


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        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					"DiagonalGaussianDistribution"







class        __lowercase       (							a_			,							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Union[str, 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 = 0.18215		,      )  -> List[Any]:







					'''simple docstring'''







					super().__init__()

					# pass init params to Encoder
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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		,      )

					lowerCamelCase    		=  nn.Convad(2 * latent_channels		,      2 * latent_channels		,      1	)
					lowerCamelCase    		=  nn.Convad(A		,      A		,      1	)

					lowerCamelCase    		=  False
					lowerCamelCase    		=  False

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







		def 					__A   ( self		,      A		,      A=False	)  -> List[str]:







					'''simple docstring'''







					if isinstance(A		,      (Encoder, Decoder)	):
								lowerCamelCase    		=  value







		def 					__A   ( self		,      A = True	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  use_tiling







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					self.enable_tiling(A	)







		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  True







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  False







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







					'''simple docstring'''







					lowerCamelCase    		=  {}

					def fn_recursive_add_processors(A		,      A		,      A	):
								if hasattr(A		,      """set_processor"""	):
											lowerCamelCase    		=  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 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  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 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					self.set_attn_processor(AttnProcessor()	)







		@apply_forward_hook
		def 					__A   ( self		,      A		,      A = 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(A		,      return_dict=A	)

					if self.use_slicing and x.shape[0] > 1:
								lowerCamelCase    		=  [self.encoder(A	) for x_slice in x.split(1	)]
								lowerCamelCase    		=  torch.cat(A	)
					else:
								lowerCamelCase    		=  self.encoder(A	)

					lowerCamelCase    		=  self.quant_conv(A	)
					lowerCamelCase    		=  DiagonalGaussianDistribution(A	)

					if not return_dict:
								return (posterior,)

					return AutoencoderKLOutput(latent_dist=A	)







		def 					__A   ( self		,      A		,      A = 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(A		,      return_dict=A	)

					lowerCamelCase    		=  self.post_quant_conv(A	)
					lowerCamelCase    		=  self.decoder(A	)

					if not return_dict:
								return (dec,)

					return DecoderOutput(sample=A	)







		@apply_forward_hook
		def 					__A   ( self		,      A		,      A = True	)  -> Union[DecoderOutput, torch.FloatTensor]:







					'''simple docstring'''







					if self.use_slicing and z.shape[0] > 1:
								lowerCamelCase    		=  [self._decode(A	).sample for z_slice in z.split(1	)]
								lowerCamelCase    		=  torch.cat(A	)
					else:
								lowerCamelCase    		=  self._decode(A	).sample

					if not return_dict:
								return (decoded,)

					return DecoderOutput(sample=A	)







		def 					__A   ( self		,      A		,      A		,      A	)  -> Optional[int]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A		,      A	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  min(a.shape[3]		,      b.shape[3]		,      A	)
					for x in range(A	):
								lowerCamelCase    		=  a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
					return b







		def 					__A   ( self		,      A		,      A = True	)  -> AutoencoderKLOutput:







					'''simple docstring'''







					lowerCamelCase    		=  int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
					lowerCamelCase    		=  int(self.tile_latent_min_size * self.tile_overlap_factor	)
					lowerCamelCase    		=  self.tile_latent_min_size - blend_extent

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

					lowerCamelCase    		=  torch.cat(A		,      dim=2	)
					lowerCamelCase    		=  DiagonalGaussianDistribution(A	)

					if not return_dict:
								return (posterior,)

					return AutoencoderKLOutput(latent_dist=A	)







		def 					__A   ( self		,      A		,      A = True	)  -> Union[DecoderOutput, torch.FloatTensor]:







					'''simple docstring'''







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

					lowerCamelCase    		=  torch.cat(A		,      dim=2	)
					if not return_dict:
								return (dec,)

					return DecoderOutput(sample=A	)







		def 					__A   ( self		,      A		,      A = False		,      A = True		,      A = None		,      )  -> Union[DecoderOutput, torch.FloatTensor]:







					'''simple docstring'''







					lowerCamelCase    		=  sample
					lowerCamelCase    		=  self.encode(A	).latent_dist
					if sample_posterior:
								lowerCamelCase    		=  posterior.sample(generator=A	)
					else:
								lowerCamelCase    		=  posterior.mode()
					lowerCamelCase    		=  self.decode(A	).sample

					if not return_dict:
								return (dec,)

					return DecoderOutput(sample=A	)





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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

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

