'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

A_     :						List[str]    							=torch.device('''cpu''')



def snake_case_      (					)			->					str:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(_UpperCAmelCase	,   stream=_UpperCAmelCase).raw)
   return im



def snake_case_      (					__snake_case  :							Optional[int])			->					Any:


   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1])

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1])

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2])

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2])



def snake_case_      (					__snake_case  :							Optional[Any]	,   __snake_case  :							List[Any]	,   __snake_case  :							Optional[Any])			->					Optional[Any]:
   lowerCAmelCase_			=      dct.pop(_UpperCAmelCase)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							Union[str, Any])			->					Union[str, Any]:
   lowerCAmelCase_			=      []
   for k in state_dict.keys():
      lowerCAmelCase_			=      k
      if ".pwconv" in k:
         lowerCAmelCase_			=      k_new.replace('''.pwconv'''	,   '''.point_wise_conv''')
      if ".dwconv" in k:
         lowerCAmelCase_			=      k_new.replace('''.dwconv'''	,   '''.depth_wise_conv''')
      if ".Proj." in k:
         lowerCAmelCase_			=      k_new.replace('''.Proj.'''	,   '''.proj.''')
      if "patch_embed" in k_new:
         lowerCAmelCase_			=      k_new.replace('''patch_embed'''	,   '''swiftformer.patch_embed.patch_embedding''')
      if "network" in k_new:
         lowerCAmelCase_			=      k_new.split('''.''')
         if ls[2].isdigit():
            lowerCAmelCase_			=      '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:])
         else:
            lowerCAmelCase_			=      k_new.replace('''network'''	,   '''swiftformer.encoder.network''')
      rename_keys.append((k, k_new))
   return rename_keys



@torch.no_grad()
def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							Union[str, Any]	,   __snake_case  :							int)			->					Union[str, Any]:
   lowerCAmelCase_			=      SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   lowerCAmelCase_			=      1000
   lowerCAmelCase_			=      '''huggingface/label-files'''
   lowerCAmelCase_			=      '''imagenet-1k-id2label.json'''
   lowerCAmelCase_			=      json.load(open(hf_hub_download(_UpperCAmelCase	,   _UpperCAmelCase	,   repo_type='''dataset''')	,   '''r'''))
   lowerCAmelCase_			=      {int(_UpperCAmelCase): v for k, v in idalabel.items()}
   lowerCAmelCase_			=      idalabel
   lowerCAmelCase_			=      {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      lowerCAmelCase_			=      [3, 3, 6, 4]
      lowerCAmelCase_			=      [48, 56, 112, 220]

   elif swiftformer_name == "swiftformer_s":
      lowerCAmelCase_			=      [3, 3, 9, 6]
      lowerCAmelCase_			=      [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      lowerCAmelCase_			=      [4, 3, 10, 5]
      lowerCAmelCase_			=      [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      lowerCAmelCase_			=      [4, 4, 12, 6]
      lowerCAmelCase_			=      [64, 128, 320, 512]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith('''https'''):
         lowerCAmelCase_			=      torch.hub.load_state_dict_from_url(_UpperCAmelCase	,   map_location='''cpu'''	,   check_hash=_UpperCAmelCase)
      else:
         lowerCAmelCase_			=      torch.load(_UpperCAmelCase	,   map_location='''cpu''')
   lowerCAmelCase_			=      checkpoint

   lowerCAmelCase_			=      create_rename_keys(_UpperCAmelCase)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase)

   # load HuggingFace model
   lowerCAmelCase_			=      SwiftFormerForImageClassification(_UpperCAmelCase).eval()
   hf_model.load_state_dict(_UpperCAmelCase)

   # prepare test inputs
   lowerCAmelCase_			=      prepare_img()
   lowerCAmelCase_			=      ViTImageProcessor.from_pretrained('''preprocessor_config''')
   lowerCAmelCase_			=      processor(images=_UpperCAmelCase	,   return_tensors='''pt''')

   # compare outputs from both models
   lowerCAmelCase_			=      get_expected_output(_UpperCAmelCase)
   lowerCAmelCase_			=      hf_model(inputs['''pixel_values''']).logits

   assert hf_logits.shape == torch.Size([1, 1000])
   assert torch.allclose(hf_logits[0, 0:5]	,   _UpperCAmelCase	,   atol=1E-3)

   Path(_UpperCAmelCase).mkdir(exist_ok=_UpperCAmelCase)
   print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}''')
   hf_model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
 A_     :						List[Any]    							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--swiftformer_name''',
     default='''swiftformer_xs''',
     choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
     type=str,
     help='''Name of the SwiftFormer model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''./converted_outputs/''',
     type=str,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

 A_     :						List[str]    							=parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						int							=   (KDPMaDiscreteScheduler,)
  __lowerCAmelCase   :						Union[str, Any]							=   10


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       A_          =       {
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,[0.0002, 0.002, 0.02] ):
            self.check_over_configs(beta_start=__snake_case ,beta_end=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config(prediction_type='''v_prediction''' )
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
            assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
            assert abs(result_mean.item() - 0.0002 ) < 1E-3


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps ,device=__snake_case )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if str(__snake_case ).startswith('''cpu''' ):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3



