from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	torch.FloatTensor



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 6_5536   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = 2   ,				SCREAMING_SNAKE_CASE_ = 2   ,				SCREAMING_SNAKE_CASE_ = 0   ,				SCREAMING_SNAKE_CASE_ = "fourier"   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = 0.0   ,				SCREAMING_SNAKE_CASE_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,				SCREAMING_SNAKE_CASE_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,				SCREAMING_SNAKE_CASE_ = "UNetMidBlock1D"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = (32, 32, 64)   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = 8   ,				SCREAMING_SNAKE_CASE_ = 1   ,				SCREAMING_SNAKE_CASE_ = False   ,				)		->      List[Any]:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      sample_size

       # time
       if time_embedding_type == "fourier":
            UpperCamelCase__										=      GaussianFourierProjection(
                embedding_size=8   ,				set_W_to_weight=SCREAMING_SNAKE_CASE_   ,				log=SCREAMING_SNAKE_CASE_   ,				flip_sin_to_cos=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      2 * block_out_channels[0]
       elif time_embedding_type == "positional":
            UpperCamelCase__										=      Timesteps(
                block_out_channels[0]   ,				flip_sin_to_cos=SCREAMING_SNAKE_CASE_   ,				downscale_freq_shift=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      block_out_channels[0]

       if use_timestep_embedding:
            UpperCamelCase__										=      block_out_channels[0] * 4
            UpperCamelCase__										=      TimestepEmbedding(
                in_channels=SCREAMING_SNAKE_CASE_   ,				time_embed_dim=SCREAMING_SNAKE_CASE_   ,				act_fn=SCREAMING_SNAKE_CASE_   ,				out_dim=block_out_channels[0]   ,				)

       UpperCamelCase__										=      nn.ModuleList([]	)
       UpperCamelCase__										=      None
       UpperCamelCase__										=      nn.ModuleList([]	)
       UpperCamelCase__										=      None

       # down
       UpperCamelCase__										=      in_channels
       for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      output_channel
            UpperCamelCase__										=      block_out_channels[i]

            if i == 0:
                 input_channel += extra_in_channels

            UpperCamelCase__										=      i == len(SCREAMING_SNAKE_CASE_	) - 1

            UpperCamelCase__										=      get_down_block(
                SCREAMING_SNAKE_CASE_   ,				num_layers=SCREAMING_SNAKE_CASE_   ,				in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				temb_channels=block_out_channels[0]   ,				add_downsample=not is_final_block or downsample_each_block   ,				)
            self.down_blocks.append(SCREAMING_SNAKE_CASE_	)

       # mid
       UpperCamelCase__										=      get_mid_block(
           SCREAMING_SNAKE_CASE_   ,				in_channels=block_out_channels[-1]   ,				mid_channels=block_out_channels[-1]   ,				out_channels=block_out_channels[-1]   ,				embed_dim=block_out_channels[0]   ,				num_layers=SCREAMING_SNAKE_CASE_   ,				add_downsample=SCREAMING_SNAKE_CASE_   ,				)

       # up
       UpperCamelCase__										=      list(reversed(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      reversed_block_out_channels[0]
       if out_block_type is None:
            UpperCamelCase__										=      out_channels
       else:
            UpperCamelCase__										=      block_out_channels[0]

       for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      output_channel
            UpperCamelCase__										=      (
                reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_	) - 1 else final_upsample_channels
            )

            UpperCamelCase__										=      i == len(SCREAMING_SNAKE_CASE_	) - 1

            UpperCamelCase__										=      get_up_block(
                SCREAMING_SNAKE_CASE_   ,				num_layers=SCREAMING_SNAKE_CASE_   ,				in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				temb_channels=block_out_channels[0]   ,				add_upsample=not is_final_block   ,				)
            self.up_blocks.append(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      output_channel

       # out
       UpperCamelCase__										=      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,				32	)
       UpperCamelCase__										=      get_out_block(
           out_block_type=SCREAMING_SNAKE_CASE_   ,				num_groups_out=SCREAMING_SNAKE_CASE_   ,				embed_dim=block_out_channels[0]   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				act_fn=SCREAMING_SNAKE_CASE_   ,				fc_dim=block_out_channels[-1] // 4   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True   ,				)		->      Union[UNetaDOutput, Tuple]:





       '''simple docstring'''





       UpperCamelCase__										=      timestep
       if not torch.is_tensor(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      torch.tensor([timesteps]   ,				dtype=torch.long   ,				device=sample.device	)
       elif torch.is_tensor(SCREAMING_SNAKE_CASE_	) and len(timesteps.shape	) == 0:
            UpperCamelCase__										=      timesteps[None].to(sample.device	)

       UpperCamelCase__										=      self.time_proj(SCREAMING_SNAKE_CASE_	)
       if self.config.use_timestep_embedding:
            UpperCamelCase__										=      self.time_mlp(SCREAMING_SNAKE_CASE_	)
       else:
            UpperCamelCase__										=      timestep_embed[..., None]
            UpperCamelCase__										=      timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
            UpperCamelCase__										=      timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

       # 2. down
       UpperCamelCase__										=      ()
       for downsample_block in self.down_blocks:
            UpperCamelCase__						,       UpperCamelCase__										=      downsample_block(hidden_states=SCREAMING_SNAKE_CASE_   ,				temb=SCREAMING_SNAKE_CASE_	)
            down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
            UpperCamelCase__										=      self.mid_block(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       # 4. up
       for i, upsample_block in enumerate(self.up_blocks	):
            UpperCamelCase__										=      down_block_res_samples[-1:]
            UpperCamelCase__										=      down_block_res_samples[:-1]
            UpperCamelCase__										=      upsample_block(SCREAMING_SNAKE_CASE_   ,				res_hidden_states_tuple=SCREAMING_SNAKE_CASE_   ,				temb=SCREAMING_SNAKE_CASE_	)

       # 5. post-process
       if self.out_block:
            UpperCamelCase__										=      self.out_block(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (sample,)

       return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_	)




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      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:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      my_str / all_sum
               my_fir_sum += prob * math.loga(A							)  # entropy formula.

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

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

    # 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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      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(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     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()



