


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()
UpperCAmelCase_   :				Union[str, Any]						      = logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Union[str, Any]						,					__A				:    str						,					__A				:    List[Any]       )			->	Optional[int]:

       """simple docstring"""

       a_      :	int							=						original_name.split('.'       )[0]
       a_      :	List[Any]							=						key.split('.'       )
       a_      :	List[Any]							=						int(key_list[key_list.index(__A       ) - 2]       )
       a_      :	Dict							=						int(key_list[key_list.index(__A       ) - 1]       )
       a_      :	Optional[int]							=						orig_block_num - offset

       a_      :	Tuple							=						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_		(    __A				:    List[str]       )			->	Optional[int]:

       """simple docstring"""

       a_      :	Tuple							=						OrderedDict()
       a_							,	a_      :	Tuple							=						0, 0
       for key, value in state_dict.items():
              if key.startswith('network'       ):
                     a_      :	List[str]							=						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
                     a_      :	Optional[Any]							=						key[: key.find('proj'       )]
                     a_      :	Union[str, Any]							=						key.replace(__A						,					F"""patch_embeddings.{total_embed_found}."""       )
                     a_      :	Optional[Any]							=						key.replace('proj'						,					'projection'       )
                     if key.endswith('bias'       ):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     a_      :	List[str]							=						'poolformer.encoder.' + key
              if "mlp.fc1" in key:
                     a_      :	Union[str, Any]							=						replace_key_with_offset(__A						,					__A						,					'mlp.fc1'						,					'output.conv1'       )
              if "mlp.fc2" in key:
                     a_      :	List[str]							=						replace_key_with_offset(__A						,					__A						,					'mlp.fc2'						,					'output.conv2'       )
              if "norm1" in key:
                     a_      :	Optional[int]							=						replace_key_with_offset(__A						,					__A						,					'norm1'						,					'before_norm'       )
              if "norm2" in key:
                     a_      :	Any							=						replace_key_with_offset(__A						,					__A						,					'norm2'						,					'after_norm'       )
              if "layer_scale_1" in key:
                     a_      :	List[Any]							=						replace_key_with_offset(__A						,					__A						,					'layer_scale_1'						,					'layer_scale_1'       )
              if "layer_scale_2" in key:
                     a_      :	str							=						replace_key_with_offset(__A						,					__A						,					'layer_scale_2'						,					'layer_scale_2'       )
              if "head" in key:
                     a_      :	Optional[int]							=						key.replace('head'						,					'classifier'       )
              a_      :	str							=						value
       return new_state_dict







def  SCREAMING_SNAKE_CASE_		(    )			->	int:

       """simple docstring"""

       a_      :	Any							=						'http://images.cocodataset.org/val2017/000000039769.jpg'
       a_      :	Optional[int]							=						Image.open(requests.get(__A						,					stream=__A       ).raw       )

       return image







@torch.no_grad()
def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    List[str]						,					__A				:    Tuple       )			->	Tuple:

       """simple docstring"""

       a_      :	Union[str, Any]							=						PoolFormerConfig()

       # set attributes based on model_name
       a_      :	Union[str, Any]							=						'huggingface/label-files'
       a_      :	str							=						model_name[-3:]
       a_      :	List[str]							=						10_00
       a_      :	int							=						'imagenet-1k-id2label.json'
       a_      :	Union[str, Any]							=						(1, 10_00)

       # set config attributes
       a_      :	str							=						json.load(open(hf_hub_download(__A						,					__A						,					repo_type='dataset'       )						,					'r'       )       )
       a_      :	List[str]							=						{int(__A       ): v for k, v in idalabel.items()}
       a_      :	List[str]							=						idalabel
       a_      :	int							=						{v: k for k, v in idalabel.items()}
       if size == "s12":
              a_      :	Any							=						[2, 2, 6, 2]
              a_      :	List[str]							=						[64, 1_28, 3_20, 5_12]
              a_      :	Union[str, Any]							=						4.0
              a_      :	int							=						0.9
       elif size == "s24":
              a_      :	List[str]							=						[4, 4, 12, 4]
              a_      :	Optional[Any]							=						[64, 1_28, 3_20, 5_12]
              a_      :	Tuple							=						4.0
              a_      :	Optional[int]							=						0.9
       elif size == "s36":
              a_      :	Union[str, Any]							=						[6, 6, 18, 6]
              a_      :	Union[str, Any]							=						[64, 1_28, 3_20, 5_12]
              a_      :	int							=						4.0
              a_      :	List[Any]							=						1e-6
              a_      :	List[str]							=						0.9
       elif size == "m36":
              a_      :	Union[str, Any]							=						[6, 6, 18, 6]
              a_      :	Optional[Any]							=						[96, 1_92, 3_84, 7_68]
              a_      :	int							=						4.0
              a_      :	Any							=						1e-6
              a_      :	Dict							=						0.95
       elif size == "m48":
              a_      :	Union[str, Any]							=						[8, 8, 24, 8]
              a_      :	str							=						[96, 1_92, 3_84, 7_68]
              a_      :	Any							=						4.0
              a_      :	Any							=						1e-6
              a_      :	int							=						0.95
       else:
              raise ValueError(F"""Size {size} not supported"""       )

