

from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 							A_ (		snake_case__					):
     _lowercase			:     List[Any]								=	4_2







class 							A_ (		nn.Module					):


     def __init__(							self			:		Optional[Any]			,  UpperCAmelCase			:		Union[str, Any]=3			,  UpperCAmelCase			:		Any=3			,  UpperCAmelCase			:		List[Any]=("DownEncoderBlock2D",)			,  UpperCAmelCase			:		Dict=(6_4,)			,  UpperCAmelCase			:		List[str]=2			,  UpperCAmelCase			:		Optional[Any]=3_2			,  UpperCAmelCase			:		List[Any]="silu"			,  UpperCAmelCase			:		Optional[int]=True			,  )							->	Any:
      super().__init__()
      __lowerCAmelCase:   Any   	=      layers_per_block

      __lowerCAmelCase:   Optional[Any]   	=      torch.nn.Convad(
          UpperCAmelCase			,  block_out_channels[0]			,  kernel_size=3			,  stride=1			,  padding=1			,  )

      __lowerCAmelCase:   Any   	=      None
      __lowerCAmelCase:   Optional[Any]   	=      nn.ModuleList([]					)

      # down
      __lowerCAmelCase:   Optional[int]   	=      block_out_channels[0]
      for i, down_block_type in enumerate(UpperCAmelCase					):
       __lowerCAmelCase:   List[str]   	=      output_channel
       __lowerCAmelCase:   Union[str, Any]   	=      block_out_channels[i]
       __lowerCAmelCase:   Tuple   	=      i == len(UpperCAmelCase					) - 1

       __lowerCAmelCase:   Tuple   	=      get_down_block(
           UpperCAmelCase			,  num_layers=self.layers_per_block			,  in_channels=UpperCAmelCase			,  out_channels=UpperCAmelCase			,  add_downsample=not is_final_block			,  resnet_eps=1E-6			,  downsample_padding=0			,  resnet_act_fn=UpperCAmelCase			,  resnet_groups=UpperCAmelCase			,  attention_head_dim=UpperCAmelCase			,  temb_channels=UpperCAmelCase			,  )
       self.down_blocks.append(UpperCAmelCase					)

      # mid
      __lowerCAmelCase:   Tuple   	=      UNetMidBlockaD(
          in_channels=block_out_channels[-1]			,  resnet_eps=1E-6			,  resnet_act_fn=UpperCAmelCase			,  output_scale_factor=1			,  resnet_time_scale_shift='default'			,  attention_head_dim=block_out_channels[-1]			,  resnet_groups=UpperCAmelCase			,  temb_channels=UpperCAmelCase			,  )

      # out
      __lowerCAmelCase:   Optional[int]   	=      nn.GroupNorm(num_channels=block_out_channels[-1]			,  num_groups=UpperCAmelCase			,  eps=1E-6					)
      __lowerCAmelCase:   Optional[Any]   	=      nn.SiLU()

      __lowerCAmelCase:   Tuple   	=      2 * out_channels if double_z else out_channels
      __lowerCAmelCase:   Optional[int]   	=      nn.Convad(block_out_channels[-1]			,  UpperCAmelCase			,  3			,  padding=1					)

      __lowerCAmelCase:   Union[str, Any]   	=      False


     def   UpperCAmelCase  (							self			:		Any			,  UpperCAmelCase			:		List[Any]					)							->	Any:
      __lowerCAmelCase:   int   	=      x
      __lowerCAmelCase:   List[Any]   	=      self.conv_in(UpperCAmelCase					)

      if self.training and self.gradient_checkpointing:

       def create_custom_forward(UpperCAmelCase			:		Optional[Any]					):
        def custom_forward(*UpperCAmelCase			:		List[Any]					):
         return module(*UpperCAmelCase					)

        return custom_forward

       # down
       if is_torch_version('>='			,  '1.11.0'					):
        for down_block in self.down_blocks:
         __lowerCAmelCase:   Dict   	=      torch.utils.checkpoint.checkpoint(
             create_custom_forward(UpperCAmelCase					)			,  UpperCAmelCase			,  use_reentrant=UpperCAmelCase					)
        # middle
        __lowerCAmelCase:   Optional[int]   	=      torch.utils.checkpoint.checkpoint(
            create_custom_forward(self.mid_block					)			,  UpperCAmelCase			,  use_reentrant=UpperCAmelCase					)
       else:
        for down_block in self.down_blocks:
         __lowerCAmelCase:   Union[str, Any]   	=      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase					)			,  UpperCAmelCase					)
        # middle
        __lowerCAmelCase:   Any   	=      torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block					)			,  UpperCAmelCase					)

      else:
       # down
       for down_block in self.down_blocks:
        __lowerCAmelCase:   Dict   	=      down_block(UpperCAmelCase					)

       # middle
       __lowerCAmelCase:   List[Any]   	=      self.mid_block(UpperCAmelCase					)

      # post-process
      __lowerCAmelCase:   Optional[int]   	=      self.conv_norm_out(UpperCAmelCase					)
      __lowerCAmelCase:   Dict   	=      self.conv_act(UpperCAmelCase					)
      __lowerCAmelCase:   Union[str, Any]   	=      self.conv_out(UpperCAmelCase					)

      return sample







class 							A_ (		nn.Module					):


     def __init__(							self			:		Optional[int]			,  UpperCAmelCase			:		List[str]=3			,  UpperCAmelCase			:		List[Any]=3			,  UpperCAmelCase			:		List[str]=("UpDecoderBlock2D",)			,  UpperCAmelCase			:		int=(6_4,)			,  UpperCAmelCase			:		Optional[Any]=2			,  UpperCAmelCase			:		Optional[Any]=3_2			,  UpperCAmelCase			:		str="silu"			,  UpperCAmelCase			:		Union[str, Any]="group"			,  )							->	int:
      super().__init__()
      __lowerCAmelCase:   str   	=      layers_per_block

      __lowerCAmelCase:   Optional[int]   	=      nn.Convad(
          UpperCAmelCase			,  block_out_channels[-1]			,  kernel_size=3			,  stride=1			,  padding=1			,  )

      __lowerCAmelCase:   str   	=      None
      __lowerCAmelCase:   List[str]   	=      nn.ModuleList([]					)

      __lowerCAmelCase:   str   	=      in_channels if norm_type == 'spatial' else None

      # mid
      __lowerCAmelCase:   str   	=      UNetMidBlockaD(
          in_channels=block_out_channels[-1]			,  resnet_eps=1E-6			,  resnet_act_fn=UpperCAmelCase			,  output_scale_factor=1			,  resnet_time_scale_shift='default' if norm_type == 'group' else norm_type			,  attention_head_dim=block_out_channels[-1]			,  resnet_groups=UpperCAmelCase			,  temb_channels=UpperCAmelCase			,  )

      # up
      __lowerCAmelCase:   Optional[int]   	=      list(reversed(UpperCAmelCase					)					)
      __lowerCAmelCase:   int   	=      reversed_block_out_channels[0]
      for i, up_block_type in enumerate(UpperCAmelCase					):
       __lowerCAmelCase:   List[str]   	=      output_channel
       __lowerCAmelCase:   Optional[int]   	=      reversed_block_out_channels[i]

       __lowerCAmelCase:   Optional[Any]   	=      i == len(UpperCAmelCase					) - 1

       __lowerCAmelCase:   Dict   	=      get_up_block(
           UpperCAmelCase			,  num_layers=self.layers_per_block + 1			,  in_channels=UpperCAmelCase			,  out_channels=UpperCAmelCase			,  prev_output_channel=UpperCAmelCase			,  add_upsample=not is_final_block			,  resnet_eps=1E-6			,  resnet_act_fn=UpperCAmelCase			,  resnet_groups=UpperCAmelCase			,  attention_head_dim=UpperCAmelCase			,  temb_channels=UpperCAmelCase			,  resnet_time_scale_shift=UpperCAmelCase			,  )
       self.up_blocks.append(UpperCAmelCase					)
       __lowerCAmelCase:   List[str]   	=      output_channel

