
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase											=    logging.get_logger(__name__)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     str				)			->						int:
       SCREAMING_SNAKE_CASE_							:				str		=original_name.split('''.'''				)[0]
       SCREAMING_SNAKE_CASE_							:				Tuple		=key.split('''.'''				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=int(key_list[key_list.index(UpperCAmelCase_				) - 2]				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=int(key_list[key_list.index(UpperCAmelCase_				) - 1]				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=orig_block_num - offset

       SCREAMING_SNAKE_CASE_							:				str		=key.replace(f'{orig_block_num}.{layer_num}.{original_name}'			,							f'block.{new_block_num}.{layer_num}.{new_name}'				)
       return key
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						Optional[Any]:
       SCREAMING_SNAKE_CASE_							:				int		=OrderedDict()
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=0, 0
       for key, value in state_dict.items():
              if key.startswith('''network'''				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=key.replace('''network'''			,							'''poolformer.encoder'''				)
              if "proj" in key:
                     # Works for the first embedding as well as the internal embedding layers
                     if key.endswith('''bias'''				) and "patch_embed" not in key:
                            patch_emb_offset += 1
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=key[: key.find('''proj'''				)]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=key.replace(UpperCAmelCase_			,							f'patch_embeddings.{total_embed_found}.'				)
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=key.replace('''proj'''			,							'''projection'''				)
                     if key.endswith('''bias'''				):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     SCREAMING_SNAKE_CASE_							:				int		='''poolformer.encoder.''' + key
              if "mlp.fc1" in key:
                     SCREAMING_SNAKE_CASE_							:				Any		=replace_key_with_offset(UpperCAmelCase_			,							UpperCAmelCase_			,							'''mlp.fc1'''			,							'''output.conv1'''				)
              if "mlp.fc2" in key:
                     SCREAMING_SNAKE_CASE_							:				int		=replace_key_with_offset(UpperCAmelCase_			,							UpperCAmelCase_			,							'''mlp.fc2'''			,							'''output.conv2'''				)
              if "norm1" in key:
                     SCREAMING_SNAKE_CASE_							:				Any		=replace_key_with_offset(UpperCAmelCase_			,							UpperCAmelCase_			,							'''norm1'''			,							'''before_norm'''				)
              if "norm2" in key:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=replace_key_with_offset(UpperCAmelCase_			,							UpperCAmelCase_			,							'''norm2'''			,							'''after_norm'''				)
              if "layer_scale_1" in key:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=replace_key_with_offset(UpperCAmelCase_			,							UpperCAmelCase_			,							'''layer_scale_1'''			,							'''layer_scale_1'''				)
              if "layer_scale_2" in key:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=replace_key_with_offset(UpperCAmelCase_			,							UpperCAmelCase_			,							'''layer_scale_2'''			,							'''layer_scale_2'''				)
              if "head" in key:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=key.replace('''head'''			,							'''classifier'''				)
              SCREAMING_SNAKE_CASE_							:				str		=value
       return new_state_dict
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[Any]:
       SCREAMING_SNAKE_CASE_							:				Tuple		='''http://images.cocodataset.org/val2017/000000039769.jpg'''
       SCREAMING_SNAKE_CASE_							:				List[str]		=Image.open(requests.get(UpperCAmelCase_			,							stream=UpperCAmelCase_				).raw				)

       return image
@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Optional[int]				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=PoolFormerConfig()

       # set attributes based on model_name
       SCREAMING_SNAKE_CASE_							:				int		='''huggingface/label-files'''
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=model_name[-3:]
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=1_0_0_0
       SCREAMING_SNAKE_CASE_							:				List[Any]		='''imagenet-1k-id2label.json'''
       SCREAMING_SNAKE_CASE_							:				int		=(1, 1_0_0_0)

       # set config attributes
       SCREAMING_SNAKE_CASE_							:				int		=json.load(open(hf_hub_download(UpperCAmelCase_			,							UpperCAmelCase_			,							repo_type='''dataset'''				)			,							'''r'''				)				)
       SCREAMING_SNAKE_CASE_							:				int		={int(UpperCAmelCase_				): v for k, v in idalabel.items()}
       SCREAMING_SNAKE_CASE_							:				List[Any]		=idalabel
       SCREAMING_SNAKE_CASE_							:				Optional[int]		={v: k for k, v in idalabel.items()}
       if size == "s12":
              SCREAMING_SNAKE_CASE_							:				Dict		=[2, 2, 6, 2]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[6_4, 1_2_8, 3_2_0, 5_1_2]
              SCREAMING_SNAKE_CASE_							:				Tuple		=4.0
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=0.9
       elif size == "s24":
              SCREAMING_SNAKE_CASE_							:				Dict		=[4, 4, 1_2, 4]
              SCREAMING_SNAKE_CASE_							:				str		=[6_4, 1_2_8, 3_2_0, 5_1_2]
              SCREAMING_SNAKE_CASE_							:				List[Any]		=4.0
              SCREAMING_SNAKE_CASE_							:				Tuple		=0.9
       elif size == "s36":
              SCREAMING_SNAKE_CASE_							:				Dict		=[6, 6, 1_8, 6]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[6_4, 1_2_8, 3_2_0, 5_1_2]
              SCREAMING_SNAKE_CASE_							:				List[Any]		=4.0
              SCREAMING_SNAKE_CASE_							:				List[str]		=1E-6
              SCREAMING_SNAKE_CASE_							:				Any		=0.9
       elif size == "m36":
              SCREAMING_SNAKE_CASE_							:				Tuple		=[6, 6, 1_8, 6]
              SCREAMING_SNAKE_CASE_							:				Any		=[9_6, 1_9_2, 3_8_4, 7_6_8]
              SCREAMING_SNAKE_CASE_							:				str		=4.0
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=1E-6
              SCREAMING_SNAKE_CASE_							:				Dict		=0.95
       elif size == "m48":
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[8, 8, 2_4, 8]
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[9_6, 1_9_2, 3_8_4, 7_6_8]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=4.0
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=1E-6
              SCREAMING_SNAKE_CASE_							:				str		=0.95
       else:
              raise ValueError(f'Size {size} not supported'				)

       # load image processor
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=PoolFormerImageProcessor(crop_pct=UpperCAmelCase_				)

       # Prepare image
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=prepare_img()
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image_processor(images=UpperCAmelCase_			,							return_tensors='''pt'''				).pixel_values

       logger.info(f'Converting model {model_name}...'				)

       # load original state dict
       SCREAMING_SNAKE_CASE_							:				Any		=torch.load(UpperCAmelCase_			,							map_location=torch.device('''cpu'''				)				)

       # rename keys
       SCREAMING_SNAKE_CASE_							:				Dict		=rename_keys(UpperCAmelCase_				)

       # create HuggingFace model and load state dict
       SCREAMING_SNAKE_CASE_							:				Dict		=PoolFormerForImageClassification(UpperCAmelCase_				)
       model.load_state_dict(UpperCAmelCase_				)
       model.eval()

       # Define image processor
       SCREAMING_SNAKE_CASE_							:				int		=PoolFormerImageProcessor(crop_pct=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=image_processor(images=prepare_img()			,							return_tensors='''pt'''				).pixel_values

       # forward pass
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([-0.3_045, -0.6_758, -0.4_869]				)
       elif size == "s24":
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.tensor([0.4_402, -0.1_374, -0.8_045]				)
       elif size == "s36":
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor([-0.6_080, -0.5_133, -0.5_898]				)
       elif size == "m36":
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([0.3_952, 0.2_263, -1.2_668]				)
       elif size == "m48":
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([0.1_167, -0.0_656, -0.3_423]				)
       else:
              raise ValueError(f'Size {size} not supported'				)

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]			,							UpperCAmelCase_			,							atol=1E-2				)

       # finally, save model and image processor
       logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'				)
       Path(UpperCAmelCase_				).mkdir(exist_ok=UpperCAmelCase_				)
       model.save_pretrained(UpperCAmelCase_				)
       print(f'Saving image processor to {pytorch_dump_folder_path}'				)
       image_processor.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
 _lowercase											=    argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     default="""poolformer_s12""",
     type=str,
     help="""Name of the model you'd like to convert.""",
 )
 parser.add_argument(
     """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 _lowercase											=    parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config(steps_offset=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A				)
              scheduler.set_timesteps(5				)
              assert torch.equal(scheduler.timesteps						,       torch.LongTensor([801, 601, 401, 201, 1]				)				)


       def 	_snake_case				(    self				)						->    Dict:
              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,       [0.002, 0.02, 0.2, 2]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    str:
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__A				)


       def 	_snake_case				(    self				)						->    int:
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.check_over_configs(thresholding=__A				)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__A						,       prediction_type=__A						,       sample_max_value=__A						,       )


       def 	_snake_case				(    self				)						->    Dict:
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=__A				)


       def 	_snake_case				(    self				)						->    Dict:
              for t, num_inference_steps in zip([1, 10, 50]						,       [10, 50, 500]				):
                     self.check_over_forward(time_step=__A						,       num_inference_steps=__A				)


       def 	_snake_case				(    self				)						->    int:
              for t, eta in zip([1, 10, 49]						,       [0.0, 0.5, 1.0]				):
                     self.check_over_forward(time_step=__A						,       eta=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(420						,       400				) - 0.14_771				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(980						,       960				) - 0.32_460				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						,       486				) - 0.00_979				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						,       998				) - 0.02				)				) < 1e-5


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
              assert abs(result_mean.item() - 0.4_982				) < 1e-3


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 172.0_067				) < 1e-2
              assert abs(result_mean.item() - 0.223_967				) < 1e-3


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 52.5_302				) < 1e-2
              assert abs(result_mean.item() - 0.0_684				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.8_295				) < 1e-2
              assert abs(result_mean.item() - 0.1_951				) < 1e-3





       def 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.0_784				) < 1e-2
              assert abs(result_mean.item() - 0.1_941				) < 1e-3
