




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      old_name

     if "patch_embed" in old_name:
          UpperCAmelCase_      :							str       =      old_name.split('''.'''				)

          if layer == "0":
               UpperCAmelCase_      :							Optional[Any]       =      old_name.replace('''0'''			,						'''convolution1'''				)
          elif layer == "1":
               UpperCAmelCase_      :							int       =      old_name.replace('''1'''			,						'''batchnorm_before'''				)
          elif layer == "3":
               UpperCAmelCase_      :							int       =      old_name.replace('''3'''			,						'''convolution2'''				)
          else:
               UpperCAmelCase_      :							Union[str, Any]       =      old_name.replace('''4'''			,						'''batchnorm_after'''				)

     if "network" in old_name and re.search(r'''\d\.\d'''			,						_lowercase				):
          UpperCAmelCase_      :							List[Any]       =      r'''\b\d{2}\b'''
          if bool(re.search(_lowercase			,						_lowercase				)				):
               UpperCAmelCase_      :							Optional[int]       =      re.search(r'''\d\.\d\d.'''			,						_lowercase				).group()
          else:
               UpperCAmelCase_      :							Tuple       =      re.search(r'''\d\.\d.'''			,						_lowercase				).group()
          if int(match[0]				) < 6:
               UpperCAmelCase_      :							Union[str, Any]       =      old_name.replace(_lowercase			,						''''''				)
               UpperCAmelCase_      :							Union[str, Any]       =      trimmed_name.replace('''network'''			,						match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]				)
               UpperCAmelCase_      :							Dict       =      '''intermediate_stages.''' + trimmed_name
          else:
               UpperCAmelCase_      :							Optional[int]       =      old_name.replace(_lowercase			,						''''''				)
               if int(match[2]				) < num_meta4D_last_stage:
                    UpperCAmelCase_      :							Union[str, Any]       =      trimmed_name.replace('''network'''			,						'''meta4D_layers.blocks.''' + match[2]				)
               else:
                    UpperCAmelCase_      :							Optional[int]       =      str(int(match[2]				) - num_meta4D_last_stage				)
                    UpperCAmelCase_      :							Dict       =      trimmed_name.replace('''network'''			,						'''meta3D_layers.blocks.''' + layer_index				)
                    if "norm1" in old_name:
                         UpperCAmelCase_      :							int       =      trimmed_name.replace('''norm1'''			,						'''layernorm1'''				)
                    elif "norm2" in old_name:
                         UpperCAmelCase_      :							Optional[Any]       =      trimmed_name.replace('''norm2'''			,						'''layernorm2'''				)
                    elif "fc1" in old_name:
                         UpperCAmelCase_      :							int       =      trimmed_name.replace('''fc1'''			,						'''linear_in'''				)
                    elif "fc2" in old_name:
                         UpperCAmelCase_      :							List[Any]       =      trimmed_name.replace('''fc2'''			,						'''linear_out'''				)

               UpperCAmelCase_      :							str       =      '''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(r'''.\d.'''			,						_lowercase				):
          UpperCAmelCase_      :							Tuple       =      old_name.replace('''network'''			,						'''intermediate_stages'''				)

     if "fc" in new_name:
          UpperCAmelCase_      :							Any       =      new_name.replace('''fc'''			,						'''convolution'''				)
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          UpperCAmelCase_      :							Any       =      new_name.replace('''norm1'''			,						'''batchnorm_before'''				)
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          UpperCAmelCase_      :							List[Any]       =      new_name.replace('''norm2'''			,						'''batchnorm_after'''				)
     if "proj" in new_name:
          UpperCAmelCase_      :							Tuple       =      new_name.replace('''proj'''			,						'''projection'''				)
     if "dist_head" in new_name:
          UpperCAmelCase_      :							List[Any]       =      new_name.replace('''dist_head'''			,						'''distillation_classifier'''				)
     elif "head" in new_name:
          UpperCAmelCase_      :							Optional[Any]       =      new_name.replace('''head'''			,						'''classifier'''				)
     elif "patch_embed" in new_name:
          UpperCAmelCase_      :							Dict       =      '''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          UpperCAmelCase_      :							Any       =      new_name.replace('''norm'''			,						'''layernorm'''				)
          UpperCAmelCase_      :							List[str]       =      '''efficientformer.''' + new_name
     else:
          UpperCAmelCase_      :							Optional[int]       =      '''efficientformer.encoder.''' + new_name