      # out
      if norm_type == "spatial":
       __lowerCAmelCase:   Tuple   	=      SpatialNorm(block_out_channels[0]			,  UpperCAmelCase					)
      else:
       __lowerCAmelCase:   List[Any]   	=      nn.GroupNorm(num_channels=block_out_channels[0]			,  num_groups=UpperCAmelCase			,  eps=1E-6					)
      __lowerCAmelCase:   List[str]   	=      nn.SiLU()
      __lowerCAmelCase:   Union[str, Any]   	=      nn.Convad(block_out_channels[0]			,  UpperCAmelCase			,  3			,  padding=1					)

      __lowerCAmelCase:   List[Any]   	=      False


     def   UpperCAmelCase  (							self			:		List[str]			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Dict=None					)							->	Any:
      __lowerCAmelCase:   int   	=      z
      __lowerCAmelCase:   Tuple   	=      self.conv_in(UpperCAmelCase					)

      __lowerCAmelCase:   Optional[Any]   	=      next(iter(self.up_blocks.parameters()					)					).dtype
      if self.training and self.gradient_checkpointing:

       def create_custom_forward(UpperCAmelCase			:		List[str]					):
        def custom_forward(*UpperCAmelCase			:		Dict					):
         return module(*UpperCAmelCase					)

        return custom_forward

       if is_torch_version('>='			,  '1.11.0'					):
        # middle
        __lowerCAmelCase:   List[Any]   	=      torch.utils.checkpoint.checkpoint(
            create_custom_forward(self.mid_block					)			,  UpperCAmelCase			,  UpperCAmelCase			,  use_reentrant=UpperCAmelCase					)
        __lowerCAmelCase:   str   	=      sample.to(UpperCAmelCase					)

        # up
        for up_block in self.up_blocks:
         __lowerCAmelCase:   str   	=      torch.utils.checkpoint.checkpoint(
             create_custom_forward(UpperCAmelCase					)			,  UpperCAmelCase			,  UpperCAmelCase			,  use_reentrant=UpperCAmelCase					)
       else:
        # middle
        __lowerCAmelCase:   Tuple   	=      torch.utils.checkpoint.checkpoint(
            create_custom_forward(self.mid_block					)			,  UpperCAmelCase			,  UpperCAmelCase					)
        __lowerCAmelCase:   Optional[int]   	=      sample.to(UpperCAmelCase					)

        # up
        for up_block in self.up_blocks:
         __lowerCAmelCase:   int   	=      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase					)			,  UpperCAmelCase			,  UpperCAmelCase					)
      else:
       # middle
       __lowerCAmelCase:   int   	=      self.mid_block(UpperCAmelCase			,  UpperCAmelCase					)
       __lowerCAmelCase:   Optional[int]   	=      sample.to(UpperCAmelCase					)

       # up
       for up_block in self.up_blocks:
        __lowerCAmelCase:   List[str]   	=      up_block(UpperCAmelCase			,  UpperCAmelCase					)

        # post-process
      if latent_embeds is None:
       __lowerCAmelCase:   str   	=      self.conv_norm_out(UpperCAmelCase					)
      else:
       __lowerCAmelCase:   Any   	=      self.conv_norm_out(UpperCAmelCase			,  UpperCAmelCase					)
      __lowerCAmelCase:   Optional[int]   	=      self.conv_act(UpperCAmelCase					)
      __lowerCAmelCase:   List[Any]   	=      self.conv_out(UpperCAmelCase					)

      return sample







class 							A_ (		nn.Module					):


     def __init__(							self			:		str			,  UpperCAmelCase			:		List[str]			,  UpperCAmelCase			:		Tuple			,  UpperCAmelCase			:		List[str]			,  UpperCAmelCase			:		str=None			,  UpperCAmelCase			:		Tuple="random"			,  UpperCAmelCase			:		int=False			,  UpperCAmelCase			:		Any=True					)							->	Tuple:
      super().__init__()
      __lowerCAmelCase:   Optional[int]   	=      n_e
      __lowerCAmelCase:   Any   	=      vq_embed_dim
      __lowerCAmelCase:   Dict   	=      beta
      __lowerCAmelCase:   List[str]   	=      legacy

      __lowerCAmelCase:   Optional[int]   	=      nn.Embedding(self.n_e			,  self.vq_embed_dim					)
      self.embedding.weight.data.uniform_(-1.0 / self.n_e			,  1.0 / self.n_e					)

      __lowerCAmelCase:   List[Any]   	=      remap
      if self.remap is not None:
       self.register_buffer('used'			,  torch.tensor(np.load(self.remap					)					)					)
       __lowerCAmelCase:   Dict   	=      self.used.shape[0]
       __lowerCAmelCase:   Union[str, Any]   	=      unknown_index  # "random" or "extra" or integer
       if self.unknown_index == "extra":
        __lowerCAmelCase:   Dict   	=      self.re_embed
        __lowerCAmelCase:   Any   	=      self.re_embed + 1
       print(
           F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
           F'''Using {self.unknown_index} for unknown indices.'''					)
      else:
       __lowerCAmelCase:   str   	=      n_e

      __lowerCAmelCase:   int   	=      sane_index_shape


     def   UpperCAmelCase  (							self			:		List[str]			,  UpperCAmelCase			:		Optional[int]					)							->	int:
      __lowerCAmelCase:   Union[str, Any]   	=      inds.shape
      assert len(UpperCAmelCase					) > 1
      __lowerCAmelCase:   Optional[int]   	=      inds.reshape(ishape[0]			,  -1					)
      __lowerCAmelCase:   List[Any]   	=      self.used.to(UpperCAmelCase					)
      __lowerCAmelCase:   Optional[int]   	=      (inds[:, :, None] == used[None, None, ...]).long()
      __lowerCAmelCase:   Any   	=      match.argmax(-1					)
      __lowerCAmelCase:   Any   	=      match.sum(2					) < 1
      if self.unknown_index == "random":
       __lowerCAmelCase:   Optional[int]   	=      torch.randint(0			,  self.re_embed			,  size=new[unknown].shape					).to(device=new.device					)
      else:
       __lowerCAmelCase:   Dict   	=      self.unknown_index
      return new.reshape(UpperCAmelCase					)


     def   UpperCAmelCase  (							self			:		List[Any]			,  UpperCAmelCase			:		Optional[int]					)							->	int:
      __lowerCAmelCase:   Dict   	=      inds.shape
      assert len(UpperCAmelCase					) > 1
      __lowerCAmelCase:   List[str]   	=      inds.reshape(ishape[0]			,  -1					)
      __lowerCAmelCase:   Tuple   	=      self.used.to(UpperCAmelCase					)
      if self.re_embed > self.used.shape[0]:  # extra token
       __lowerCAmelCase:   Optional[int]   	=      0  # simply set to zero
      __lowerCAmelCase:   Union[str, Any]   	=      torch.gather(used[None, :][inds.shape[0] * [0], :]			,  1			,  UpperCAmelCase					)
      return back.reshape(UpperCAmelCase					)


     def   UpperCAmelCase  (							self			:		Dict			,  UpperCAmelCase			:		str					)							->	str:
      # reshape z -> (batch, height, width, channel) and flatten
      __lowerCAmelCase:   Dict   	=      z.permute(0			,  2			,  3			,  1					).contiguous()
      __lowerCAmelCase:   Optional[Any]   	=      z.view(-1			,  self.vq_embed_dim					)

      # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
      __lowerCAmelCase:   List[Any]   	=      torch.argmin(torch.cdist(UpperCAmelCase			,  self.embedding.weight					)			,  dim=1					)

      __lowerCAmelCase:   Tuple   	=      self.embedding(UpperCAmelCase					).view(z.shape					)
      __lowerCAmelCase:   Optional[Any]   	=      None
      __lowerCAmelCase:   Any   	=      None

      # compute loss for embedding
      if not self.legacy:
       __lowerCAmelCase:   Dict   	=      self.beta * torch.mean((z_q.detach() - z) ** 2					) + torch.mean((z_q - z.detach()) ** 2					)
      else:
       __lowerCAmelCase:   Any   	=      torch.mean((z_q.detach() - z) ** 2					) + self.beta * torch.mean((z_q - z.detach()) ** 2					)

      # preserve gradients
      __lowerCAmelCase:   List[Any]   	=      z + (z_q - z).detach()

      # reshape back to match original input shape
      __lowerCAmelCase:   Union[str, Any]   	=      z_q.permute(0			,  3			,  1			,  2					).contiguous()

      if self.remap is not None:
       __lowerCAmelCase:   Optional[int]   	=      min_encoding_indices.reshape(z.shape[0]			,  -1					)  # add batch axis
       __lowerCAmelCase:   List[str]   	=      self.remap_to_used(UpperCAmelCase					)
       __lowerCAmelCase:   Union[str, Any]   	=      min_encoding_indices.reshape(-1			,  1					)  # flatten

      if self.sane_index_shape:
       __lowerCAmelCase:   Dict   	=      min_encoding_indices.reshape(z_q.shape[0]			,  z_q.shape[2]			,  z_q.shape[3]					)

      return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


     def   UpperCAmelCase  (							self			:		str			,  UpperCAmelCase			:		Optional[int]			,  UpperCAmelCase			:		int					)							->	int:
      # shape specifying (batch, height, width, channel)
      if self.remap is not None:
       __lowerCAmelCase:   Any   	=      indices.reshape(shape[0]			,  -1					)  # add batch axis
       __lowerCAmelCase:   List[str]   	=      self.unmap_to_all(UpperCAmelCase					)
       __lowerCAmelCase:   Tuple   	=      indices.reshape(-1					)  # flatten again

      # get quantized latent vectors
      __lowerCAmelCase:   Optional[Any]   	=      self.embedding(UpperCAmelCase					)

      if shape is not None:
       __lowerCAmelCase:   Optional[int]   	=      z_q.view(UpperCAmelCase					)
       # reshape back to match original input shape
       __lowerCAmelCase:   Union[str, Any]   	=      z_q.permute(0			,  3			,  1			,  2					).contiguous()

      return z_q







class 							A_ (		snake_case__					):


     def __init__(							self			:		Dict			,  UpperCAmelCase			:		Optional[Any]			,  UpperCAmelCase			:		Optional[int]=False					)							->	Tuple:
      __lowerCAmelCase:   Optional[Any]   	=      parameters
      __lowerCAmelCase	,							__lowerCAmelCase:   Tuple   	=      torch.chunk(UpperCAmelCase			,  2			,  dim=1					)
      __lowerCAmelCase:   Union[str, Any]   	=      torch.clamp(self.logvar			,  -30.0			,  20.0					)
      __lowerCAmelCase:   List[Any]   	=      deterministic
      __lowerCAmelCase:   List[Any]   	=      torch.exp(0.5 * self.logvar					)
      __lowerCAmelCase:   Optional[int]   	=      torch.exp(self.logvar					)
      if self.deterministic:
       __lowerCAmelCase:   int   	=      torch.zeros_like(
           self.mean			,  device=self.parameters.device			,  dtype=self.parameters.dtype					)


     def   UpperCAmelCase  (							self			:		Union[str, Any]			,  UpperCAmelCase			:		Optional[int] = None					)							->	torch.FloatTensor:
      # make sure sample is on the same device as the parameters and has same dtype
      __lowerCAmelCase:   Union[str, Any]   	=      randn_tensor(
          self.mean.shape			,  generator=UpperCAmelCase			,  device=self.parameters.device			,  dtype=self.parameters.dtype					)
      __lowerCAmelCase:   Optional[Any]   	=      self.mean + self.std * sample
      return x


