





'''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 						__a(SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   SwinConfig()
    _lowerCAmelCase						      =   swin_name.split("_"     )

    _lowerCAmelCase						      =   name_split[1]
    _lowerCAmelCase						      =   int(name_split[4]     )
    _lowerCAmelCase						      =   int(name_split[3][-1]     )

    if model_size == "tiny":
        _lowerCAmelCase						      =   96
        _lowerCAmelCase						      =   (2, 2, 6, 2)
        _lowerCAmelCase						      =   (3, 6, 12, 24)
    elif model_size == "small":
        _lowerCAmelCase						      =   96
        _lowerCAmelCase						      =   (2, 2, 18, 2)
        _lowerCAmelCase						      =   (3, 6, 12, 24)
    elif model_size == "base":
        _lowerCAmelCase						      =   128
        _lowerCAmelCase						      =   (2, 2, 18, 2)
        _lowerCAmelCase						      =   (4, 8, 16, 32)
    else:
        _lowerCAmelCase						      =   192
        _lowerCAmelCase						      =   (2, 2, 18, 2)
        _lowerCAmelCase						      =   (6, 12, 24, 48)

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

    _lowerCAmelCase						      =   img_size
    _lowerCAmelCase						      =   num_classes
    _lowerCAmelCase						      =   embed_dim
    _lowerCAmelCase						      =   depths
    _lowerCAmelCase						      =   num_heads
    _lowerCAmelCase						      =   window_size

    return config


def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]     ):



    '''simple docstring'''





    if "patch_embed.proj" in name:
        _lowerCAmelCase						      =   name.replace("patch_embed.proj"		,							"embeddings.patch_embeddings.projection"     )
    if "patch_embed.norm" in name:
        _lowerCAmelCase						      =   name.replace("patch_embed.norm"		,							"embeddings.norm"     )
    if "layers" in name:
        _lowerCAmelCase						      =   "encoder." + name
    if "attn.proj" in name:
        _lowerCAmelCase						      =   name.replace("attn.proj"		,							"attention.output.dense"     )
    if "attn" in name:
        _lowerCAmelCase						      =   name.replace("attn"		,							"attention.self"     )
    if "norm1" in name:
        _lowerCAmelCase						      =   name.replace("norm1"		,							"layernorm_before"     )
    if "norm2" in name:
        _lowerCAmelCase						      =   name.replace("norm2"		,							"layernorm_after"     )
    if "mlp.fc1" in name:
        _lowerCAmelCase						      =   name.replace("mlp.fc1"		,							"intermediate.dense"     )
    if "mlp.fc2" in name:
        _lowerCAmelCase						      =   name.replace("mlp.fc2"		,							"output.dense"     )

    if name == "norm.weight":
        _lowerCAmelCase						      =   "layernorm.weight"
    if name == "norm.bias":
        _lowerCAmelCase						      =   "layernorm.bias"

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

    return name


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





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

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

            if "weight" in key:
                _lowerCAmelCase						      =   val[:dim, :]
                _lowerCAmelCase						      =   val[
                    dim : dim * 2, :
                ]
                _lowerCAmelCase						      =   val[-dim:, :]
            else:
                _lowerCAmelCase						      =   val[
                    :dim
                ]
                _lowerCAmelCase						      =   val[
                    dim : dim * 2
                ]
                _lowerCAmelCase						      =   val[
                    -dim:
                ]
        else:
            _lowerCAmelCase						      =   val

    return orig_state_dict


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   timm.create_model(SCREAMING_SNAKE_CASE_		,							pretrained=SCREAMING_SNAKE_CASE_     )
    timm_model.eval()

    _lowerCAmelCase						      =   get_swin_config(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   SwinForImageClassification(SCREAMING_SNAKE_CASE_     )
    model.eval()

    _lowerCAmelCase						      =   convert_state_dict(timm_model.state_dict()		,							SCREAMING_SNAKE_CASE_     )
    model.load_state_dict(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   "http://images.cocodataset.org/val2017/000000039769.jpg"

    _lowerCAmelCase						      =   AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,							"-"     )     )     )
    _lowerCAmelCase						      =   Image.open(requests.get(SCREAMING_SNAKE_CASE_		,							stream=SCREAMING_SNAKE_CASE_     ).raw     )
    _lowerCAmelCase						      =   image_processor(images=SCREAMING_SNAKE_CASE_		,							return_tensors="pt"     )

    _lowerCAmelCase						      =   timm_model(inputs["pixel_values"]     )
    _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     ).logits

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

    print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''     )
    model.save_pretrained(SCREAMING_SNAKE_CASE_     )

    print(F'''Saving image processor to {pytorch_dump_folder_path}'''     )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				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."
       )

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









'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       (DDPMParallelScheduler,)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self		)     ->				List[Any]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     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=_lowerCAmelCase      ,  beta_end=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				int:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     for t in [0, 500, 999]:
         self.check_over_forward(time_step=_lowerCAmelCase		)




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

     assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   self.dummy_sample_deter + 0.1
     _lowerCAmelCase						      =   self.dummy_sample_deter - 0.1

     _lowerCAmelCase						      =   samplea.shape[0]
     _lowerCAmelCase						      =   torch.stack([samplea, samplea, samplea]      ,  dim=0		)
     _lowerCAmelCase						      =   torch.arange(_lowerCAmelCase		)[0:3, None].repeat(1      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   model(samples.flatten(0      ,  1		)      ,  timesteps.flatten(0      ,  1		)		)
     _lowerCAmelCase						      =   scheduler.batch_step_no_noise(_lowerCAmelCase      ,  timesteps.flatten(0      ,  1		)      ,  samples.flatten(0      ,  1		)		)

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 1153.1833		) < 1E-2
     assert abs(result_mean.item() - 0.5005		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   torch.manual_seed(0		)

     for t in reversed(range(_lowerCAmelCase		)		):
         # 1. predict noise residual
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)

         # 2. predict previous mean of sample x_t-1
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  generator=_lowerCAmelCase		).prev_sample

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 258.9606		) < 1E-2
     assert abs(result_mean.item() - 0.3372		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   torch.manual_seed(0		)

     for t in reversed(range(_lowerCAmelCase		)		):
         # 1. predict noise residual
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)

         # 2. predict previous mean of sample x_t-1
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  generator=_lowerCAmelCase		).prev_sample

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 202.0296		) < 1E-2
     assert abs(result_mean.item() - 0.2631		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=_lowerCAmelCase		)

     _lowerCAmelCase						      =   scheduler.timesteps

     for i, timestep in enumerate(_lowerCAmelCase		):
         if i == len(_lowerCAmelCase		) - 1:
             _lowerCAmelCase						      =   -1
         else:
             _lowerCAmelCase						      =   timesteps[i + 1]

         _lowerCAmelCase						      =   scheduler.previous_timestep(_lowerCAmelCase		)
         _lowerCAmelCase						      =   prev_t.item()

         self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 51, 0]

     with self.assertRaises(_lowerCAmelCase      ,  msg="`custom_timesteps` must be in descending order."		):
         scheduler.set_timesteps(timesteps=_lowerCAmelCase		)




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

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     with self.assertRaises(_lowerCAmelCase      ,  msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
         scheduler.set_timesteps(num_inference_steps=_lowerCAmelCase      ,  timesteps=_lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         _lowerCAmelCase      ,  msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"      ,  ):
         scheduler.set_timesteps(timesteps=_lowerCAmelCase		)


