



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        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:"DiagonalGaussianDistribution"




class        snake_case__    (A__ ,     A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Dict      =					True

						@register_to_config
						def __init__(       self ,       __lowercase = 3 ,       __lowercase = 3 ,       __lowercase = ("DownEncoderBlock2D",) ,       __lowercase = ("UpDecoderBlock2D",) ,       __lowercase = (6_4,) ,       __lowercase = 1 ,       __lowercase = "silu" ,       __lowercase = 4 ,       __lowercase = 3_2 ,       __lowercase = 3_2 ,       __lowercase = 0.1_8_2_1_5 ,       ) ->				Union[str, Any]:
									"""simple docstring"""




									super().__init__()

									# pass init params to Encoder
									a__       :						Optional[int]							=  Encoder(
									    in_channels=__lowercase ,       out_channels=__lowercase ,       down_block_types=__lowercase ,       block_out_channels=__lowercase ,       layers_per_block=__lowercase ,       act_fn=__lowercase ,       norm_num_groups=__lowercase ,       double_z=__lowercase ,       )

									# pass init params to Decoder
									a__       :						str							=  Decoder(
									    in_channels=__lowercase ,       out_channels=__lowercase ,       up_block_types=__lowercase ,       block_out_channels=__lowercase ,       layers_per_block=__lowercase ,       norm_num_groups=__lowercase ,       act_fn=__lowercase ,       )

									a__       :						Tuple							=  nn.Convad(2 * latent_channels ,       2 * latent_channels ,       1  )
									a__       :						Dict							=  nn.Convad(__lowercase ,       __lowercase ,       1  )

									a__       :						Dict							=  False
									a__       :						List[Any]							=  False

									# only relevant if vae tiling is enabled
									a__       :						int							=  self.config.sample_size
									a__       :						int							=  (
									    self.config.sample_size[0]
									    if isinstance(self.config.sample_size ,       (list, tuple)  )
									    else self.config.sample_size
									)
									a__       :						int							=  int(sample_size / (2 ** (len(self.config.block_out_channels  ) - 1))  )
									a__       :						List[str]							=  0.2_5

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=False  ) ->				List[str]:
									"""simple docstring"""




									if isinstance(__lowercase ,       (Encoder, Decoder)  ):
												a__       :						List[Any]							=  value

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase = True  ) ->				Dict:
									"""simple docstring"""




									a__       :						Tuple							=  use_tiling

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									self.enable_tiling(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  True

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Any							=  False

						@property
						# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict[str, AttentionProcessor]:
									"""simple docstring"""




									a__       :						List[Any]							=  {}

									def fn_recursive_add_processors(__lowercase ,       __lowercase ,       __lowercase  ):
												if hasattr(__lowercase ,       """set_processor"""  ):
															a__       :						Optional[int]							=  module.processor

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

												return processors

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

									return processors

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						List[str]							=  len(self.attn_processors.keys()  )

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

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

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									self.set_attn_processor(AttnProcessor()  )

						@apply_forward_hook
						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = True  ) ->				AutoencoderKLOutput:
									"""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(__lowercase ,       return_dict=__lowercase  )

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

									a__       :						str							=  self.quant_conv(__lowercase  )
									a__       :						List[str]							=  DiagonalGaussianDistribution(__lowercase  )

									if not return_dict:
												return (posterior,)

									return AutoencoderKLOutput(latent_dist=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = True  ) ->				Union[DecoderOutput, torch.FloatTensor]:
									"""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(__lowercase ,       return_dict=__lowercase  )

									a__       :						List[Any]							=  self.post_quant_conv(__lowercase  )
									a__       :						Dict							=  self.decoder(__lowercase  )

									if not return_dict:
												return (dec,)

									return DecoderOutput(sample=__lowercase  )

						@apply_forward_hook
						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = True  ) ->				Union[DecoderOutput, torch.FloatTensor]:
									"""simple docstring"""




									if self.use_slicing and z.shape[0] > 1:
												a__       :						Union[str, Any]							=  [self._decode(__lowercase  ).sample for z_slice in z.split(1  )]
												a__       :						Dict							=  torch.cat(__lowercase  )
									else:
												a__       :						Tuple							=  self._decode(__lowercase  ).sample

									if not return_dict:
												return (decoded,)

									return DecoderOutput(sample=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Dict:
									"""simple docstring"""




									a__       :						Optional[Any]							=  min(a.shape[2] ,       b.shape[2] ,       __lowercase  )
									for y in range(__lowercase  ):
												a__       :						int							=  a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = True  ) ->				AutoencoderKLOutput:
									"""simple docstring"""




									a__       :						Any							=  int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)  )
									a__       :						int							=  int(self.tile_latent_min_size * self.tile_overlap_factor  )
									a__       :						Tuple							=  self.tile_latent_min_size - blend_extent

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

									a__       :						List[Any]							=  torch.cat(__lowercase ,       dim=2  )
									a__       :						str							=  DiagonalGaussianDistribution(__lowercase  )

									if not return_dict:
												return (posterior,)

									return AutoencoderKLOutput(latent_dist=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = True  ) ->				Union[DecoderOutput, torch.FloatTensor]:
									"""simple docstring"""




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

									a__       :						str							=  torch.cat(__lowercase ,       dim=2  )
									if not return_dict:
												return (dec,)

									return DecoderOutput(sample=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = False ,       __lowercase = True ,       __lowercase = None ,       ) ->				Union[DecoderOutput, torch.FloatTensor]:
									"""simple docstring"""




									a__       :						Tuple							=  sample
									a__       :						Optional[int]							=  self.encode(__lowercase  ).latent_dist
									if sample_posterior:
												a__       :						List[str]							=  posterior.sample(generator=__lowercase  )
									else:
												a__       :						int							=  posterior.mode()
									a__       :						str							=  self.decode(__lowercase  ).sample

									if not return_dict:
												return (dec,)

									return DecoderOutput(sample=__lowercase  )









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




