




'''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 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     "DiagonalGaussianDistribution"







class 					_a (a__,		a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					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							,)      ->				Optional[Any]:
          super().__init__()

          # pass init params to Encoder
          snake_case			:      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
          snake_case			:      str        =			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							,)

          snake_case			:      Any        =			nn.Convad(2 * latent_channels							,2 * latent_channels							,1							)
          snake_case			:      Dict        =			nn.Convad(__a							,__a							,1							)

          snake_case			:      str        =			False
          snake_case			:      List[str]        =			False

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







     def 			snake_case_      (				self							,__a							,__a=False							)      ->				List[str]:
          if isinstance(__a							,(Encoder, Decoder)							):
               snake_case			:      int        =			value







     def 			snake_case_      (				self							,__a = True							)      ->				str:
          snake_case			:      Optional[Any]        =			use_tiling







     def 			snake_case_      (				self							)      ->				List[Any]:
          self.enable_tiling(__a							)







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      Tuple        =			True







     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      Tuple        =			False







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

          def fn_recursive_add_processors(__a							,__a							,__a							):
               if hasattr(__a							,"""set_processor"""							):
                    snake_case			:      Tuple        =			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							)      ->				Optional[int]:
          snake_case			:      Any        =			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							)      ->				List[Any]:
          self.set_attn_processor(AttnProcessor()							)







     @apply_forward_hook
     def 			snake_case_      (				self							,__a							,__a = 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(__a							,return_dict=__a							)

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

          snake_case			:      List[Any]        =			self.quant_conv(__a							)
          snake_case			:      Optional[int]        =			DiagonalGaussianDistribution(__a							)

          if not return_dict:
               return (posterior,)

          return AutoencoderKLOutput(latent_dist=__a							)







     def 			snake_case_      (				self							,__a							,__a = 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(__a							,return_dict=__a							)

          snake_case			:      Optional[Any]        =			self.post_quant_conv(__a							)
          snake_case			:      Union[str, Any]        =			self.decoder(__a							)

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=__a							)







     @apply_forward_hook
     def 			snake_case_      (				self							,__a							,__a = True							)      ->				Union[DecoderOutput, torch.FloatTensor]:
          if self.use_slicing and z.shape[0] > 1:
               snake_case			:      Any        =			[self._decode(__a							).sample for z_slice in z.split(1							)]
               snake_case			:      Optional[int]        =			torch.cat(__a							)
          else:
               snake_case			:      Optional[int]        =			self._decode(__a							).sample

          if not return_dict:
               return (decoded,)

          return DecoderOutput(sample=__a							)







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







     def 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Union[str, Any]:
          snake_case			:      List[Any]        =			min(a.shape[3]							,b.shape[3]							,__a							)
          for x in range(__a							):
               snake_case			:      Tuple        =			a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
          return b







     def 			snake_case_      (				self							,__a							,__a = True							)      ->				AutoencoderKLOutput:
          snake_case			:      List[str]        =			int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)							)
          snake_case			:      Dict        =			int(self.tile_latent_min_size * self.tile_overlap_factor							)
          snake_case			:      List[Any]        =			self.tile_latent_min_size - blend_extent

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

          snake_case			:      str        =			torch.cat(__a							,dim=2							)
          snake_case			:      str        =			DiagonalGaussianDistribution(__a							)

          if not return_dict:
               return (posterior,)

          return AutoencoderKLOutput(latent_dist=__a							)







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

          snake_case			:      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							,)      ->				Union[DecoderOutput, torch.FloatTensor]:
          snake_case			:      List[str]        =			sample
          snake_case			:      str        =			self.encode(__a							).latent_dist
          if sample_posterior:
               snake_case			:      int        =			posterior.sample(generator=__a							)
          else:
               snake_case			:      str        =			posterior.mode()
          snake_case			:      str        =			self.decode(__a							).sample

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=__a							)









'''simple docstring'''







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

      logger.info(F"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase      :   int      =       pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       v

      logger.info(F"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



