


"""simple docstring"""







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 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    "DiagonalGaussianDistribution"


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


       a  :    List[Any]       =True

       @register_to_config
       def __init__(						self		,					snake_case__ = 3		,					snake_case__ = 3		,					snake_case__ = ("DownEncoderBlock2D",)		,					snake_case__ = ("UpDecoderBlock2D",)		,					snake_case__ = (64,)		,					snake_case__ = 1		,					snake_case__ = "silu"		,					snake_case__ = 4		,					snake_case__ = 32		,					snake_case__ = 32		,					snake_case__ = 0.18215		,					):






         """simple docstring"""





         super().__init__()

         # pass init params to Encoder
         lowerCAmelCase					:					int								=							Encoder(
             in_channels=snake_case__		,					out_channels=snake_case__		,					down_block_types=snake_case__		,					block_out_channels=snake_case__		,					layers_per_block=snake_case__		,					act_fn=snake_case__		,					norm_num_groups=snake_case__		,					double_z=snake_case__		,					)

         # pass init params to Decoder
         lowerCAmelCase					:					Optional[int]								=							Decoder(
             in_channels=snake_case__		,					out_channels=snake_case__		,					up_block_types=snake_case__		,					block_out_channels=snake_case__		,					layers_per_block=snake_case__		,					norm_num_groups=snake_case__		,					act_fn=snake_case__		,					)

         lowerCAmelCase					:					List[str]								=							nn.Convad(2 * latent_channels		,					2 * latent_channels		,					1						)
         lowerCAmelCase					:					Optional[int]								=							nn.Convad(snake_case__		,					snake_case__		,					1						)

         lowerCAmelCase					:					Tuple								=							False
         lowerCAmelCase					:					Any								=							False

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

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__=False						):






         """simple docstring"""





         if isinstance(snake_case__		,					(Encoder, Decoder)						):
           lowerCAmelCase					:					Union[str, Any]								=							value

       def 	lowercase__							(						self		,					snake_case__ = True						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							use_tiling

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.enable_tiling(snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							True

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							False

       @property
       # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							{}

         def fn_recursive_add_processors(snake_case__		,					snake_case__		,					snake_case__						):
           if hasattr(snake_case__		,					"set_processor"						):
             lowerCAmelCase					:					Union[str, Any]								=							module.processor

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

           return processors

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

         return processors

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							len(self.attn_processors.keys()						)

         if isinstance(snake_case__		,					snake_case__						) and len(snake_case__						) != count:
           raise ValueError(
               f"""A dict of processors was passed, but the number of processors {len(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(snake_case__		,					snake_case__		,					snake_case__						):
           if hasattr(snake_case__		,					"set_processor"						):
             if not isinstance(snake_case__		,					snake_case__						):
               module.set_processor(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}"""		,					snake_case__		,					snake_case__						)

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.set_attn_processor(AttnProcessor()						)

       @apply_forward_hook
       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = True						):






         """simple docstring"""





         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(snake_case__		,					return_dict=snake_case__						)

         if self.use_slicing and x.shape[0] > 1:
           lowerCAmelCase					:					Tuple								=							[self.encoder(snake_case__						) for x_slice in x.split(1						)]
           lowerCAmelCase					:					List[Any]								=							torch.cat(snake_case__						)
         else:
           lowerCAmelCase					:					Optional[Any]								=							self.encoder(snake_case__						)

         lowerCAmelCase					:					Tuple								=							self.quant_conv(snake_case__						)
         lowerCAmelCase					:					Any								=							DiagonalGaussianDistribution(snake_case__						)

         if not return_dict:
           return (posterior,)

         return AutoencoderKLOutput(latent_dist=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = True						):






         """simple docstring"""





         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(snake_case__		,					return_dict=snake_case__						)

         lowerCAmelCase					:					List[Any]								=							self.post_quant_conv(snake_case__						)
         lowerCAmelCase					:					Any								=							self.decoder(snake_case__						)

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=snake_case__						)

       @apply_forward_hook
       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = True						):






         """simple docstring"""





         if self.use_slicing and z.shape[0] > 1:
           lowerCAmelCase					:					Optional[int]								=							[self._decode(snake_case__						).sample for z_slice in z.split(1						)]
           lowerCAmelCase					:					List[str]								=							torch.cat(snake_case__						)
         else:
           lowerCAmelCase					:					Optional[int]								=							self._decode(snake_case__						).sample

         if not return_dict:
           return (decoded,)

         return DecoderOutput(sample=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							min(a.shape[2]		,					b.shape[2]		,					snake_case__						)
         for y in range(snake_case__						):
           lowerCAmelCase					:					int								=							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							min(a.shape[3]		,					b.shape[3]		,					snake_case__						)
         for x in range(snake_case__						):
           lowerCAmelCase					:					List[str]								=							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
         return b

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = True						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)						)
         lowerCAmelCase					:					List[str]								=							int(self.tile_latent_min_size * self.tile_overlap_factor						)
         lowerCAmelCase					:					Union[str, Any]								=							self.tile_latent_min_size - blend_extent

