

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     Optional[int]=1  )							->		int:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('.'  )[n_shave_prefix_segments:]  )
  else:
    return ".".join(path.split('.'  )[:n_shave_prefix_segments]  )





def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     List[str]=0  )							->		Optional[Any]:
  __UpperCAmelCase	:  Union[str, Any]		=     []
  for old_item in old_list:
    __UpperCAmelCase	:  Union[str, Any]		=     old_item
    __UpperCAmelCase	:  Tuple		=     new_item.replace('block.'    ,       'resnets.'  )
    __UpperCAmelCase	:  Dict		=     new_item.replace('conv_shorcut'    ,       'conv1'  )
    __UpperCAmelCase	:  Optional[int]		=     new_item.replace('in_shortcut'    ,       'conv_shortcut'  )
    __UpperCAmelCase	:  Optional[Any]		=     new_item.replace('temb_proj'    ,       'time_emb_proj'  )

    __UpperCAmelCase	:  Any		=     shave_segments(snake_case_    ,       n_shave_prefix_segments=snake_case_  )

    mapping.append({'old': old_item, 'new': new_item}  )

  return mapping





def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     Tuple=0    ,       snake_case_					:     Optional[int]=False  )							->		Tuple:
  __UpperCAmelCase	:  str		=     []
  for old_item in old_list:
    __UpperCAmelCase	:  Dict		=     old_item

    # In `model.mid`, the layer is called `attn`.
    if not in_mid:
      __UpperCAmelCase	:  Union[str, Any]		=     new_item.replace('attn'    ,       'attentions'  )
    __UpperCAmelCase	:  Dict		=     new_item.replace('.k.'    ,       '.key.'  )
    __UpperCAmelCase	:  Dict		=     new_item.replace('.v.'    ,       '.value.'  )
    __UpperCAmelCase	:  List[Any]		=     new_item.replace('.q.'    ,       '.query.'  )

    __UpperCAmelCase	:  List[str]		=     new_item.replace('proj_out'    ,       'proj_attn'  )
    __UpperCAmelCase	:  List[Any]		=     new_item.replace('norm'    ,       'group_norm'  )

    __UpperCAmelCase	:  str		=     shave_segments(snake_case_    ,       n_shave_prefix_segments=snake_case_  )
    mapping.append({'old': old_item, 'new': new_item}  )

  return mapping





def  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     Any    ,       snake_case_					:     Optional[Any]    ,       snake_case_					:     str=None    ,       snake_case_					:     Tuple=None    ,       snake_case_					:     Tuple=None  )							->		Dict:
  assert isinstance(snake_case_    ,       snake_case_  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  if attention_paths_to_split is not None:
    if config is None:
      raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'  )

    for path, path_map in attention_paths_to_split.items():
      __UpperCAmelCase	:  List[Any]		=     old_checkpoint[path]
      __UpperCAmelCase	:  Any		=     old_tensor.shape[0] // 3

      __UpperCAmelCase	:  List[Any]		=     (-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

      __UpperCAmelCase	:  List[str]		=     old_tensor.shape[0] // config.get('num_head_channels'    ,       1  ) // 3

      __UpperCAmelCase	:  Union[str, Any]		=     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
      __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     old_tensor.split(channels // num_heads    ,       dim=1  )

      __UpperCAmelCase	:  Any		=     query.reshape(snake_case_  ).squeeze()
      __UpperCAmelCase	:  Tuple		=     key.reshape(snake_case_  ).squeeze()
      __UpperCAmelCase	:  Tuple		=     value.reshape(snake_case_  ).squeeze()

  for path in paths:
    __UpperCAmelCase	:  Union[str, Any]		=     path['new']

    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    __UpperCAmelCase	:  List[Any]		=     new_path.replace('down.'    ,       'down_blocks.'  )
    __UpperCAmelCase	:  str		=     new_path.replace('up.'    ,       'up_blocks.'  )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        __UpperCAmelCase	:  Optional[Any]		=     new_path.replace(replacement['old']    ,       replacement['new']  )

    if "attentions" in new_path:
      __UpperCAmelCase	:  str		=     old_checkpoint[path['old']].squeeze()
    else:
      __UpperCAmelCase	:  str		=     old_checkpoint[path['old']]





def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     Any  )							->		Dict:
  __UpperCAmelCase	:  List[str]		=     {}

  __UpperCAmelCase	:  int		=     checkpoint['temb.dense.0.weight']
  __UpperCAmelCase	:  Union[str, Any]		=     checkpoint['temb.dense.0.bias']
  __UpperCAmelCase	:  str		=     checkpoint['temb.dense.1.weight']
  __UpperCAmelCase	:  Tuple		=     checkpoint['temb.dense.1.bias']

  __UpperCAmelCase	:  Tuple		=     checkpoint['norm_out.weight']
  __UpperCAmelCase	:  Any		=     checkpoint['norm_out.bias']

  __UpperCAmelCase	:  int		=     checkpoint['conv_in.weight']
  __UpperCAmelCase	:  Dict		=     checkpoint['conv_in.bias']
  __UpperCAmelCase	:  Any		=     checkpoint['conv_out.weight']
  __UpperCAmelCase	:  Any		=     checkpoint['conv_out.bias']

  __UpperCAmelCase	:  int		=     len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'down' in layer}  )
  __UpperCAmelCase	:  Any		=     {
      layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(snake_case_  )
  }

  __UpperCAmelCase	:  Dict		=     len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'up' in layer}  )
  __UpperCAmelCase	:  Optional[Any]		=     {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(snake_case_  )}

  for i in range(snake_case_  ):
    __UpperCAmelCase	:  Any		=     (i - 1) // (config['layers_per_block'] + 1)

    if any('downsample' in layer for layer in down_blocks[i]  ):
      __UpperCAmelCase	:  Dict		=     checkpoint[
          F"down.{i}.downsample.op.weight"
      ]
      __UpperCAmelCase	:  List[str]		=     checkpoint[F"down.{i}.downsample.op.bias"]
    #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
    #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

    if any('block' in layer for layer in down_blocks[i]  ):
      __UpperCAmelCase	:  int		=     len(
          {'.'.join(shave_segments(snake_case_    ,       2  ).split('.'  )[:2]  ) for layer in down_blocks[i] if 'block' in layer}  )
      __UpperCAmelCase	:  int		=     {
          layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
          for layer_id in range(snake_case_  )
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block']  ):
          __UpperCAmelCase	:  List[str]		=     renew_resnet_paths(blocks[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_  )

    if any('attn' in layer for layer in down_blocks[i]  ):
      __UpperCAmelCase	:  Dict		=     len(
          {'.'.join(shave_segments(snake_case_    ,       2  ).split('.'  )[:2]  ) for layer in down_blocks[i] if 'attn' in layer}  )
      __UpperCAmelCase	:  Optional[int]		=     {
          layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
          for layer_id in range(snake_case_  )
      }

      if num_attn > 0:
        for j in range(config['layers_per_block']  ):
          __UpperCAmelCase	:  Any		=     renew_attention_paths(attns[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_    ,       config=snake_case_  )

  __UpperCAmelCase	:  Union[str, Any]		=     [key for key in checkpoint if 'mid.block_1' in key]
  __UpperCAmelCase	:  List[Any]		=     [key for key in checkpoint if 'mid.block_2' in key]
  __UpperCAmelCase	:  Union[str, Any]		=     [key for key in checkpoint if 'mid.attn_1' in key]

  # Mid new 2
  __UpperCAmelCase	:  int		=     renew_resnet_paths(snake_case_  )
  assign_to_checkpoint(
      snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,       )

  __UpperCAmelCase	:  Any		=     renew_resnet_paths(snake_case_  )
  assign_to_checkpoint(
      snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,       )

  __UpperCAmelCase	:  Optional[int]		=     renew_attention_paths(snake_case_    ,       in_mid=snake_case_  )
  assign_to_checkpoint(
      snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,       )

  for i in range(snake_case_  ):
    __UpperCAmelCase	:  List[Any]		=     num_up_blocks - 1 - i

    if any('upsample' in layer for layer in up_blocks[i]  ):
      __UpperCAmelCase	:  List[Any]		=     checkpoint[
          F"up.{i}.upsample.conv.weight"
      ]
      __UpperCAmelCase	:  Optional[int]		=     checkpoint[F"up.{i}.upsample.conv.bias"]

    if any('block' in layer for layer in up_blocks[i]  ):
      __UpperCAmelCase	:  int		=     len(
          {'.'.join(shave_segments(snake_case_    ,       2  ).split('.'  )[:2]  ) for layer in up_blocks[i] if 'block' in layer}  )
      __UpperCAmelCase	:  List[str]		=     {
          layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(snake_case_  )
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block'] + 1  ):
          __UpperCAmelCase	:  Tuple		=     {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
          __UpperCAmelCase	:  int		=     renew_resnet_paths(blocks[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[replace_indices]  )

    if any('attn' in layer for layer in up_blocks[i]  ):
      __UpperCAmelCase	:  List[str]		=     len(
          {'.'.join(shave_segments(snake_case_    ,       2  ).split('.'  )[:2]  ) for layer in up_blocks[i] if 'attn' in layer}  )
      __UpperCAmelCase	:  Union[str, Any]		=     {
          layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(snake_case_  )
      }

      if num_attn > 0:
        for j in range(config['layers_per_block'] + 1  ):
          __UpperCAmelCase	:  List[str]		=     {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
          __UpperCAmelCase	:  Union[str, Any]		=     renew_attention_paths(attns[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[replace_indices]  )

  __UpperCAmelCase	:  Optional[Any]		=     {k.replace('mid_new_2'    ,       'mid_block'  ): v for k, v in new_checkpoint.items()}
  return new_checkpoint





def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     Union[str, Any]  )							->		Optional[int]:
  __UpperCAmelCase	:  Union[str, Any]		=     {}

  __UpperCAmelCase	:  Any		=     checkpoint['encoder.norm_out.weight']
  __UpperCAmelCase	:  Tuple		=     checkpoint['encoder.norm_out.bias']

  __UpperCAmelCase	:  Any		=     checkpoint['encoder.conv_in.weight']
  __UpperCAmelCase	:  Any		=     checkpoint['encoder.conv_in.bias']
  __UpperCAmelCase	:  Union[str, Any]		=     checkpoint['encoder.conv_out.weight']
  __UpperCAmelCase	:  Dict		=     checkpoint['encoder.conv_out.bias']

  __UpperCAmelCase	:  List[str]		=     checkpoint['decoder.norm_out.weight']
  __UpperCAmelCase	:  List[Any]		=     checkpoint['decoder.norm_out.bias']

  __UpperCAmelCase	:  Optional[Any]		=     checkpoint['decoder.conv_in.weight']
  __UpperCAmelCase	:  Union[str, Any]		=     checkpoint['decoder.conv_in.bias']
  __UpperCAmelCase	:  str		=     checkpoint['decoder.conv_out.weight']
  __UpperCAmelCase	:  int		=     checkpoint['decoder.conv_out.bias']

  __UpperCAmelCase	:  Tuple		=     len({'.'.join(layer.split('.'  )[:3]  ) for layer in checkpoint if 'down' in layer}  )
  __UpperCAmelCase	:  List[Any]		=     {
      layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(snake_case_  )
  }

  __UpperCAmelCase	:  Optional[int]		=     len({'.'.join(layer.split('.'  )[:3]  ) for layer in checkpoint if 'up' in layer}  )
  __UpperCAmelCase	:  Optional[Any]		=     {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(snake_case_  )}

  for i in range(snake_case_  ):
    __UpperCAmelCase	:  Any		=     (i - 1) // (config['layers_per_block'] + 1)

    if any('downsample' in layer for layer in down_blocks[i]  ):
      __UpperCAmelCase	:  Optional[int]		=     checkpoint[
          F"encoder.down.{i}.downsample.conv.weight"
      ]
      __UpperCAmelCase	:  Any		=     checkpoint[
          F"encoder.down.{i}.downsample.conv.bias"
      ]

    if any('block' in layer for layer in down_blocks[i]  ):
      __UpperCAmelCase	:  List[Any]		=     len(
          {'.'.join(shave_segments(snake_case_    ,       3  ).split('.'  )[:3]  ) for layer in down_blocks[i] if 'block' in layer}  )
      __UpperCAmelCase	:  Union[str, Any]		=     {
          layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
          for layer_id in range(snake_case_  )
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block']  ):
          __UpperCAmelCase	:  Union[str, Any]		=     renew_resnet_paths(blocks[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_  )

    if any('attn' in layer for layer in down_blocks[i]  ):
      __UpperCAmelCase	:  Union[str, Any]		=     len(
          {'.'.join(shave_segments(snake_case_    ,       3  ).split('.'  )[:3]  ) for layer in down_blocks[i] if 'attn' in layer}  )
      __UpperCAmelCase	:  Union[str, Any]		=     {
          layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
          for layer_id in range(snake_case_  )
      }

      if num_attn > 0:
        for j in range(config['layers_per_block']  ):
          __UpperCAmelCase	:  Dict		=     renew_attention_paths(attns[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_    ,       config=snake_case_  )

  __UpperCAmelCase	:  Union[str, Any]		=     [key for key in checkpoint if 'mid.block_1' in key]
  __UpperCAmelCase	:  Any		=     [key for key in checkpoint if 'mid.block_2' in key]
  __UpperCAmelCase	:  Tuple		=     [key for key in checkpoint if 'mid.attn_1' in key]

  # Mid new 2
  __UpperCAmelCase	:  Dict		=     renew_resnet_paths(snake_case_  )
  assign_to_checkpoint(
      snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,       )

  __UpperCAmelCase	:  str		=     renew_resnet_paths(snake_case_  )
  assign_to_checkpoint(
      snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,       )

  __UpperCAmelCase	:  List[Any]		=     renew_attention_paths(snake_case_    ,       in_mid=snake_case_  )
  assign_to_checkpoint(
      snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,       )

  for i in range(snake_case_  ):
    __UpperCAmelCase	:  Any		=     num_up_blocks - 1 - i

    if any('upsample' in layer for layer in up_blocks[i]  ):
      __UpperCAmelCase	:  Optional[int]		=     checkpoint[
          F"decoder.up.{i}.upsample.conv.weight"
      ]
      __UpperCAmelCase	:  List[str]		=     checkpoint[
          F"decoder.up.{i}.upsample.conv.bias"
      ]

    if any('block' in layer for layer in up_blocks[i]  ):
      __UpperCAmelCase	:  Tuple		=     len(
          {'.'.join(shave_segments(snake_case_    ,       3  ).split('.'  )[:3]  ) for layer in up_blocks[i] if 'block' in layer}  )
      __UpperCAmelCase	:  Any		=     {
          layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(snake_case_  )
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block'] + 1  ):
          __UpperCAmelCase	:  Any		=     {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
          __UpperCAmelCase	:  Tuple		=     renew_resnet_paths(blocks[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[replace_indices]  )

    if any('attn' in layer for layer in up_blocks[i]  ):
      __UpperCAmelCase	:  Union[str, Any]		=     len(
          {'.'.join(shave_segments(snake_case_    ,       3  ).split('.'  )[:3]  ) for layer in up_blocks[i] if 'attn' in layer}  )
      __UpperCAmelCase	:  List[str]		=     {
          layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(snake_case_  )
      }

      if num_attn > 0:
        for j in range(config['layers_per_block'] + 1  ):
          __UpperCAmelCase	:  Dict		=     {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
          __UpperCAmelCase	:  Dict		=     renew_attention_paths(attns[j]  )
          assign_to_checkpoint(snake_case_    ,       snake_case_    ,       snake_case_    ,       additional_replacements=[replace_indices]  )

  __UpperCAmelCase	:  Optional[Any]		=     {k.replace('mid_new_2'    ,       'mid_block'  ): v for k, v in new_checkpoint.items()}
  __UpperCAmelCase	:  str		=     checkpoint['quant_conv.weight']
  __UpperCAmelCase	:  str		=     checkpoint['quant_conv.bias']
  if "quantize.embedding.weight" in checkpoint:
    __UpperCAmelCase	:  int		=     checkpoint['quantize.embedding.weight']
  __UpperCAmelCase	:  Dict		=     checkpoint['post_quant_conv.weight']
  __UpperCAmelCase	:  Optional[Any]		=     checkpoint['post_quant_conv.bias']

  return new_checkpoint


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			argparse.ArgumentParser()

      parser.add_argument(
          '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
      )

      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help='''The config json file corresponding to the architecture.''',
      )

      parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

      _SCREAMING_SNAKE_CASE							 =			parser.parse_args()
      _SCREAMING_SNAKE_CASE							 =			torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            _SCREAMING_SNAKE_CASE							 =			json.loads(f.read())

      # unet case
      _SCREAMING_SNAKE_CASE							 =			{key.split('''.''')[0] for key in checkpoint.keys()}
      if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
            _SCREAMING_SNAKE_CASE							 =			convert_vq_autoenc_checkpoint(checkpoint, config)
      else:
            _SCREAMING_SNAKE_CASE							 =			convert_ddpm_checkpoint(checkpoint, config)

      if "ddpm" in config:
            del config["ddpm"]

      if config["_class_name"] == "VQModel":
            _SCREAMING_SNAKE_CASE							 =			VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            _SCREAMING_SNAKE_CASE							 =			AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            _SCREAMING_SNAKE_CASE							 =			UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

            _SCREAMING_SNAKE_CASE							 =			DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

            _SCREAMING_SNAKE_CASE							 =			DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)












'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class 	_lowercase    (				a					):
   _UpperCamelCase									= """mobilevit"""


   def __init__(			self     , _UpperCAmelCase=3     , _UpperCAmelCase=256     , _UpperCAmelCase=2     , _UpperCAmelCase=[144, 192, 240]     , _UpperCAmelCase=[16, 32, 64, 96, 128, 160, 640]     , _UpperCAmelCase=4     , _UpperCAmelCase=2.0     , _UpperCAmelCase=4.0     , _UpperCAmelCase="silu"     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.0     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.02     , _UpperCAmelCase=1E-5     , _UpperCAmelCase=True     , _UpperCAmelCase=256     , _UpperCAmelCase=[6, 12, 18]     , _UpperCAmelCase=0.1     , _UpperCAmelCase=255     , **_UpperCAmelCase     , ):
       super().__init__(**_UpperCAmelCase       )

       A							:							Union[str, Any]				=    num_channels
       A							:							Union[str, Any]				=    image_size
       A							:							List[str]				=    patch_size
       A							:							Any				=    hidden_sizes
       A							:							Dict				=    neck_hidden_sizes
       A							:							int				=    num_attention_heads
       A							:							List[str]				=    mlp_ratio
       A							:							List[Any]				=    expand_ratio
       A							:							Dict				=    hidden_act
       A							:							List[Any]				=    conv_kernel_size
       A							:							List[str]				=    output_stride
       A							:							Dict				=    hidden_dropout_prob
       A							:							List[str]				=    attention_probs_dropout_prob
       A							:							Optional[Any]				=    classifier_dropout_prob
       A							:							Any				=    initializer_range
       A							:							Union[str, Any]				=    layer_norm_eps
       A							:							Union[str, Any]				=    qkv_bias

       # decode head attributes for semantic segmentation
       A							:							int				=    aspp_out_channels
       A							:							Any				=    atrous_rates
       A							:							Tuple				=    aspp_dropout_prob
       A							:							List[Any]				=    semantic_loss_ignore_index







class 	_lowercase    (				a					):
   _UpperCamelCase									= version.parse("""1.11"""					)


   @property
   def        snake_case					(			self       ):
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]       )


   @property
   def        snake_case					(			self       ):
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]       )
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]       )


   @property
   def        snake_case					(			self       ):
       return 1E-4
