





'''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    (							_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Any	):
				A__    =      old_name

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

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

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

												A__    =      "last_stage." + trimmed_name

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

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

				return new_name





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[Any]	):




				for key in checkpoint.copy().keys():
								A__    =      checkpoint.pop(snake_case_	)
								A__    =      val

				return checkpoint





def 						UpperCamelCase    (							):
				A__    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
				A__    =      Image.open(requests.get(snake_case_						,	stream=snake_case_	).raw	)

				return image





def 						UpperCamelCase    (							_lowerCamelCase : Path						,	_lowerCamelCase : Path						,	_lowerCamelCase : Path						,	_lowerCamelCase : bool	):
				A__    =      torch.load(snake_case_						,	map_location="cpu"	)["model"]
				A__    =      EfficientFormerConfig.from_json_file(snake_case_	)
				A__    =      EfficientFormerForImageClassificationWithTeacher(snake_case_	)
				A__    =      "_".join(checkpoint_path.split("/"	)[-1].split("."	)[0].split("_"	)[:-1]	)

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

				model.load_state_dict(snake_case_	)
				model.eval()

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

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

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

				assert torch.allclose(snake_case_						,	snake_case_	)

				A__    =      model(snake_case_	)
				A__    =      outputs.logits

				A__    =      (1, 10_00)

				if "l1" in model_name:
								A__    =      torch.Tensor(
								    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]	)
								assert torch.allclose(logits[0, :10]						,	snake_case_						,	atol=1e-3	)
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								A__    =      torch.Tensor(
								    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]	)
								assert torch.allclose(logits[0, :10]						,	snake_case_						,	atol=1e-3	)
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								A__    =      torch.Tensor(
								    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]	)
								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(snake_case_	).mkdir(exist_ok=snake_case_	)
				model.save_pretrained(snake_case_	)
				print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"	)
				processor.save_pretrained(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=snake_case_						,	)
								processor.push_to_hub(
								    repo_id=F"Bearnardd/{pytorch_dump_path}"						,	commit_message="Add image processor"						,	use_temp_dir=snake_case_						,	)


if __name__ == "__main__":
		__lowerCAmelCase							:     List[Any]		     =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 tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)
         # copy over dummy past residuals
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             new_scheduler.set_timesteps(lowercase_							)
             # copy over dummy past residuals
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

         # copy over dummy past residuals (must be after setting timesteps)
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(lowercase_							)

             # copy over dummy past residual (must be after setting timesteps)
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
             scheduler.set_timesteps(lowercase_							)
         elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
             A__    =      num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     scheduler.set_timesteps(10							)
     assert torch.equal(
         scheduler.timesteps						,	torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

     for scheduler_class in self.scheduler_classes:
         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



