

"""simple docstring"""






import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    SwinConfig()
    _lowerCAmelCase       :   Union[str, Any]  =    swin_name.split('_'   )

    _lowerCAmelCase       :   Dict  =    name_split[1]
    _lowerCAmelCase       :   Optional[Any]  =    int(name_split[4]   )
    _lowerCAmelCase       :   Union[str, Any]  =    int(name_split[3][-1]   )

    if model_size == "tiny":
        _lowerCAmelCase       :   str  =    96
        _lowerCAmelCase       :   List[Any]  =    (2, 2, 6, 2)
        _lowerCAmelCase       :   str  =    (3, 6, 12, 24)
    elif model_size == "small":
        _lowerCAmelCase       :   Optional[Any]  =    96
        _lowerCAmelCase       :   Dict  =    (2, 2, 18, 2)
        _lowerCAmelCase       :   Tuple  =    (3, 6, 12, 24)
    elif model_size == "base":
        _lowerCAmelCase       :   Union[str, Any]  =    128
        _lowerCAmelCase       :   List[str]  =    (2, 2, 18, 2)
        _lowerCAmelCase       :   int  =    (4, 8, 16, 32)
    else:
        _lowerCAmelCase       :   Any  =    192
        _lowerCAmelCase       :   str  =    (2, 2, 18, 2)
        _lowerCAmelCase       :   Any  =    (6, 12, 24, 48)

    if "in22k" in swin_name:
        _lowerCAmelCase       :   Dict  =    21841
    else:
        _lowerCAmelCase       :   Optional[int]  =    1000
        _lowerCAmelCase       :   Dict  =    'huggingface/label-files'
        _lowerCAmelCase       :   Any  =    'imagenet-1k-id2label.json'
        _lowerCAmelCase       :   int  =    json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type='dataset'   ) ,   'r'   )   )
        _lowerCAmelCase       :   Dict  =    {int(_lowerCamelCase   ): v for k, v in idalabel.items()}
        _lowerCAmelCase       :   Dict  =    idalabel
        _lowerCAmelCase       :   Dict  =    {v: k for k, v in idalabel.items()}

    _lowerCAmelCase       :   Any  =    img_size
    _lowerCAmelCase       :   Dict  =    num_classes
    _lowerCAmelCase       :   List[Any]  =    embed_dim
    _lowerCAmelCase       :   Dict  =    depths
    _lowerCAmelCase       :   Optional[Any]  =    num_heads
    _lowerCAmelCase       :   Tuple  =    window_size

    return config

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if "patch_embed.proj" in name:
        _lowerCAmelCase       :   Optional[Any]  =    name.replace('patch_embed.proj' ,   'embeddings.patch_embeddings.projection'   )
    if "patch_embed.norm" in name:
        _lowerCAmelCase       :   Optional[Any]  =    name.replace('patch_embed.norm' ,   'embeddings.norm'   )
    if "layers" in name:
        _lowerCAmelCase       :   List[Any]  =    'encoder.' + name
    if "attn.proj" in name:
        _lowerCAmelCase       :   Dict  =    name.replace('attn.proj' ,   'attention.output.dense'   )
    if "attn" in name:
        _lowerCAmelCase       :   Any  =    name.replace('attn' ,   'attention.self'   )
    if "norm1" in name:
        _lowerCAmelCase       :   Optional[Any]  =    name.replace('norm1' ,   'layernorm_before'   )
    if "norm2" in name:
        _lowerCAmelCase       :   int  =    name.replace('norm2' ,   'layernorm_after'   )
    if "mlp.fc1" in name:
        _lowerCAmelCase       :   List[Any]  =    name.replace('mlp.fc1' ,   'intermediate.dense'   )
    if "mlp.fc2" in name:
        _lowerCAmelCase       :   Tuple  =    name.replace('mlp.fc2' ,   'output.dense'   )

    if name == "norm.weight":
        _lowerCAmelCase       :   Any  =    'layernorm.weight'
    if name == "norm.bias":
        _lowerCAmelCase       :   Union[str, Any]  =    'layernorm.bias'

    if "head" in name:
        _lowerCAmelCase       :   int  =    name.replace('head' ,   'classifier'   )
    else:
        _lowerCAmelCase       :   str  =    'swin.' + name

    return name

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    for key in orig_state_dict.copy().keys():
        _lowerCAmelCase       :   Any  =    orig_state_dict.pop(_lowerCamelCase   )

        if "mask" in key:
            continue
        elif "qkv" in key:
            _lowerCAmelCase       :   str  =    key.split('.'   )
            _lowerCAmelCase       :   List[str]  =    int(key_split[1]   )
            _lowerCAmelCase       :   Union[str, Any]  =    int(key_split[3]   )
            _lowerCAmelCase       :   Optional[int]  =    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                _lowerCAmelCase       :   str  =    val[:dim, :]
                _lowerCAmelCase       :   int  =    val[
                    dim : dim * 2, :
                ]
                _lowerCAmelCase       :   str  =    val[-dim:, :]
            else:
                _lowerCAmelCase       :   Any  =    val[
                    :dim
                ]
                _lowerCAmelCase       :   Union[str, Any]  =    val[
                    dim : dim * 2
                ]
                _lowerCAmelCase       :   str  =    val[
                    -dim:
                ]
        else:
            _lowerCAmelCase       :   List[Any]  =    val

    return orig_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    timm.create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase   )
    timm_model.eval()

    _lowerCAmelCase       :   Optional[int]  =    get_swin_config(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    SwinForImageClassification(_lowerCamelCase   )
    model.eval()

    _lowerCAmelCase       :   List[str]  =    convert_state_dict(timm_model.state_dict() ,   _lowerCamelCase   )
    model.load_state_dict(_lowerCamelCase   )

    _lowerCAmelCase       :   List[Any]  =    'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowerCAmelCase       :   Dict  =    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' ,   '-'   )   )   )
    _lowerCAmelCase       :   List[Any]  =    Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase   ).raw   )
    _lowerCAmelCase       :   Optional[int]  =    image_processor(images=_lowerCamelCase ,   return_tensors='pt'   )

    _lowerCAmelCase       :   List[Any]  =    timm_model(inputs['pixel_values']   )
    _lowerCAmelCase       :   int  =    model(**_lowerCamelCase   ).logits

    assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1e-3   )

    print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(_lowerCamelCase   )

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
    image_processor.save_pretrained(_lowerCamelCase   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swin_name""",
     default="""swin_tiny_patch4_window7_224""",
     type=str,
     help="""Name of the Swin timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )

 _lowerCAmelCase						=		parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		(UniPCMultistepScheduler,)
  _UpperCAmelCase          =		(("num_inference_steps", 25),)

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_A      )
      return config

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   int  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Union[str, Any]  =    0.1 * sample
      _lowerCAmelCase       :   Tuple  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Optional[int]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )
          # copy over dummy past residuals
          _lowerCAmelCase       :   Tuple  =    dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class.from_pretrained(_A      )
              new_scheduler.set_timesteps(_A      )
              # copy over dummy past residuals
              _lowerCAmelCase       :   Union[str, Any]  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase,					_lowerCAmelCase       :   str  =    sample, sample
          for t in range(_A    ,time_step + scheduler.config.solver_order + 1      ):
              _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
              _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   List[str]  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Dict  =    0.1 * sample
      _lowerCAmelCase       :   str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Any  =    self.get_scheduler_config()
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )

          # copy over dummy past residuals (must be after setting timesteps)
          _lowerCAmelCase       :   List[str]  =    dummy_past_residuals[: scheduler.config.solver_order]

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

              # copy over dummy past residual (must be after setting timesteps)
              _lowerCAmelCase       :   str  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if scheduler is None:
          _lowerCAmelCase       :   int  =    self.scheduler_classes[0]
          _lowerCAmelCase       :   List[str]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
      _lowerCAmelCase       :   int  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   List[str]  =    10
      _lowerCAmelCase       :   str  =    self.dummy_model()
      _lowerCAmelCase       :   str  =    self.dummy_sample_deter
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Any  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      return sample

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    dict(self.forward_default_kwargs      )

      _lowerCAmelCase       :   Any  =    kwargs.pop('num_inference_steps'    ,_A      )

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   str  =    self.get_scheduler_config()
          _lowerCAmelCase       :   List[str]  =    scheduler_class(**_A      )

          _lowerCAmelCase       :   Any  =    self.dummy_sample
          _lowerCAmelCase       :   Tuple  =    0.1 * sample

          if num_inference_steps is not None and hasattr(_A    ,'set_timesteps'      ):
              scheduler.set_timesteps(_A      )
          elif num_inference_steps is not None and not hasattr(_A    ,'set_timesteps'      ):
              _lowerCAmelCase       :   Optional[Any]  =    num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _lowerCAmelCase       :   Optional[Any]  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          _lowerCAmelCase       :   Optional[Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

          _lowerCAmelCase       :   Any  =    scheduler.timesteps[5]
          _lowerCAmelCase       :   List[str]  =    scheduler.timesteps[6]

          _lowerCAmelCase       :   List[str]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    UniPCMultistepScheduler(**self.get_scheduler_config()      )
      _lowerCAmelCase       :   Optional[Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

      _lowerCAmelCase       :   int  =    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   List[str]  =    DEISMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Tuple  =    DPMSolverMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Any  =    UniPCMultistepScheduler.from_config(scheduler.config      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   List[str]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for timesteps in [25, 50, 100, 999, 1000]:
          self.check_over_configs(num_train_timesteps=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(thresholding=_A      )
      for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=_A    ,prediction_type=_A    ,sample_max_value=_A    ,solver_order=_A    ,solver_type=_A    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  _lowerCAmelCase       :   List[Any]  =    self.full_loop(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  assert not torch.isnan(_A      ).any(), "Samples have nan numbers"

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(lower_order_final=_A      )
      self.check_over_configs(lower_order_final=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
          self.check_over_forward(num_inference_steps=_A    ,time_step=0      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.full_loop()
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.full_loop(prediction_type='v_prediction'      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.1_0_1_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   int  =    self.get_scheduler_config(thresholding=_A    ,dynamic_thresholding_ratio=0      )
      _lowerCAmelCase       :   Tuple  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    10
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_model()
      _lowerCAmelCase       :   Dict  =    self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Tuple  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      assert sample.dtype == torch.floataa







  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   str  =    scheduler_class(**_A      )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
          assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps





