

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__ (SCREAMING_SNAKE_CASE__					):
    __lowerCamelCase						:	List[str]														=					42



class 				SCREAMING_SNAKE_CASE__ (SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__					):
    __lowerCamelCase						:	Optional[Any]														=					True





    @register_to_config
    def __init__(				self						,       a = 3						,       a = 3						,       a = ("DownEncoderBlock2D",)						,       a = ("UpDecoderBlock2D",)						,       a = (64,)						,       a = 1						,       a = "silu"						,       a = 4						,       a = 32						,       a = 32						,       a = 0.18_215						,       ):
         super().__init__()

         # pass init params to Encoder
         lowercase__   :       Any				=	Encoder(
             in_channels=a						,       out_channels=a						,       down_block_types=a						,       block_out_channels=a						,       layers_per_block=a						,       act_fn=a						,       norm_num_groups=a						,       double_z=a						,       )

         # pass init params to Decoder
         lowercase__   :       int				=	Decoder(
             in_channels=a						,       out_channels=a						,       up_block_types=a						,       block_out_channels=a						,       layers_per_block=a						,       norm_num_groups=a						,       act_fn=a						,       )

         lowercase__   :       Optional[int]				=	nn.Convad(2 * latent_channels						,       2 * latent_channels						,       1)
         lowercase__   :       Tuple				=	nn.Convad(a						,       a						,       1)

         lowercase__   :       Any				=	False
         lowercase__   :       List[Any]				=	False

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





    def 				snake_case_    (				self						,       a						,       a=False):
         if isinstance(a						,       (Encoder, Decoder)):
              lowercase__   :       int				=	value





    def 				snake_case_    (				self						,       a = True):
         lowercase__   :       List[Any]				=	use_tiling





    def 				snake_case_    (				self):
         self.enable_tiling(a)





    def 				snake_case_    (				self):
         lowercase__   :       str				=	True





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	False





    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	{}

         def fn_recursive_add_processors(a						,       a						,       a):
              if hasattr(a						,       'set_processor'):
                   lowercase__   :       Any				=	module.processor

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

              return processors

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

         return processors





    def 				snake_case_    (				self						,       a):
         lowercase__   :       List[str]				=	len(self.attn_processors.keys())

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

         def fn_recursive_attn_processor(a						,       a						,       a):
              if hasattr(a						,       'set_processor'):
                   if not isinstance(a						,       a):
                        module.set_processor(a)
                   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}"""						,       a						,       a)

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





    def 				snake_case_    (				self):
         self.set_attn_processor(AttnProcessor())





    @apply_forward_hook
    def 				snake_case_    (				self						,       a						,       a = True):
         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(a						,       return_dict=a)

         if self.use_slicing and x.shape[0] > 1:
              lowercase__   :       Union[str, Any]				=	[self.encoder(a) for x_slice in x.split(1)]
              lowercase__   :       str				=	torch.cat(a)
         else:
              lowercase__   :       List[Any]				=	self.encoder(a)

         lowercase__   :       Optional[Any]				=	self.quant_conv(a)
         lowercase__   :       Dict				=	DiagonalGaussianDistribution(a)

         if not return_dict:
              return (posterior,)

         return AutoencoderKLOutput(latent_dist=a)





    def 				snake_case_    (				self						,       a						,       a = True):
         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(a						,       return_dict=a)

         lowercase__   :       Tuple				=	self.post_quant_conv(a)
         lowercase__   :       int				=	self.decoder(a)

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=a)





    @apply_forward_hook
    def 				snake_case_    (				self						,       a						,       a = True):
         if self.use_slicing and z.shape[0] > 1:
              lowercase__   :       Any				=	[self._decode(a).sample for z_slice in z.split(1)]
              lowercase__   :       Any				=	torch.cat(a)
         else:
              lowercase__   :       str				=	self._decode(a).sample

         if not return_dict:
              return (decoded,)

         return DecoderOutput(sample=a)





