import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						42
  lowerCamelCase__     =						None



def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=0.999     ,      __UpperCamelCase="cosine"     ,      ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__UpperCamelCase				):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__UpperCamelCase				):
                  return math.exp(t * -12.0				)

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      SCREAMING_SNAKE_CASE_          =  []
      for i in range(_lowerCamelCase				):
            SCREAMING_SNAKE_CASE_          =  i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_          =  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_lowerCamelCase				) / alpha_bar_fn(_lowerCamelCase				)     ,      _lowerCamelCase				)				)
      return torch.tensor(_lowerCamelCase     ,      dtype=torch.floataa				)





class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						1







  @register_to_config
  def __init__(							self :       Dict  ,     __magic_name__ :       Any = 1_000  ,     __magic_name__ :       str = 0.0001  ,     __magic_name__ :       Any = 0.02  ,     __magic_name__ :       Any = "linear"  ,     __magic_name__ :       str = None  ,     __magic_name__ :       List[Any] = True  ,     __magic_name__ :       Optional[Any] = True  ,     __magic_name__ :       Any = 0  ,     __magic_name__ :       int = "epsilon"  ,     __magic_name__ :       Optional[Any] = 1.0  ,     **__magic_name__ :       Tuple  ,     )  ->	int:





        if kwargs.get("set_alpha_to_one"  ,     UpperCamelCase__   ) is not None:
              SCREAMING_SNAKE_CASE_          =  (
                  "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
              )
              deprecate("set_alpha_to_one"  ,     "1.0.0"  ,     UpperCamelCase__  ,     standard_warn=UpperCamelCase__   )
              SCREAMING_SNAKE_CASE_          =  kwargs["set_alpha_to_one"]
        if trained_betas is not None:
              SCREAMING_SNAKE_CASE_          =  torch.tensor(UpperCamelCase__  ,     dtype=torch.floataa   )
        elif beta_schedule == "linear":
              SCREAMING_SNAKE_CASE_          =  torch.linspace(UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              SCREAMING_SNAKE_CASE_          =  (
                  torch.linspace(beta_start**0.5  ,     beta_end**0.5  ,     UpperCamelCase__  ,     dtype=torch.floataa   ) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              SCREAMING_SNAKE_CASE_          =  betas_for_alpha_bar(UpperCamelCase__   )
        else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

        SCREAMING_SNAKE_CASE_          =  1.0 - self.betas
        SCREAMING_SNAKE_CASE_          =  torch.cumprod(self.alphas  ,     dim=0   )

        # At every step in inverted ddim, we are looking into the next alphas_cumprod
        # For the final step, there is no next alphas_cumprod, and the index is out of bounds
        # `set_alpha_to_zero` decides whether we set this parameter simply to zero
        # in this case, self.step() just output the predicted noise
        # or whether we use the final alpha of the "non-previous" one.
        SCREAMING_SNAKE_CASE_          =  torch.tensor(0.0   ) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        SCREAMING_SNAKE_CASE_          =  1.0

        # setable values
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(np.arange(0  ,     UpperCamelCase__   ).copy().astype(np.intaa   )   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       int  ,     __magic_name__ :       Optional[int] = None   )  ->	torch.FloatTensor:
        return sample







  def    __A     (							self :       Any  ,     __magic_name__ :       str  ,     __magic_name__ :       int = None   )  ->	List[str]:





        if num_inference_steps > self.config.num_train_timesteps:
              raise ValueError(
                  F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                  F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                  F''' maximal {self.config.num_train_timesteps} timesteps.'''   )

        SCREAMING_SNAKE_CASE_          =  num_inference_steps
        SCREAMING_SNAKE_CASE_          =  self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        SCREAMING_SNAKE_CASE_          =  (np.arange(0  ,     UpperCamelCase__   ) * step_ratio).round().copy().astype(np.intaa   )
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(UpperCamelCase__   ).to(UpperCamelCase__   )
        self.timesteps += self.config.steps_offset







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[Any] = 0.0  ,     __magic_name__ :       Tuple = False  ,     __magic_name__ :       Optional[Any] = None  ,     __magic_name__ :       Tuple = True  ,     )  ->	Union[DDIMSchedulerOutput, Tuple]:
        SCREAMING_SNAKE_CASE_          =  timestep + self.config.num_train_timesteps // self.num_inference_steps

        # 2. compute alphas, betas
        # change original implementation to exactly match noise levels for analogous forward process
        SCREAMING_SNAKE_CASE_          =  self.alphas_cumprod[timestep]
        SCREAMING_SNAKE_CASE_          =  (
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        SCREAMING_SNAKE_CASE_          =  1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
              SCREAMING_SNAKE_CASE_          =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              SCREAMING_SNAKE_CASE_          =  model_output
        elif self.config.prediction_type == "sample":
              SCREAMING_SNAKE_CASE_          =  model_output
              SCREAMING_SNAKE_CASE_          =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
              SCREAMING_SNAKE_CASE_          =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              SCREAMING_SNAKE_CASE_          =  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
              raise ValueError(
                  F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                  " `v_prediction`"   )

        # 4. Clip or threshold "predicted x_0"
        if self.config.clip_sample:
              SCREAMING_SNAKE_CASE_          =  pred_original_sample.clamp(
                  -self.config.clip_sample_range  ,     self.config.clip_sample_range   )

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        SCREAMING_SNAKE_CASE_          =  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        SCREAMING_SNAKE_CASE_          =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
              return (prev_sample, pred_original_sample)
        return DDIMSchedulerOutput(prev_sample=UpperCamelCase__  ,     pred_original_sample=UpperCamelCase__   )







  def __len__(							self :       Union[str, Any]   )  ->	Union[str, Any]:
        return self.config.num_train_timesteps



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     :  Optional[Any]					     =       logging.get_logger(__name__)

A     :  List[Any]					     =       torch.device("cpu")


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__UpperCamelCase     ,      stream=__UpperCamelCase				).raw				)
      return im


def      a__ (		__UpperCamelCase				):
      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]				)

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]				)

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]				)

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  dct.pop(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  val


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      for k in state_dict.keys():
            SCREAMING_SNAKE_CASE_          =  k
            if ".pwconv" in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".pwconv"     ,      ".point_wise_conv"				)
            if ".dwconv" in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".dwconv"     ,      ".depth_wise_conv"				)
            if ".Proj." in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".Proj."     ,      ".proj."				)
            if "patch_embed" in k_new:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace("patch_embed"     ,      "swiftformer.patch_embed.patch_embedding"				)
            if "network" in k_new:
                  SCREAMING_SNAKE_CASE_          =  k_new.split("."				)
                  if ls[2].isdigit():
                        SCREAMING_SNAKE_CASE_          =  "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]				)
                  else:
                        SCREAMING_SNAKE_CASE_          =  k_new.replace("network"     ,      "swiftformer.encoder.network"				)
            rename_keys.append((k, k_new)				)
      return rename_keys


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_          =  idalabel
      SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 6, 4]
            SCREAMING_SNAKE_CASE_          =  [4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 9, 6]
            SCREAMING_SNAKE_CASE_          =  [4_8, 6_4, 1_6_8, 2_2_4]

      elif swiftformer_name == "swiftformer_l1":
            SCREAMING_SNAKE_CASE_          =  [4, 3, 1_0, 5]
            SCREAMING_SNAKE_CASE_          =  [4_8, 9_6, 1_9_2, 3_8_4]

      elif swiftformer_name == "swiftformer_l3":
            SCREAMING_SNAKE_CASE_          =  [4, 4, 1_2, 6]
            SCREAMING_SNAKE_CASE_          =  [6_4, 1_2_8, 3_2_0, 5_1_2]

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

      SCREAMING_SNAKE_CASE_          =  create_rename_keys(__UpperCamelCase				)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_          =  SwiftFormerForImageClassification(__UpperCamelCase				).eval()
      hf_model.load_state_dict(__UpperCamelCase				)

      # prepare test inputs
      SCREAMING_SNAKE_CASE_          =  prepare_img()
      SCREAMING_SNAKE_CASE_          =  ViTImageProcessor.from_pretrained("preprocessor_config"				)
      SCREAMING_SNAKE_CASE_          =  processor(images=__UpperCamelCase     ,      return_tensors="pt"				)

      # compare outputs from both models
      SCREAMING_SNAKE_CASE_          =  get_expected_output(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  hf_model(inputs["pixel_values"]				).logits

      assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
      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     :  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     :  List[Any]					     =       parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


