
'''simple docstring'''







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class UpperCAmelCase__	( __a	):





  """simple docstring"""

  __UpperCAmelCase				:				Tuple   =					42
  __UpperCAmelCase				:				Optional[Any]   =					None





def 					UpperCAmelCase_		(__a      :    str ,	__a      :    List[Any]=0.999 ,	__a      :    Dict="cosine" ,	):






   """simple docstring"""

   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__a      :    Union[str, Any] ):
         return math.exp(t * -12.0 )

   else:
      raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

   _a				:	int			=   []
   for i in range(snake_case__ ):
      _a				:	Tuple			=   i / num_diffusion_timesteps
      _a				:	Union[str, Any]			=   (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__ ) / alpha_bar_fn(snake_case__ ) ,	snake_case__ ) )
   return torch.tensor(snake_case__ ,	dtype=torch.floataa )







class UpperCAmelCase__	( __a    ,				__a	):





  """simple docstring"""

  @register_to_config
  def __init__(		self      :     List[str]							,_a      :     int = 1000							,_a      :     str = "fixed_small_log"							,_a      :     bool = True							,_a      :     Optional[float] = 1.0							,_a      :     str = "epsilon"							,_a      :     str = "squaredcos_cap_v2"							,):


     '''simple docstring'''






     if beta_schedule != "squaredcos_cap_v2":
        raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''       )

     _a				:	Tuple			=   betas_for_alpha_bar(a_       )

     _a				:	List[str]			=   1.0 - self.betas
     _a				:	Optional[int]			=   torch.cumprod(self.alphas							,dim=0       )
     _a				:	Tuple			=   torch.tensor(1.0       )

     # standard deviation of the initial noise distribution
     _a				:	Optional[Any]			=   1.0

     # setable values
     _a				:	str			=   None
     _a				:	Tuple			=   torch.from_numpy(np.arange(0							,a_       )[::-1].copy()       )

     _a				:	int			=   variance_type


  def   __lowercase		(		self      :     Dict							,_a      :     torch.FloatTensor							,_a      :     Optional[int] = None       ):


     '''simple docstring'''






     return sample


  def   __lowercase		(		self      :     Any							,_a      :     int							,_a      :     Union[str, torch.device] = None       ):


     '''simple docstring'''






     _a				:	List[Any]			=   num_inference_steps
     _a				:	Any			=   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
     _a				:	Optional[int]			=   (np.arange(0							,a_       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
     _a				:	Optional[Any]			=   torch.from_numpy(a_       ).to(a_       )


  def   __lowercase		(		self      :     List[Any]							,_a      :     Tuple							,_a      :     Dict=None							,_a      :     Union[str, Any]=None							,_a      :     Tuple=None       ):


     '''simple docstring'''






     if prev_timestep is None:
        _a				:	Optional[Any]			=   t - 1

     _a				:	Dict			=   self.alphas_cumprod[t]
     _a				:	List[Any]			=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
     _a				:	List[str]			=   1 - alpha_prod_t
     _a				:	List[Any]			=   1 - alpha_prod_t_prev

     if prev_timestep == t - 1:
        _a				:	int			=   self.betas[t]
     else:
        _a				:	Union[str, Any]			=   1 - alpha_prod_t / alpha_prod_t_prev

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     _a				:	int			=   beta_prod_t_prev / beta_prod_t * beta

     if variance_type is None:
        _a				:	Any			=   self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small_log":
        _a				:	Optional[int]			=   torch.log(torch.clamp(a_							,min=1E-20       )       )
        _a				:	int			=   torch.exp(0.5 * variance       )
     elif variance_type == "learned_range":
        # NOTE difference with DDPM scheduler
        _a				:	Optional[Any]			=   variance.log()
        _a				:	List[str]			=   beta.log()

        _a				:	Optional[int]			=   (predicted_variance + 1) / 2
        _a				:	Dict			=   frac * max_log + (1 - frac) * min_log

     return variance


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     torch.FloatTensor							,_a      :     int							,_a      :     torch.FloatTensor							,_a      :     Optional[int] = None							,_a      :     Tuple=None							,_a      :     bool = True							,):


     '''simple docstring'''






     _a				:	List[Any]			=   timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
        _a				:	str			=   torch.split(a_							,sample.shape[1]							,dim=1       )
     else:
        _a				:	Dict			=   None