     return new_name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in checkpoint.copy().keys():
          UpperCAmelCase_      :							Optional[Any]       =      checkpoint.pop(_lowercase				)
          UpperCAmelCase_      :							Optional[Any]       =      val

     return checkpoint







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_      :							int       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)

     return image







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      torch.load(_lowercase			,						map_location='''cpu'''				)['''model''']
     UpperCAmelCase_      :							int       =      EfficientFormerConfig.from_json_file(_lowercase				)
     UpperCAmelCase_      :							str       =      EfficientFormerForImageClassificationWithTeacher(_lowercase				)
     UpperCAmelCase_      :							List[Any]       =      '''_'''.join(checkpoint_path.split('''/'''				)[-1].split('''.'''				)[0].split('''_'''				)[:-1]				)

     UpperCAmelCase_      :							List[Any]       =      config.depths[-1] - config.num_metaad_blocks + 1
     UpperCAmelCase_      :							List[str]       =      convert_torch_checkpoint(_lowercase			,						_lowercase				)

     model.load_state_dict(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							int       =      {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     UpperCAmelCase_      :							int       =      prepare_img()
     UpperCAmelCase_      :							Union[str, Any]       =      256
     UpperCAmelCase_      :							Dict       =      224
     UpperCAmelCase_      :							Union[str, Any]       =      EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size}			,						crop_size={'''height''': crop_size, '''width''': crop_size}			,						resample=pillow_resamplings['''bicubic''']			,						)
     UpperCAmelCase_      :							List[str]       =      processor(images=_lowercase			,						return_tensors='''pt'''				).pixel_values

     # original processing pipeline
     UpperCAmelCase_      :							str       =      Compose(
         [
             Resize(_lowercase			,						interpolation=pillow_resamplings['''bicubic''']				),
             CenterCrop(_lowercase				),
             ToTensor(),
             Normalize(_lowercase			,						_lowercase				),
         ]				)
     UpperCAmelCase_      :							Any       =      image_transforms(_lowercase				).unsqueeze(0				)

     assert torch.allclose(_lowercase			,						_lowercase				)

     UpperCAmelCase_      :							Any       =      model(_lowercase				)
     UpperCAmelCase_      :							int       =      outputs.logits

     UpperCAmelCase_      :							int       =      (1, 1000)

     if "l1" in model_name:
          UpperCAmelCase_      :							Dict       =      torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]				)
          assert torch.allclose(logits[0, :10]			,						_lowercase			,						atol=1E-3				)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          UpperCAmelCase_      :							Optional[Any]       =      torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]				)
          assert torch.allclose(logits[0, :10]			,						_lowercase			,						atol=1E-3				)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          UpperCAmelCase_      :							Any       =      torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]				)
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''				)

     # Save Checkpoints
     Path(_lowercase				).mkdir(exist_ok=_lowercase				)
     model.save_pretrained(_lowercase				)
     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
     processor.save_pretrained(_lowercase				)
     print(f'''Processor successfuly saved at {pytorch_dump_path}'''				)

     if push_to_hub:
          print('''Pushing model to the hub...'''				)

          model.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''			,						commit_message='''Add model'''			,						use_temp_dir=_lowercase			,						)
          processor.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''			,						commit_message='''Add image processor'''			,						use_temp_dir=_lowercase			,						)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path',
         default=None,
         type=str,
         required=True,
         help='Path to EfficientFormer pytorch checkpoint.',
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for EfficientFormer model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )

     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
     parser.add_argument(
         '--no-push_to_hub',
         dest='push_to_hub',
         action='store_false',
         help='Do not push model and image processor to the hub',
     )
     parser.set_defaults(push_to_hub=True)

     __a          =			parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )










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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				42


class   __a(					_a    ,						_a       ):






    """simple docstring"""





    lowerCAmelCase        =				True





    @register_to_config
    def __init__(				self       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = ("DownEncoderBlock2D",)       ,_SCREAMING_SNAKE_CASE = ("UpDecoderBlock2D",)       ,_SCREAMING_SNAKE_CASE = (64,)       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = "silu"       ,_SCREAMING_SNAKE_CASE = 4       ,_SCREAMING_SNAKE_CASE = 32       ,_SCREAMING_SNAKE_CASE = 32       ,_SCREAMING_SNAKE_CASE = 0.1_82_15       ,)			->		Optional[int]:
         super().__init__()

