModel.from_pretrained(
              "diffusers/consistency-models-test",subfolder="test_unet",)
          return unet



    @property
    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: List[str]		    = UNetaDModel.from_pretrained(
              "diffusers/consistency-models-test",subfolder="test_unet_class_cond",)
          return unet



    def 	lowerCamelCase_	(						self   :							Any,__A   :							List[Any]=False  ):
          if class_cond:
                _lowerCamelCase					: str		    = self.dummy_cond_unet
          else:
                _lowerCamelCase					: int		    = self.dummy_uncond_unet

          # Default to CM multistep sampler
          _lowerCamelCase					: int		    = CMStochasticIterativeScheduler(
              num_train_timesteps=4_0,sigma_min=0.002,sigma_max=80.0,)

          _lowerCamelCase					: List[str]		    = {
              "unet": unet,
              "scheduler": scheduler,
          }

          return components



    def 	lowerCamelCase_	(						self   :							Optional[Any],__A   :							int,__A   :							Dict=0  ):
          if str(a_  ).startswith("mps"  ):
                _lowerCamelCase					: Union[str, Any]		    = torch.manual_seed(a_  )
          else:
                _lowerCamelCase					: str		    = torch.Generator(device=a_  ).manual_seed(a_  )

          _lowerCamelCase					: str		    = {
              "batch_size": 1,
              "num_inference_steps": None,
              "timesteps": [2_2, 0],
              "generator": generator,
              "output_type": "np",
          }

          return inputs



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: List[Any]		    = "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase					: List[Any]		    = self.get_dummy_components()
          _lowerCamelCase					: List[Any]		    = ConsistencyModelPipeline(**a_  )
          _lowerCamelCase					: Dict		    = pipe.to(a_  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: Any		    = self.get_dummy_inputs(a_  )
          _lowerCamelCase					: Optional[int]		    = pipe(**a_  ).images
          assert image.shape == (1, 3_2, 3_2, 3)

          _lowerCamelCase					: Optional[Any]		    = image[0, -3:, -3:, -1]
          _lowerCamelCase					: int		    = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]  )

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



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: int		    = "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase					: List[str]		    = self.get_dummy_components(class_cond=a_  )
          _lowerCamelCase					: Any		    = ConsistencyModelPipeline(**a_  )
          _lowerCamelCase					: Union[str, Any]		    = pipe.to(a_  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: List[Any]		    = self.get_dummy_inputs(a_  )
          _lowerCamelCase					: Tuple		    = 0
          _lowerCamelCase					: Dict		    = pipe(**a_  ).images
          assert image.shape == (1, 3_2, 3_2, 3)

          _lowerCamelCase					: List[Any]		    = image[0, -3:, -3:, -1]
          _lowerCamelCase					: List[Any]		    = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]  )

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



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase					: int		    = "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase					: str		    = self.get_dummy_components()
          _lowerCamelCase					: int		    = ConsistencyModelPipeline(**a_  )
          _lowerCamelCase					: int		    = pipe.to(a_  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: Union[str, Any]		    = self.get_dummy_inputs(a_  )
          _lowerCamelCase					: Tuple		    = 1
          _lowerCamelCase					: Optional[int]		    = None
          _lowerCamelCase					: Dict		    = pipe(**a_  ).images
          assert image.shape == (1, 3_2, 3_2, 3)

          _lowerCamelCase					: Any		    = image[0, -3:, -3:, -1]
          _lowerCamelCase					: Dict		    = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]  )

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



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Optional[int]		    = "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase					: Tuple		    = self.get_dummy_components(class_cond=a_  )
          _lowerCamelCase					: List[str]		    = ConsistencyModelPipeline(**a_  )
          _lowerCamelCase					: str		    = pipe.to(a_  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: Tuple		    = self.get_dummy_inputs(a_  )
          _lowerCamelCase					: Optional[int]		    = 1
          _lowerCamelCase					: Union[str, Any]		    = None
          _lowerCamelCase					: List[Any]		    = 0
          _lowerCamelCase					: int		    = pipe(**a_  ).images
          assert image.shape == (1, 3_2, 3_2, 3)

          _lowerCamelCase					: Union[str, Any]		    = image[0, -3:, -3:, -1]
          _lowerCamelCase					: List[Any]		    = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]  )

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


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



    def 	lowerCamelCase_	(						self   :							str  ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							Dict=0,__A   :							Dict=False,__A   :							Tuple="cpu",__A   :							Union[str, Any]=torch.floataa,__A   :							str=(1, 3, 6_4, 6_4)  ):
          _lowerCamelCase					: str		    = torch.manual_seed(a_  )

          _lowerCamelCase					: Dict		    = {
              "num_inference_steps": None,
              "timesteps": [2_2, 0],
              "class_labels": 0,
              "generator": generator,
              "output_type": "np",
          }

          if get_fixed_latents:
                _lowerCamelCase					: Any		    = self.get_fixed_latents(seed=a_,device=a_,dtype=a_,shape=a_  )
                _lowerCamelCase					: int		    = latents

          return inputs



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							Any=0,__A   :							Optional[int]="cpu",__A   :							int=torch.floataa,__A   :							Any=(1, 3, 6_4, 6_4)  ):
          if type(a_  ) == str:
                _lowerCamelCase					: Optional[int]		    = torch.device(a_  )
          _lowerCamelCase					: Optional[Any]		    = torch.Generator(device=a_  ).manual_seed(a_  )
          _lowerCamelCase					: Optional[Any]		    = randn_tensor(a_,generator=a_,device=a_,dtype=a_  )
          return latents



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Optional[int]		    = UNetaDModel.from_pretrained("diffusers/consistency_models",subfolder="diffusers_cd_imagenet64_l2"  )
          _lowerCamelCase					: int		    = CMStochasticIterativeScheduler(
              num_train_timesteps=4_0,sigma_min=0.002,sigma_max=80.0,)
          _lowerCamelCase					: Tuple		    = ConsistencyModelPipeline(unet=a_,scheduler=a_  )
          pipe.to(torch_device=a_  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: List[str]		    = self.get_inputs()
          _lowerCamelCase					: Optional[int]		    = pipe(**a_  ).images
          assert image.shape == (1, 6_4, 6_4, 3)

          _lowerCamelCase					: List[Any]		    = image[0, -3:, -3:, -1]

          _lowerCamelCase					: Tuple		    = np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]  )

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



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Dict		    = UNetaDModel.from_pretrained("diffusers/consistency_models",subfolder="diffusers_cd_imagenet64_l2"  )
          _lowerCamelCase					: Union[str, Any]		    = CMStochasticIterativeScheduler(
              num_train_timesteps=4_0,sigma_min=0.002,sigma_max=80.0,)
          _lowerCamelCase					: Optional[int]		    = ConsistencyModelPipeline(unet=a_,scheduler=a_  )
          pipe.to(torch_device=a_  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: List[Any]		    = self.get_inputs()
          _lowerCamelCase					: Optional[int]		    = 1
          _lowerCamelCase					: Optional[Any]		    = None
          _lowerCamelCase					: List[str]		    = pipe(**a_  ).images
          assert image.shape == (1, 6_4, 6_4, 3)

          _lowerCamelCase					: Optional[int]		    = image[0, -3:, -3:, -1]

          _lowerCamelCase					: List[Any]		    = np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]  )

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



    @require_torch_a
    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: int		    = UNetaDModel.from_pretrained("diffusers/consistency_models",subfolder="diffusers_cd_imagenet64_l2"  )
          _lowerCamelCase					: List[Any]		    = CMStochasticIterativeScheduler(
              num_train_timesteps=4_0,sigma_min=0.002,sigma_max=80.0,)
          _lowerCamelCase					: Optional[Any]		    = ConsistencyModelPipeline(unet=a_,scheduler=a_  )
          pipe.to(torch_device=a_,torch_dtype=torch.floataa  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: Tuple		    = self.get_inputs(get_fixed_latents=a_,device=a_  )
          # Ensure usage of flash attention in torch 2.0
          with sdp_kernel(enable_flash=a_,enable_math=a_,enable_mem_efficient=a_  ):
                _lowerCamelCase					: int		    = pipe(**a_  ).images
          assert image.shape == (1, 6_4, 6_4, 3)

          _lowerCamelCase					: str		    = image[0, -3:, -3:, -1]

          _lowerCamelCase					: Any		    = np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]  )

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



    @require_torch_a
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Optional[Any]		    = UNetaDModel.from_pretrained("diffusers/consistency_models",subfolder="diffusers_cd_imagenet64_l2"  )
          _lowerCamelCase					: int		    = CMStochasticIterativeScheduler(
              num_train_timesteps=4_0,sigma_min=0.002,sigma_max=80.0,)
          _lowerCamelCase					: List[Any]		    = ConsistencyModelPipeline(unet=a_,scheduler=a_  )
          pipe.to(torch_device=a_,torch_dtype=torch.floataa  )
          pipe.set_progress_bar_config(disable=a_  )

          _lowerCamelCase					: int		    = self.get_inputs(get_fixed_latents=a_,device=a_  )
          _lowerCamelCase					: List[str]		    = 1
          _lowerCamelCase					: Optional[int]		    = None
          # Ensure usage of flash attention in torch 2.0
          with sdp_kernel(enable_flash=a_,enable_math=a_,enable_mem_efficient=a_  ):
                _lowerCamelCase					: Optional[Any]		    = pipe(**a_  ).images
          assert image.shape == (1, 6_4, 6_4, 3)

          _lowerCamelCase					: Optional[int]		    = image[0, -3:, -3:, -1]

          _lowerCamelCase					: Optional[Any]		    = np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]  )

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



'''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  (       A							,A							)  ->				str:
	lowercase						:		Optional[int]   					=		old_name

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

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

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

			lowercase						:		Dict   					=		"last_stage." + trimmed_name

	elif "network" in old_name and re.search(r".\d."							,A							):
		lowercase						:		Union[str, Any]   					=		old_name.replace("network"							,"intermediate_stages"							)

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

	return new_name




def 	_A  (       A							,A							)  ->				Optional[Any]:
	for key in checkpoint.copy().keys():
		lowercase						:		List[str]   					=		checkpoint.pop(A							)
		lowercase						:		int   					=		val

	return checkpoint




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		str   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		Optional[Any]   					=		Image.open(requests.get(A							,stream=A							).raw							)

	return image




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		torch.load(A							,map_location="cpu"							)["model"]
	lowercase						:		int   					=		EfficientFormerConfig.from_json_file(A							)
	lowercase						:		Tuple   					=		EfficientFormerForImageClassificationWithTeacher(A							)
	lowercase						:		int   					=		"_".join(checkpoint_path.split("/"							)[-1].split("."							)[0].split("_"							)[:-1]							)

	lowercase						:		Optional[int]   					=		config.depths[-1] - config.num_metaad_blocks + 1
	lowercase						:		int   					=		convert_torch_checkpoint(A							,A							)

	model.load_state_dict(A							)
	model.eval()

	lowercase						:		List[Any]   					=		{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	lowercase						:		Tuple   					=		prepare_img()
	lowercase						:		Optional[int]   					=		2_5_6
	lowercase						:		str   					=		2_2_4
	lowercase						:		List[str]   					=		EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}							,crop_size={"height": crop_size, "width": crop_size}							,resample=pillow_resamplings["bicubic"]							,)
	lowercase						:		Union[str, Any]   					=		processor(images=A							,return_tensors="pt"							).pixel_values

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

	assert torch.allclose(A							,A							)

	lowercase						:		Union[str, Any]   					=		model(A							)
	lowercase						:		Any   					=		outputs.logits

	lowercase						:		List[str]   					=		(1, 1_0_0_0)

	if "l1" in model_name:
		lowercase						:		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, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		lowercase						:		List[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, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		lowercase						:		Optional[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   : Tuple             =				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   : 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,
					)