     # 1. compute alphas, betas
     if prev_timestep is None:
        _a				:	Optional[Any]			=   t - 1

     _a				:	Optional[int]			=   self.alphas_cumprod[t]
     _a				:	Optional[int]			=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
     _a				:	Optional[Any]			=   1 - alpha_prod_t
     _a				:	int			=   1 - alpha_prod_t_prev

     if prev_timestep == t - 1:
        _a				:	str			=   self.betas[t]
        _a				:	int			=   self.alphas[t]
     else:
        _a				:	Dict			=   1 - alpha_prod_t / alpha_prod_t_prev
        _a				:	str			=   1 - beta

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
        _a				:	List[str]			=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
        _a				:	Tuple			=   model_output
     else:
        raise ValueError(
            F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
            ' for the UnCLIPScheduler.'       )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
        _a				:	int			=   torch.clamp(
            a_							,-self.config.clip_sample_range							,self.config.clip_sample_range       )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     _a				:	List[Any]			=   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
     _a				:	Any			=   alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     _a				:	Dict			=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     _a				:	Any			=   0
     if t > 0:
        _a				:	str			=   randn_tensor(
            model_output.shape							,dtype=model_output.dtype							,generator=a_							,device=model_output.device       )

        _a				:	Union[str, Any]			=   self._get_variance(
            a_							,predicted_variance=a_							,prev_timestep=a_							,)

        if self.variance_type == "fixed_small_log":
           _a				:	Tuple			=   variance
        elif self.variance_type == "learned_range":
           _a				:	int			=   (0.5 * variance).exp()
        else:
           raise ValueError(
               F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
               ' for the UnCLIPScheduler.'       )

        _a				:	List[Any]			=   variance * variance_noise

     _a				:	Any			=   pred_prev_sample + variance

     if not return_dict:
        return (pred_prev_sample,)

     return UnCLIPSchedulerOutput(prev_sample=a_							,pred_original_sample=a_       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     torch.FloatTensor							,_a      :     torch.FloatTensor							,_a      :     torch.IntTensor							,):


     '''simple docstring'''






     _a				:	List[Any]			=   self.alphas_cumprod.to(device=original_samples.device							,dtype=original_samples.dtype       )
     _a				:	str			=   timesteps.to(original_samples.device       )

     _a				:	Union[str, Any]			=   alphas_cumprod[timesteps] ** 0.5
     _a				:	List[str]			=   sqrt_alpha_prod.flatten()
     while len(sqrt_alpha_prod.shape       ) < len(original_samples.shape       ):
        _a				:	Optional[Any]			=   sqrt_alpha_prod.unsqueeze(-1       )

     _a				:	Tuple			=   (1 - alphas_cumprod[timesteps]) ** 0.5
     _a				:	Optional[Any]			=   sqrt_one_minus_alpha_prod.flatten()
     while len(sqrt_one_minus_alpha_prod.shape       ) < len(original_samples.shape       ):
        _a				:	str			=   sqrt_one_minus_alpha_prod.unsqueeze(-1       )

     _a				:	Any			=   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples









"""simple docstring"""


import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  []
  for i in range(config.num_hidden_layers    ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")    )
    rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")    )
    rename_keys.append(
        (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")    )
    rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")    )
    rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")    )
    rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")    )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""module.cls_token""", """vit.embeddings.cls_token"""),
          ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
      ]    )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""module.norm.weight""", """layernorm.weight"""),
            ("""module.norm.bias""", """layernorm.bias"""),
        ]    )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    _snake_case							:		Any								=  [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""    ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""norm.weight""", """vit.layernorm.weight"""),
            ("""norm.bias""", """vit.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ]    )

  return rename_keys


def 					UpperCAmelCase__       (snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  for i in range(config.num_hidden_layers    ):
    if base_model:
      _snake_case							:		List[Any]								=  """"""
    else:
      _snake_case							:		List[Any]								=  """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"    )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case							:		Optional[Any]								=  in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		Optional[Any]								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case							:		List[str]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  [
      """module.fc.fc1.weight""",
      """module.fc.fc1.bias""",
      """module.fc.bn1.weight""",
      """module.fc.bn1.bias""",
      """module.fc.bn1.running_mean""",
      """module.fc.bn1.running_var""",
      """module.fc.bn1.num_batches_tracked""",
      """module.fc.fc2.weight""",
      """module.fc.fc2.bias""",
      """module.fc.bn2.weight""",
      """module.fc.bn2.bias""",
      """module.fc.bn2.running_mean""",
      """module.fc.bn2.running_var""",
      """module.fc.bn2.num_batches_tracked""",
      """module.fc.fc3.weight""",
      """module.fc.fc3.bias""",
  ]
  for k in ignore_keys:
    state_dict.pop(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Tuple		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  dct.pop(snake_case__    )
  _snake_case							:		Union[str, Any]								=  val