         # pass init params to Encoder
         UpperCAmelCase_      :							List[Any]       =      Encoder(
             in_channels=_SCREAMING_SNAKE_CASE       ,out_channels=_SCREAMING_SNAKE_CASE       ,down_block_types=_SCREAMING_SNAKE_CASE       ,block_out_channels=_SCREAMING_SNAKE_CASE       ,layers_per_block=_SCREAMING_SNAKE_CASE       ,act_fn=_SCREAMING_SNAKE_CASE       ,norm_num_groups=_SCREAMING_SNAKE_CASE       ,double_z=_SCREAMING_SNAKE_CASE       ,)

         # pass init params to Decoder
         UpperCAmelCase_      :							List[str]       =      Decoder(
             in_channels=_SCREAMING_SNAKE_CASE       ,out_channels=_SCREAMING_SNAKE_CASE       ,up_block_types=_SCREAMING_SNAKE_CASE       ,block_out_channels=_SCREAMING_SNAKE_CASE       ,layers_per_block=_SCREAMING_SNAKE_CASE       ,norm_num_groups=_SCREAMING_SNAKE_CASE       ,act_fn=_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							int       =      nn.Convad(2 * latent_channels       ,2 * latent_channels       ,1  )
         UpperCAmelCase_      :							Union[str, Any]       =      nn.Convad(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,1  )

         UpperCAmelCase_      :							Optional[Any]       =      False
         UpperCAmelCase_      :							Dict       =      False

         # only relevant if vae tiling is enabled
         UpperCAmelCase_      :							List[Any]       =      self.config.sample_size
         UpperCAmelCase_      :							List[str]       =      (
             self.config.sample_size[0]
             if isinstance(self.config.sample_size       ,(list, tuple)  )
             else self.config.sample_size
         )
         UpperCAmelCase_      :							List[str]       =      int(sample_size / (2 ** (len(self.config.block_out_channels  ) - 1))  )
         UpperCAmelCase_      :							int       =      0.25





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		int:
         if isinstance(_SCREAMING_SNAKE_CASE       ,(Encoder, Decoder)  ):
              UpperCAmelCase_      :							Union[str, Any]       =      value





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE = True  )			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      use_tiling





    def 							a__				(				self  )			->		Optional[Any]:
         self.enable_tiling(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							str       =      True





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Any       =      False





    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def 							a__				(				self  )			->		Dict[str, AttentionProcessor]:
         UpperCAmelCase_      :							int       =      {}

         def fn_recursive_add_processors(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              if hasattr(_SCREAMING_SNAKE_CASE       ,'''set_processor'''  ):
                   UpperCAmelCase_      :							Tuple       =      module.processor

              for sub_name, child in module.named_children():
                   fn_recursive_add_processors(f'''{name}.{sub_name}'''       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

              return processors

         for name, module in self.named_children():
              fn_recursive_add_processors(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         return processors





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      len(self.attn_processors.keys()  )

         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and len(_SCREAMING_SNAKE_CASE  ) != count:
              raise ValueError(
                  f'''A dict of processors was passed, but the number of processors {len(_SCREAMING_SNAKE_CASE  )} does not match the'''
                  f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''  )

         def fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              if hasattr(_SCREAMING_SNAKE_CASE       ,'''set_processor'''  ):
                   if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                        module.set_processor(_SCREAMING_SNAKE_CASE  )
                   else:
                        module.set_processor(processor.pop(f'''{name}.processor'''  )  )

              for sub_name, child in module.named_children():
                   fn_recursive_attn_processor(f'''{name}.{sub_name}'''       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         for name, module in self.named_children():
              fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         self.set_attn_processor(AttnProcessor()  )





    @apply_forward_hook
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		AutoencoderKLOutput:
         if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
              return self.tiled_encode(_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

         if self.use_slicing and x.shape[0] > 1:
              UpperCAmelCase_      :							int       =      [self.encoder(_SCREAMING_SNAKE_CASE  ) for x_slice in x.split(1  )]
              UpperCAmelCase_      :							List[str]       =      torch.cat(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Optional[Any]       =      self.encoder(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      self.quant_conv(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      DiagonalGaussianDistribution(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (posterior,)

         return AutoencoderKLOutput(latent_dist=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		Union[DecoderOutput, torch.FloatTensor]:
         if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
              return self.tiled_decode(_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      self.post_quant_conv(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      self.decoder(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )





    @apply_forward_hook
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		Union[DecoderOutput, torch.FloatTensor]:
         if self.use_slicing and z.shape[0] > 1:
              UpperCAmelCase_      :							Tuple       =      [self._decode(_SCREAMING_SNAKE_CASE  ).sample for z_slice in z.split(1  )]
              UpperCAmelCase_      :							str       =      torch.cat(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							str       =      self._decode(_SCREAMING_SNAKE_CASE  ).sample

         if not return_dict:
              return (decoded,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							str       =      min(a.shape[2]       ,b.shape[2]       ,_SCREAMING_SNAKE_CASE  )
         for y in range(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							str       =      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Any       =      min(a.shape[3]       ,b.shape[3]       ,_SCREAMING_SNAKE_CASE  )
         for x in range(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							List[str]       =      a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
         return b





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		AutoencoderKLOutput:
         UpperCAmelCase_      :							Optional[int]       =      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)  )
         UpperCAmelCase_      :							Any       =      int(self.tile_latent_min_size * self.tile_overlap_factor  )
         UpperCAmelCase_      :							str       =      self.tile_latent_min_size - blend_extent

         # Split the image into 512x512 tiles and encode them separately.
         UpperCAmelCase_      :							Tuple       =      []
         for i in range(0       ,x.shape[2]       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							List[str]       =      []
              for j in range(0       ,x.shape[3]       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                   UpperCAmelCase_      :							int       =      self.encoder(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							str       =      self.quant_conv(_SCREAMING_SNAKE_CASE  )
                   row.append(_SCREAMING_SNAKE_CASE  )
              rows.append(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      []
         for i, row in enumerate(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							str       =      []
              for j, tile in enumerate(_SCREAMING_SNAKE_CASE  ):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                        UpperCAmelCase_      :							Optional[Any]       =      self.blend_v(rows[i - 1][j]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   if j > 0:
                        UpperCAmelCase_      :							List[str]       =      self.blend_h(row[j - 1]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   result_row.append(tile[:, :, :row_limit, :row_limit]  )
              result_rows.append(torch.cat(_SCREAMING_SNAKE_CASE       ,dim=3  )  )

         UpperCAmelCase_      :							Optional[Any]       =      torch.cat(_SCREAMING_SNAKE_CASE       ,dim=2  )
         UpperCAmelCase_      :							Union[str, Any]       =      DiagonalGaussianDistribution(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (posterior,)

         return AutoencoderKLOutput(latent_dist=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		Union[DecoderOutput, torch.FloatTensor]:
         UpperCAmelCase_      :							Optional[Any]       =      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)  )
         UpperCAmelCase_      :							str       =      int(self.tile_sample_min_size * self.tile_overlap_factor  )
         UpperCAmelCase_      :							Any       =      self.tile_sample_min_size - blend_extent

         # Split z into overlapping 64x64 tiles and decode them separately.
         # The tiles have an overlap to avoid seams between tiles.
         UpperCAmelCase_      :							Tuple       =      []
         for i in range(0       ,z.shape[2]       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Tuple       =      []
              for j in range(0       ,z.shape[3]       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							Optional[int]       =      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                   UpperCAmelCase_      :							List[str]       =      self.post_quant_conv(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Dict       =      self.decoder(_SCREAMING_SNAKE_CASE  )
                   row.append(_SCREAMING_SNAKE_CASE  )
              rows.append(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      []
         for i, row in enumerate(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							int       =      []
              for j, tile in enumerate(_SCREAMING_SNAKE_CASE  ):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                        UpperCAmelCase_      :							Optional[int]       =      self.blend_v(rows[i - 1][j]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   if j > 0:
                        UpperCAmelCase_      :							Optional[int]       =      self.blend_h(row[j - 1]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   result_row.append(tile[:, :, :row_limit, :row_limit]  )
              result_rows.append(torch.cat(_SCREAMING_SNAKE_CASE       ,dim=3  )  )

         UpperCAmelCase_      :							Dict       =      torch.cat(_SCREAMING_SNAKE_CASE       ,dim=2  )
         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,)			->		Union[DecoderOutput, torch.FloatTensor]:
         UpperCAmelCase_      :							str       =      sample
         UpperCAmelCase_      :							Optional[Any]       =      self.encode(_SCREAMING_SNAKE_CASE  ).latent_dist
         if sample_posterior:
              UpperCAmelCase_      :							List[Any]       =      posterior.sample(generator=_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Union[str, Any]       =      posterior.mode()
         UpperCAmelCase_      :							List[Any]       =      self.decode(_SCREAMING_SNAKE_CASE  ).sample

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )