
"""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			:       Any   		=       logging.get_logger(__name__)

_A			:       Union[str, Any]   		=       torch.device("""cpu""")


def   __magic_name__	(      ) ->				List[str]:
       lowercase    :				List[Any]       		=							"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase    :				Any       		=							Image.open(requests.get(__snake_case     , stream=__snake_case  ).raw  )
       return im


def   __magic_name__	(      __snake_case				:       str  ) ->				Optional[Any]:
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]  )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]  )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]  )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]  )


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       int     , __snake_case				:       List[str]  ) ->				int:
       lowercase    :				Union[str, Any]       		=							dct.pop(__snake_case  )
       lowercase    :				Optional[int]       		=							val


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


@torch.no_grad()
def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       int     , __snake_case				:       str  ) ->				Optional[int]:
       lowercase    :				Union[str, Any]       		=							SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       lowercase    :				Union[str, Any]       		=							1000
       lowercase    :				int       		=							"huggingface/label-files"
       lowercase    :				List[Any]       		=							"imagenet-1k-id2label.json"
       lowercase    :				List[str]       		=							json.load(open(hf_hub_download(__snake_case     , __snake_case     , repo_type="dataset"  )     , "r"  )  )
       lowercase    :				Union[str, Any]       		=							{int(__snake_case  ): v for k, v in idalabel.items()}
       lowercase    :				Optional[int]       		=							idalabel
       lowercase    :				Any       		=							{v: k for k, v in idalabel.items()}

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

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

       elif swiftformer_name == "swiftformer_l1":
              lowercase    :				Optional[int]       		=							[4, 3, 10, 5]
              lowercase    :				List[Any]       		=							[48, 96, 192, 384]

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

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith("https"  ):
                     lowercase    :				str       		=							torch.hub.load_state_dict_from_url(__snake_case     , map_location="cpu"     , check_hash=__snake_case  )
              else:
                     lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )
       lowercase    :				List[str]       		=							checkpoint

       lowercase    :				Dict       		=							create_rename_keys(__snake_case  )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(__snake_case     , __snake_case     , __snake_case  )

       # load HuggingFace model
       lowercase    :				Any       		=							SwiftFormerForImageClassification(__snake_case  ).eval()
       hf_model.load_state_dict(__snake_case  )

       # prepare test inputs
       lowercase    :				Optional[int]       		=							prepare_img()
       lowercase    :				Optional[int]       		=							ViTImageProcessor.from_pretrained("preprocessor_config"  )
       lowercase    :				List[str]       		=							processor(images=__snake_case     , return_tensors="pt"  )

       # compare outputs from both models
       lowercase    :				Union[str, Any]       		=							get_expected_output(__snake_case  )
       lowercase    :				int       		=							hf_model(inputs["pixel_values"]  ).logits

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

       Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
       print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""  )
       hf_model.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[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			:       Optional[Any]   		=       parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_A			:       int   		=       """CompVis/stable-diffusion-v1-1"""
_A			:       Any   		=       """CompVis/stable-diffusion-v1-2"""
_A			:       Optional[int]   		=       """CompVis/stable-diffusion-v1-3"""
_A			:       Union[str, Any]   		=       """CompVis/stable-diffusion-v1-4"""

class 		a__						(      a_						):
   def __init__(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a = True	,    ):
          super()._init_()

          lowercase    :				Optional[Any]       		=							StableDiffusionPipeline.from_pretrained(_a				)
          lowercase    :				str       		=							StableDiffusionPipeline.from_pretrained(_a				)
          lowercase    :				Dict       		=							StableDiffusionPipeline.from_pretrained(_a				)
          lowercase    :				Union[str, Any]       		=							StableDiffusionPipeline(
              vae=_a	,    text_encoder=_a	,    tokenizer=_a	,    unet=_a	,    scheduler=_a	,    safety_checker=_a	,    feature_extractor=_a	,    requires_safety_checker=_a	,    )

          self.register_modules(pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea				)
   @property
   def 			__magic_name__			(			self				):
          return {k: getattr(self	,    _a				) for k in self.config.keys() if not k.startswith("_"				)}
   def 			__magic_name__			(			self	,    _a = "auto"				):
          if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 lowercase    :				str       		=							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(_a				)
   def 			__magic_name__			(			self				):
          self.enable_attention_slicing(_a				)
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          lowercase    :				List[Any]       		=							"cuda" if torch.cuda.is_available() else "cpu"
          self.to(_a				)

          # Checks if the height and width are divisible by 8 or not
          if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}."""				)

          # Get first result from Stable Diffusion Checkpoint v1.1
          lowercase    :				List[Any]       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get first result from Stable Diffusion Checkpoint v1.2
          lowercase    :				Any       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get first result from Stable Diffusion Checkpoint v1.3
          lowercase    :				str       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get first result from Stable Diffusion Checkpoint v1.4
          lowercase    :				Optional[int]       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
          return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]				)