       # load image processor
       a_      :	int							=						PoolFormerImageProcessor(crop_pct=__A       )

       # Prepare image
       a_      :	Any							=						prepare_img()
       a_      :	Optional[int]							=						image_processor(images=__A						,					return_tensors='pt'       ).pixel_values

       logger.info(F"""Converting model {model_name}..."""       )

       # load original state dict
       a_      :	Union[str, Any]							=						torch.load(__A						,					map_location=torch.device('cpu'       )       )

       # rename keys
       a_      :	int							=						rename_keys(__A       )

       # create HuggingFace model and load state dict
       a_      :	int							=						PoolFormerForImageClassification(__A       )
       model.load_state_dict(__A       )
       model.eval()

       # Define image processor
       a_      :	Dict							=						PoolFormerImageProcessor(crop_pct=__A       )
       a_      :	Optional[Any]							=						image_processor(images=prepare_img()						,					return_tensors='pt'       ).pixel_values

       # forward pass
       a_      :	Optional[int]							=						model(__A       )
       a_      :	Union[str, Any]							=						outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              a_      :	str							=						torch.tensor([-0.3045, -0.6758, -0.4869]       )
       elif size == "s24":
              a_      :	Dict							=						torch.tensor([0.4402, -0.1374, -0.8045]       )
       elif size == "s36":
              a_      :	Dict							=						torch.tensor([-0.6080, -0.5133, -0.5898]       )
       elif size == "m36":
              a_      :	List[Any]							=						torch.tensor([0.3952, 0.2263, -1.2668]       )
       elif size == "m48":
              a_      :	Any							=						torch.tensor([0.1167, -0.0656, -0.3423]       )
       else:
              raise ValueError(F"""Size {size} not supported"""       )

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

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


if __name__ == "__main__":
   UpperCAmelCase_   :				Optional[int]						      = 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.'
   )
   UpperCAmelCase_   :				Tuple						      = 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 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		(DDIMParallelScheduler,)
      snake_case__       :  Union[str, Any]       =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Any							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						1_0, 0.0

             a_      :	str							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config(steps_offset=1  )
             a_      :	str							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )
             scheduler.set_timesteps(5  )
             assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,							[0.002, 0.02, 0.2, 2]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__  ,							prediction_type=SCREAMING_SNAKE_CASE__  ,							sample_max_value=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             for t in [1, 1_0, 4_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Union[str, Any]							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,							4_0_0  ) - 0.14771  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0  ) - 0.32460  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,							4_8_6  ) - 0.00979  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8  ) - 0.02  )  ) < 1E-5
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Dict							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config()
             a_      :	List[str]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	int							=						1_0, 0.0
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter
             a_      :	Optional[Any]							=						self.dummy_sample_deter + 0.1
             a_      :	Any							=						self.dummy_sample_deter - 0.1

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

             a_      :	int							=						model(samples.flatten(0  ,							1  )  ,							timesteps.flatten(0  ,							1  )  )
             a_      :	Optional[Any]							=						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__  ,							timesteps.flatten(0  ,							1  )  ,							samples.flatten(0  ,							1  )  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[int]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 1147.7904  ) < 1E-2
             assert abs(result_mean.item() - 0.4982  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	Tuple							=						self.full_loop()

             a_      :	List[str]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 172.0067  ) < 1E-2
             assert abs(result_mean.item() - 0.223967  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Optional[int]							=						self.full_loop(prediction_type='v_prediction'  )

             a_      :	str							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 52.5302  ) < 1E-2
             assert abs(result_mean.item() - 0.0684  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Dict							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.8295  ) < 1E-2
             assert abs(result_mean.item() - 0.1951  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Tuple							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	Dict							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.0784  ) < 1E-2
             assert abs(result_mean.item() - 0.1941  ) < 1E-3