     def   UpperCAmelCase  (							self			:		Optional[int]			,  UpperCAmelCase			:		Optional[int]=None					)							->	Dict:
      if self.deterministic:
       return torch.Tensor([0.0]					)
      else:
       if other is None:
        return 0.5 * torch.sum(torch.pow(self.mean			,  2					) + self.var - 1.0 - self.logvar			,  dim=[1, 2, 3]					)
       else:
        return 0.5 * torch.sum(
            torch.pow(self.mean - other.mean			,  2					) / other.var
            + self.var / other.var
            - 1.0
            - self.logvar
            + other.logvar			,  dim=[1, 2, 3]			,  )


     def   UpperCAmelCase  (							self			:		str			,  UpperCAmelCase			:		str			,  UpperCAmelCase			:		Any=[1, 2, 3]					)							->	Dict:
      if self.deterministic:
       return torch.Tensor([0.0]					)
      __lowerCAmelCase:   Tuple   	=      np.log(2.0 * np.pi					)
      return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean			,  2					) / self.var			,  dim=UpperCAmelCase					)


     def   UpperCAmelCase  (							self			:		int					)							->	Optional[Any]:
      return self.mean









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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__   	=							logging.get_logger(__name__)
def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =MobileViTConfig()

 # size of the architecture
 if "mobilevit_s" in mobilevit_name:
  _lowercase							      =[144, 192, 240]
  _lowercase							      =[16, 32, 64, 96, 128, 160, 640]
 elif "mobilevit_xs" in mobilevit_name:
  _lowercase							      =[96, 120, 144]
  _lowercase							      =[16, 32, 48, 64, 80, 96, 384]
 elif "mobilevit_xxs" in mobilevit_name:
  _lowercase							      =[64, 80, 96]
  _lowercase							      =[16, 16, 24, 48, 64, 80, 320]
  _lowercase							      =0.05
  _lowercase							      =2.0

 if mobilevit_name.startswith('''deeplabv3_'''						):
  _lowercase							      =512
  _lowercase							      =16
  _lowercase							      =21
  _lowercase							      ='''pascal-voc-id2label.json'''
 else:
  _lowercase							      =1000
  _lowercase							      ='''imagenet-1k-id2label.json'''

 _lowercase							      ='''huggingface/label-files'''
 _lowercase							      =json.load(open(hf_hub_download(__snake_case     ,						__snake_case     ,						repo_type='''dataset'''						)     ,						'''r'''						)						)
 _lowercase							      ={int(__snake_case						): v for k, v in idalabel.items()}
 _lowercase							      =idalabel
 _lowercase							      ={v: k for k, v in idalabel.items()}

 return config
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=False						)     ->			Tuple:







 """simple docstring"""


 for i in range(1     ,						6						):
  if F"layer_{i}." in name:
   _lowercase							      =name.replace(F"layer_{i}."     ,						F"encoder.layer.{i - 1}."						)

 if "conv_1." in name:
  _lowercase							      =name.replace('''conv_1.'''     ,						'''conv_stem.'''						)
 if ".block." in name:
  _lowercase							      =name.replace('''.block.'''     ,						'''.'''						)

 if "exp_1x1" in name:
  _lowercase							      =name.replace('''exp_1x1'''     ,						'''expand_1x1'''						)
 if "red_1x1" in name:
  _lowercase							      =name.replace('''red_1x1'''     ,						'''reduce_1x1'''						)
 if ".local_rep.conv_3x3." in name:
  _lowercase							      =name.replace('''.local_rep.conv_3x3.'''     ,						'''.conv_kxk.'''						)
 if ".local_rep.conv_1x1." in name:
  _lowercase							      =name.replace('''.local_rep.conv_1x1.'''     ,						'''.conv_1x1.'''						)
 if ".norm." in name:
  _lowercase							      =name.replace('''.norm.'''     ,						'''.normalization.'''						)
 if ".conv." in name:
  _lowercase							      =name.replace('''.conv.'''     ,						'''.convolution.'''						)
 if ".conv_proj." in name:
  _lowercase							      =name.replace('''.conv_proj.'''     ,						'''.conv_projection.'''						)

 for i in range(0     ,						2						):
  for j in range(0     ,						4						):
   if F".{i}.{j}." in name:
    _lowercase							      =name.replace(F".{i}.{j}."     ,						F".{i}.layer.{j}."						)

 for i in range(2     ,						6						):
  for j in range(0     ,						4						):
   if F".{i}.{j}." in name:
    _lowercase							      =name.replace(F".{i}.{j}."     ,						F".{i}."						)
    if "expand_1x1" in name:
     _lowercase							      =name.replace('''expand_1x1'''     ,						'''downsampling_layer.expand_1x1'''						)
    if "conv_3x3" in name:
     _lowercase							      =name.replace('''conv_3x3'''     ,						'''downsampling_layer.conv_3x3'''						)
    if "reduce_1x1" in name:
     _lowercase							      =name.replace('''reduce_1x1'''     ,						'''downsampling_layer.reduce_1x1'''						)

 for i in range(2     ,						5						):
  if F".global_rep.{i}.weight" in name:
   _lowercase							      =name.replace(F".global_rep.{i}.weight"     ,						'''.layernorm.weight'''						)
  if F".global_rep.{i}.bias" in name:
   _lowercase							      =name.replace(F".global_rep.{i}.bias"     ,						'''.layernorm.bias'''						)

 if ".global_rep." in name:
  _lowercase							      =name.replace('''.global_rep.'''     ,						'''.transformer.'''						)
 if ".pre_norm_mha.0." in name:
  _lowercase							      =name.replace('''.pre_norm_mha.0.'''     ,						'''.layernorm_before.'''						)
 if ".pre_norm_mha.1.out_proj." in name:
  _lowercase							      =name.replace('''.pre_norm_mha.1.out_proj.'''     ,						'''.attention.output.dense.'''						)
 if ".pre_norm_ffn.0." in name:
  _lowercase							      =name.replace('''.pre_norm_ffn.0.'''     ,						'''.layernorm_after.'''						)
 if ".pre_norm_ffn.1." in name:
  _lowercase							      =name.replace('''.pre_norm_ffn.1.'''     ,						'''.intermediate.dense.'''						)
 if ".pre_norm_ffn.4." in name:
  _lowercase							      =name.replace('''.pre_norm_ffn.4.'''     ,						'''.output.dense.'''						)
 if ".transformer." in name:
  _lowercase							      =name.replace('''.transformer.'''     ,						'''.transformer.layer.'''						)

 if ".aspp_layer." in name:
  _lowercase							      =name.replace('''.aspp_layer.'''     ,						'''.'''						)
 if ".aspp_pool." in name:
  _lowercase							      =name.replace('''.aspp_pool.'''     ,						'''.'''						)
 if "seg_head." in name:
  _lowercase							      =name.replace('''seg_head.'''     ,						'''segmentation_head.'''						)
 if "segmentation_head.classifier.classifier." in name:
  _lowercase							      =name.replace('''segmentation_head.classifier.classifier.'''     ,						'''segmentation_head.classifier.'''						)

 if "classifier.fc." in name:
  _lowercase							      =name.replace('''classifier.fc.'''     ,						'''classifier.'''						)
 elif (not base_model) and ("segmentation_head." not in name):
  _lowercase							      ='''mobilevit.''' + name

 return name
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			Optional[Any]:







 """simple docstring"""


 if base_model:
  _lowercase							      =''''''
 else:
  _lowercase							      ='''mobilevit.'''

 for key in orig_state_dict.copy().keys():
  _lowercase							      =orig_state_dict.pop(__snake_case						)

  if key[:8] == "encoder.":
   _lowercase							      =key[8:]

  if "qkv" in key:
   _lowercase							      =key.split('''.'''						)
   _lowercase							      =int(key_split[0][6:]						) - 1
   _lowercase							      =int(key_split[3]						)
   _lowercase							      =model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"						)
   _lowercase							      =layer.transformer.layer[transformer_num].attention.attention.all_head_size
   _lowercase							      =(
       F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
   )
   if "weight" in key:
    _lowercase							      =val[:dim, :]
    _lowercase							      =val[dim : dim * 2, :]
    _lowercase							      =val[-dim:, :]
   else:
    _lowercase							      =val[:dim]
    _lowercase							      =val[dim : dim * 2]
    _lowercase							      =val[-dim:]
  else:
   _lowercase							      =val

 return orig_state_dict
def 				UpperCAmelCase_					(							)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _lowercase							      =Image.open(requests.get(__snake_case     ,						stream=__snake_case						).raw						)
 return im
@torch.no_grad()
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			int:







 """simple docstring"""


 _lowercase							      =get_mobilevit_config(__snake_case						)

 # load original state_dict
 _lowercase							      =torch.load(__snake_case     ,						map_location='''cpu'''						)

 # load 🤗 model
 if mobilevit_name.startswith('''deeplabv3_'''						):
  _lowercase							      =MobileViTForSemanticSegmentation(__snake_case						).eval()
 else:
  _lowercase							      =MobileViTForImageClassification(__snake_case						).eval()

 _lowercase							      =convert_state_dict(__snake_case     ,						__snake_case						)
 model.load_state_dict(__snake_case						)

 # Check outputs on an image, prepared by MobileViTImageProcessor
 _lowercase							      =MobileViTImageProcessor(crop_size=config.image_size     ,						size=config.image_size + 32						)
 _lowercase							      =image_processor(images=prepare_img()     ,						return_tensors='''pt'''						)
 _lowercase							      =model(**__snake_case						)
 _lowercase							      =outputs.logits

 if mobilevit_name.startswith('''deeplabv3_'''						):
  assert logits.shape == (1, 21, 32, 32)

  if mobilevit_name == "deeplabv3_mobilevit_s":
   _lowercase							      =torch.tensor(
       [
           [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
           [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
           [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
       ]						)
  elif mobilevit_name == "deeplabv3_mobilevit_xs":
   _lowercase							      =torch.tensor(
       [
           [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
           [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
           [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
       ]						)
  elif mobilevit_name == "deeplabv3_mobilevit_xxs":
   _lowercase							      =torch.tensor(
       [
           [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
           [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
           [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
       ]						)
  else:
   raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"						)

  assert torch.allclose(logits[0, :3, :3, :3]     ,						__snake_case     ,						atol=1e-4						)
 else:
  assert logits.shape == (1, 1000)

  if mobilevit_name == "mobilevit_s":
   _lowercase							      =torch.tensor([-0.98_66, 0.23_92, -1.12_41]						)
  elif mobilevit_name == "mobilevit_xs":
   _lowercase							      =torch.tensor([-2.47_61, -0.93_99, -1.95_87]						)
  elif mobilevit_name == "mobilevit_xxs":
   _lowercase							      =torch.tensor([-1.93_64, -1.23_27, -0.46_53]						)
  else:
   raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"						)

  assert torch.allclose(logits[0, :3]     ,						__snake_case     ,						atol=1e-4						)

 Path(__snake_case						).mkdir(exist_ok=__snake_case						)
 print(F"Saving model {mobilevit_name} 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 push_to_hub:
  _lowercase							      ={
      '''mobilevit_s''': '''mobilevit-small''',
      '''mobilevit_xs''': '''mobilevit-x-small''',
      '''mobilevit_xxs''': '''mobilevit-xx-small''',
      '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
      '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
      '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
  }

  print('''Pushing to the hub...'''						)
  _lowercase							      =model_mapping[mobilevit_name]
  image_processor.push_to_hub(__snake_case     ,						organization='''apple'''						)
  model.push_to_hub(__snake_case     ,						organization='''apple'''						)


if __name__ == "__main__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--mobilevit_name''',
      default='''mobilevit_s''',
      type=str,
      help=(
          '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
          ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
      ),
  )
  parser.add_argument(
      '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
  )
  parser.add_argument(
      '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
  )

  UpperCAmelCase__   	=							parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )