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       List[str]				=	min(a.shape[2]						,       b.shape[2]						,       a)
         for y in range(a):
              lowercase__   :       Any				=	a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Dict				=	min(a.shape[3]						,       b.shape[3]						,       a)
         for x in range(a):
              lowercase__   :       str				=	a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
         return b





    def 				snake_case_    (				self						,       a						,       a = True):
         lowercase__   :       Optional[int]				=	int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
         lowercase__   :       str				=	int(self.tile_latent_min_size * self.tile_overlap_factor)
         lowercase__   :       Dict				=	self.tile_latent_min_size - blend_extent

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

         lowercase__   :       List[str]				=	torch.cat(a						,       dim=2)
         lowercase__   :       Union[str, Any]				=	DiagonalGaussianDistribution(a)

         if not return_dict:
              return (posterior,)

         return AutoencoderKLOutput(latent_dist=a)





    def 				snake_case_    (				self						,       a						,       a = True):
         lowercase__   :       Optional[Any]				=	int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
         lowercase__   :       List[str]				=	int(self.tile_sample_min_size * self.tile_overlap_factor)
         lowercase__   :       int				=	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.
         lowercase__   :       Optional[Any]				=	[]
         for i in range(0						,       z.shape[2]						,       a):
              lowercase__   :       Union[str, Any]				=	[]
              for j in range(0						,       z.shape[3]						,       a):
                   lowercase__   :       Any				=	z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                   lowercase__   :       Optional[Any]				=	self.post_quant_conv(a)
                   lowercase__   :       Optional[int]				=	self.decoder(a)
                   row.append(a)
              rows.append(a)
         lowercase__   :       Tuple				=	[]
         for i, row in enumerate(a):
              lowercase__   :       List[str]				=	[]
              for j, tile in enumerate(a):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                        lowercase__   :       Optional[int]				=	self.blend_v(rows[i - 1][j]						,       a						,       a)
                   if j > 0:
                        lowercase__   :       Dict				=	self.blend_h(row[j - 1]						,       a						,       a)
                   result_row.append(tile[:, :, :row_limit, :row_limit])
              result_rows.append(torch.cat(a						,       dim=3))

         lowercase__   :       Optional[int]				=	torch.cat(a						,       dim=2)
         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=a)





    def 				snake_case_    (				self						,       a						,       a = False						,       a = True						,       a = None						,       ):
         lowercase__   :       Optional[int]				=	sample
         lowercase__   :       Optional[int]				=	self.encode(a).latent_dist
         if sample_posterior:
              lowercase__   :       List[str]				=	posterior.sample(generator=a)
         else:
              lowercase__   :       List[Any]				=	posterior.mode()
         lowercase__   :       List[Any]				=	self.decode(a).sample

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=a)


import os

try:
       from .build_directory_md import good_file_paths
except ImportError:
       from build_directory_md import good_file_paths  # type: ignore

snake_case_        =			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

snake_case_        =			[file for file in filepaths if file != file.lower()]
if upper_files:
       print(F'''{len(upper_files)} files contain uppercase characters:''')
       print('''\n'''.join(upper_files) + '''\n''')

snake_case_        =			[file for file in filepaths if ''' ''' in file]
if space_files:
       print(F'''{len(space_files)} files contain space characters:''')
       print('''\n'''.join(space_files) + '''\n''')

snake_case_        =			[file for file in filepaths if '''-''' in file]
if hyphen_files:
       print(F'''{len(hyphen_files)} files contain hyphen characters:''')
       print('''\n'''.join(hyphen_files) + '''\n''')

snake_case_        =			[file for file in filepaths if os.sep not in file]
if nodir_files:
       print(F'''{len(nodir_files)} files are not in a directory:''')
       print('''\n'''.join(nodir_files) + '''\n''')

snake_case_        =			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
       import sys

       sys.exit(bad_files)