         # Split the image into 512x512 tiles and encode them separately.
         lowerCAmelCase					:					int								=							[]
         for i in range(0		,					x.shape[2]		,					snake_case__						):
           lowerCAmelCase					:					int								=							[]
           for j in range(0		,					x.shape[3]		,					snake_case__						):
             lowerCAmelCase					:					List[str]								=							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
             lowerCAmelCase					:					List[str]								=							self.encoder(snake_case__						)
             lowerCAmelCase					:					List[Any]								=							self.quant_conv(snake_case__						)
             row.append(snake_case__						)
           rows.append(snake_case__						)
         lowerCAmelCase					:					List[Any]								=							[]
         for i, row in enumerate(snake_case__						):
           lowerCAmelCase					:					List[Any]								=							[]
           for j, tile in enumerate(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:
               lowerCAmelCase					:					Any								=							self.blend_v(rows[i - 1][j]		,					snake_case__		,					snake_case__						)
             if j > 0:
               lowerCAmelCase					:					Dict								=							self.blend_h(row[j - 1]		,					snake_case__		,					snake_case__						)
             result_row.append(tile[:, :, :row_limit, :row_limit]						)
           result_rows.append(torch.cat(snake_case__		,					dim=3						)						)

         lowerCAmelCase					:					Optional[int]								=							torch.cat(snake_case__		,					dim=2						)
         lowerCAmelCase					:					Any								=							DiagonalGaussianDistribution(snake_case__						)

         if not return_dict:
           return (posterior,)

         return AutoencoderKLOutput(latent_dist=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = True						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)						)
         lowerCAmelCase					:					int								=							int(self.tile_sample_min_size * self.tile_overlap_factor						)
         lowerCAmelCase					:					Dict								=							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.
         lowerCAmelCase					:					Dict								=							[]
         for i in range(0		,					z.shape[2]		,					snake_case__						):
           lowerCAmelCase					:					Optional[Any]								=							[]
           for j in range(0		,					z.shape[3]		,					snake_case__						):
             lowerCAmelCase					:					int								=							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
             lowerCAmelCase					:					List[str]								=							self.post_quant_conv(snake_case__						)
             lowerCAmelCase					:					Optional[Any]								=							self.decoder(snake_case__						)
             row.append(snake_case__						)
           rows.append(snake_case__						)
         lowerCAmelCase					:					List[str]								=							[]
         for i, row in enumerate(snake_case__						):
           lowerCAmelCase					:					List[str]								=							[]
           for j, tile in enumerate(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:
               lowerCAmelCase					:					Dict								=							self.blend_v(rows[i - 1][j]		,					snake_case__		,					snake_case__						)
             if j > 0:
               lowerCAmelCase					:					Dict								=							self.blend_h(row[j - 1]		,					snake_case__		,					snake_case__						)
             result_row.append(tile[:, :, :row_limit, :row_limit]						)
           result_rows.append(torch.cat(snake_case__		,					dim=3						)						)

         lowerCAmelCase					:					Tuple								=							torch.cat(snake_case__		,					dim=2						)
         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=snake_case__						)







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = False		,					snake_case__ = True		,					snake_case__ = None		,					):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							sample
         lowerCAmelCase					:					Dict								=							self.encode(snake_case__						).latent_dist
         if sample_posterior:
           lowerCAmelCase					:					str								=							posterior.sample(generator=snake_case__						)
         else:
           lowerCAmelCase					:					str								=							posterior.mode()
         lowerCAmelCase					:					Optional[Any]								=							self.decode(snake_case__						).sample

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=snake_case__						)









"""simple docstring"""







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):  # picklable for multiprocessing


  '''simple docstring'''







  return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def  a__      (    ):


  '''simple docstring'''







  with parallel_backend("spark"    ):
    assert ParallelBackendConfig.backend_name == "spark"

  lowerCAmelCase					:					List[str]								=							[1, 2, 3]
  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=2    )

  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=-1    )





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,				[2, -1]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							[1, 2]
  lowerCAmelCase					:					int								=							{"a": 1, "b": 2}
  lowerCAmelCase					:					List[str]								=							{"a": [1, 2], "b": [3, 4]}
  lowerCAmelCase					:					Dict								=							{"a": {"1": 1}, "b": 2}
  lowerCAmelCase					:					Tuple								=							{"a": 1, "b": 2, "c": 3, "d": 4}
  lowerCAmelCase					:					Any								=							[2, 3]
  lowerCAmelCase					:					Any								=							{"a": 2, "b": 3}
  lowerCAmelCase					:					Optional[int]								=							{"a": [2, 3], "b": [4, 5]}
  lowerCAmelCase					:					Optional[int]								=							{"a": {"1": 2}, "b": 3}
  lowerCAmelCase					:					str								=							{"a": 2, "b": 3, "c": 4, "d": 5}

  with parallel_backend("spark"    ):
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa





