




"""simple docstring"""

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 						_snake_case					( _snake_case							: str							)   -> None:






      '''simple docstring'''


      _A       ,   _A        =       analyze_text(_snake_case							)
      _A        =       list(' ' + ascii_lowercase							)
      # what is our total sum of probabilities.
      _A        =       sum(single_char_strings.values()							)

      # one length string
      _A        =       0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  _A        =       single_char_strings[ch]
                  _A        =       my_str / all_sum
                  my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

                   # print entropy
      print(F'''{round(-1 * my_fir_sum							):.1f}'''							)

      # two len string
      _A        =       sum(two_char_strings.values()							)
      _A        =       0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  _A        =       cha + cha
                  if sequence in two_char_strings:
                        _A        =       two_char_strings[sequence]
                        _A        =       int(_snake_case							) / all_sum
                        my_sec_sum += prob * math.loga(_snake_case							)

                        # print second entropy
      print(F'''{round(-1 * my_sec_sum							):.1f}'''							)

      # print the difference between them
      print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}'''							)






def 						_snake_case					( _snake_case							: str							)   -> tuple[dict, dict]:






      '''simple docstring'''


      _A        =       Counter()  # type: ignore
      _A        =       Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0    ,  len(_snake_case							) - 1							):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings






def 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
       main()





"""simple docstring"""

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a         =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def 						_snake_case					( _snake_case							: Dict							)   -> int:






      '''simple docstring'''


      if isinstance(_snake_case    ,  _snake_case							):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError('boolean value expected'							)






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: Tuple    ,  _snake_case							: Dict    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]=False							)   -> List[str]:






      '''simple docstring'''


      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint






def 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: int=None							)   -> Optional[int]:






      '''simple docstring'''


      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,  dim=0							)
      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,  dim=0							)

      _A        =       checkpoint[F'''{old_prefix}.norm.weight''']
      _A        =       checkpoint[F'''{old_prefix}.norm.bias''']

      _A        =       weight_q.squeeze(-1							).squeeze(-1							)
      _A        =       bias_q.squeeze(-1							).squeeze(-1							)
      _A        =       weight_k.squeeze(-1							).squeeze(-1							)
      _A        =       bias_k.squeeze(-1							).squeeze(-1							)
      _A        =       weight_v.squeeze(-1							).squeeze(-1							)
      _A        =       bias_v.squeeze(-1							).squeeze(-1							)

      _A        =       (
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
      )
      _A        =       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

      return new_checkpoint






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Any							)   -> str:






      '''simple docstring'''


      _A        =       torch.load(_snake_case    ,  map_location='cpu'							)
      _A        =       {}

      _A        =       checkpoint['time_embed.0.weight']
      _A        =       checkpoint['time_embed.0.bias']
      _A        =       checkpoint['time_embed.2.weight']
      _A        =       checkpoint['time_embed.2.bias']

      if unet_config["num_class_embeds"] is not None:
            _A        =       checkpoint['label_emb.weight']

      _A        =       checkpoint['input_blocks.0.0.weight']
      _A        =       checkpoint['input_blocks.0.0.bias']

      _A        =       unet_config['down_block_types']
      _A        =       unet_config['layers_per_block']
      _A        =       unet_config['attention_head_dim']
      _A        =       unet_config['block_out_channels']
      _A        =       1
      _A        =       channels_list[0]

      for i, layer_type in enumerate(_snake_case							):
            _A        =       channels_list[i]
            _A        =       current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''down_blocks.{i}.attentions.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

            if i != len(_snake_case							) - 1:
                  _A        =       F'''down_blocks.{i}.downsamplers.0'''
                  _A        =       F'''input_blocks.{current_layer}.0'''
                  _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                  current_layer += 1

            _A        =       current_channels

      # hardcoded the mid-block for now
      _A        =       'mid_block.resnets.0'
      _A        =       'middle_block.0'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.attentions.0'
      _A        =       'middle_block.1'
      _A        =       convert_attention(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.resnets.1'
      _A        =       'middle_block.2'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       0
      _A        =       unet_config['up_block_types']

      for i, layer_type in enumerate(_snake_case							):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.1'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''up_blocks.{i}.attentions.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.2'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       checkpoint['out.0.weight']
      _A        =       checkpoint['out.0.bias']
      _A        =       checkpoint['out.2.weight']
      _A        =       checkpoint['out.2.bias']

      return new_checkpoint


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()

       parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
       parser.add_argument(
           '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
       )
       parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

       a         =      parser.parse_args()
       a         =      strabool(args.class_cond)

       a         =      os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              a         =      IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              a         =      TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              a         =      None

       a         =      con_pt_to_diffuser(args.unet_path, unet_config)

       a         =      UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              a         =      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              a         =      CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       a         =      CMStochasticIterativeScheduler(**scheduler_config)

       a         =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)