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		str								=  ViTMSNConfig()
  _snake_case							:		Any								=  10_00

  _snake_case							:		Tuple								=  """datasets/huggingface/label-files"""
  _snake_case							:		Dict								=  """imagenet-1k-id2label.json"""
  _snake_case							:		int								=  json.load(open(hf_hub_download(snake_case__		,   snake_case__    )		,   """r"""    )    )
  _snake_case							:		Any								=  {int(snake_case__    ): v for k, v in idalabel.items()}
  _snake_case							:		List[Any]								=  idalabel
  _snake_case							:		str								=  {v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    _snake_case							:		Tuple								=  3_84
    _snake_case							:		Dict								=  15_36
    _snake_case							:		Tuple								=  6
  elif "l16" in checkpoint_url:
    _snake_case							:		Any								=  10_24
    _snake_case							:		int								=  40_96
    _snake_case							:		str								=  24
    _snake_case							:		Optional[int]								=  16
    _snake_case							:		List[Any]								=  0.1
  elif "b4" in checkpoint_url:
    _snake_case							:		Tuple								=  4
  elif "l7" in checkpoint_url:
    _snake_case							:		int								=  7
    _snake_case							:		Dict								=  10_24
    _snake_case							:		Optional[Any]								=  40_96
    _snake_case							:		Any								=  24
    _snake_case							:		Union[str, Any]								=  16
    _snake_case							:		Optional[int]								=  0.1

  _snake_case							:		int								=  ViTMSNModel(snake_case__    )

  _snake_case							:		Optional[int]								=  torch.hub.load_state_dict_from_url(snake_case__		,   map_location="""cpu"""    )["""target_encoder"""]

  _snake_case							:		List[str]								=  ViTImageProcessor(size=config.image_size    )

  remove_projection_head(snake_case__    )
  _snake_case							:		List[str]								=  create_rename_keys(snake_case__		,   base_model=snake_case__    )

  for src, dest in rename_keys:
    rename_key(snake_case__		,   snake_case__		,   snake_case__    )
  read_in_q_k_v(snake_case__		,   snake_case__		,   base_model=snake_case__    )

  model.load_state_dict(snake_case__    )
  model.eval()

  _snake_case							:		Union[str, Any]								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""

  _snake_case							:		Tuple								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  _snake_case							:		str								=  ViTImageProcessor(
      size=config.image_size		,   image_mean=snake_case__		,   image_std=snake_case__    )
  _snake_case							:		Any								=  image_processor(images=snake_case__		,   return_tensors="""pt"""    )

  # forward pass
  torch.manual_seed(2    )
  _snake_case							:		int								=  model(**snake_case__    )
  _snake_case							:		List[Any]								=  outputs.last_hidden_state

  # The following Colab Notebook was used to generate these outputs:
  # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
  if "s16" in checkpoint_url:
    _snake_case							:		Optional[Any]								=  torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]    )
  elif "b16" in checkpoint_url:
    _snake_case							:		str								=  torch.tensor([[14.28_89, -18.90_45, 11.72_81]]    )
  elif "l16" in checkpoint_url:
    _snake_case							:		Optional[int]								=  torch.tensor([[41.50_28, -22.86_81, 45.64_75]]    )
  elif "b4" in checkpoint_url:
    _snake_case							:		List[Any]								=  torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]    )
  else:
    _snake_case							:		Optional[int]								=  torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]    )

  # verify logits
  assert torch.allclose(last_hidden_state[:, 0, :3]		,   snake_case__		,   atol=1e-4    )

  print(F"Saving model to {pytorch_dump_folder_path}"    )
  model.save_pretrained(snake_case__    )

  print(F"Saving image processor to {pytorch_dump_folder_path}"    )
  image_processor.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
         type=str,
         help='''URL of the checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     A_						=				parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


