

'''simple docstring'''
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ):







 @register_to_config
 def __init__(       self  , snake_case_ = 1_28  , snake_case_ = 2_56  , snake_case_ = 20_00.0  , snake_case_ = 7_68  , snake_case_ = 12  , snake_case_ = 12  , snake_case_ = 64  , snake_case_ = 20_48  , snake_case_ = 0.1  , ):
   super().__init__()

   lowercase									=nn.Sequential(
       nn.Linear(snake_case_  , d_model * 4  , bias=snake_case_   )  , nn.SiLU()  , nn.Linear(d_model * 4  , d_model * 4  , bias=snake_case_   )  , nn.SiLU()  , )

   lowercase									=nn.Embedding(snake_case_  , snake_case_   )
   lowercase									=False

   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )

   lowercase									=nn.Dropout(p=snake_case_   )

   lowercase									=nn.ModuleList()
   for lyr_num in range(snake_case_   ):
     # FiLM conditional T5 decoder
     lowercase									=DecoderLayer(d_model=snake_case_  , d_kv=snake_case_  , num_heads=snake_case_  , d_ff=snake_case_  , dropout_rate=snake_case_   )
     self.decoders.append(snake_case_   )

   lowercase									=TaLayerNorm(snake_case_   )

   lowercase									=nn.Dropout(p=snake_case_   )
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_   ):
   lowercase									=torch.mul(query_input.unsqueeze(-1   )  , key_input.unsqueeze(-2   )   )
   return mask.unsqueeze(-3   )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase							, lowercase							, lowercase									=decoder_input_tokens.shape
   assert decoder_noise_time.shape == (batch,)

   # decoder_noise_time is in [0, 1), so rescale to expected timing range.
   lowercase									=get_timestep_embedding(
       decoder_noise_time * self.config.max_decoder_noise_time  , embedding_dim=self.config.d_model  , max_period=self.config.max_decoder_noise_time  , ).to(dtype=self.dtype   )

   lowercase									=self.conditioning_emb(snake_case_   ).unsqueeze(1   )

   assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

   lowercase									=decoder_input_tokens.shape[1]

   # If we want to use relative positions for audio context, we can just offset
   # this sequence by the length of encodings_and_masks.
   lowercase									=torch.broadcast_to(
       torch.arange(snake_case_  , device=decoder_input_tokens.device   )  , (batch, seq_length)  , )

   lowercase									=self.position_encoding(snake_case_   )

   lowercase									=self.continuous_inputs_projection(snake_case_   )
   inputs += position_encodings
   lowercase									=self.dropout(snake_case_   )

   # decoder: No padding present.
   lowercase									=torch.ones(
       decoder_input_tokens.shape[:2]  , device=decoder_input_tokens.device  , dtype=inputs.dtype   )

   # Translate encoding masks to encoder-decoder masks.
   lowercase									=[(x, self.encoder_decoder_mask(snake_case_  , snake_case_   )) for x, y in encodings_and_masks]

   # cross attend style: concat encodings
   lowercase									=torch.cat([x[0] for x in encodings_and_encdec_masks]  , dim=1   )
   lowercase									=torch.cat([x[1] for x in encodings_and_encdec_masks]  , dim=-1   )

   for lyr in self.decoders:
     lowercase									=lyr(
         snake_case_  , conditioning_emb=snake_case_  , encoder_hidden_states=snake_case_  , encoder_attention_mask=snake_case_  , )[0]

   lowercase									=self.decoder_norm(snake_case_   )
   lowercase									=self.post_dropout(snake_case_   )

   lowercase									=self.spec_out(snake_case_   )
   return spec_out

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_=1E-6   ):
   super().__init__()
   lowercase									=nn.ModuleList()

   # cond self attention: layer 0
   self.layer.append(
       TaLayerSelfAttentionCond(d_model=snake_case_  , d_kv=snake_case_  , num_heads=snake_case_  , dropout_rate=snake_case_   )   )

   # cross attention: layer 1
   self.layer.append(
       TaLayerCrossAttention(
           d_model=snake_case_  , d_kv=snake_case_  , num_heads=snake_case_  , dropout_rate=snake_case_  , layer_norm_epsilon=snake_case_  , )   )

   # Film Cond MLP + dropout: last layer
   self.layer.append(
       TaLayerFFCond(d_model=snake_case_  , d_ff=snake_case_  , dropout_rate=snake_case_  , layer_norm_epsilon=snake_case_   )   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=None  , snake_case_=None  , snake_case_=None  , snake_case_=None  , ):
   lowercase									=self.layer[0](
       snake_case_  , conditioning_emb=snake_case_  , attention_mask=snake_case_  , )

   if encoder_hidden_states is not None:
     lowercase									=torch.where(encoder_attention_mask > 0  , 0  , -1E10   ).to(
         encoder_hidden_states.dtype   )

     lowercase									=self.layer[1](
         snake_case_  , key_value_states=snake_case_  , attention_mask=snake_case_  , )

   # Apply Film Conditional Feed Forward layer
   lowercase									=self.layer[-1](snake_case_  , snake_case_   )

   return (hidden_states,)

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=TaLayerNorm(snake_case_   )
   lowercase									=TaFiLMLayer(in_features=d_model * 4  , out_features=snake_case_   )
   lowercase									=Attention(query_dim=snake_case_  , heads=snake_case_  , dim_head=snake_case_  , out_bias=snake_case_  , scale_qk=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=None  , ):
   # pre_self_attention_layer_norm
   lowercase									=self.layer_norm(snake_case_   )

   if conditioning_emb is not None:
     lowercase									=self.FiLMLayer(snake_case_  , snake_case_   )

   # Self-attention block
   lowercase									=self.attention(snake_case_   )

   lowercase									=hidden_states + self.dropout(snake_case_   )

   return hidden_states

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=Attention(query_dim=snake_case_  , heads=snake_case_  , dim_head=snake_case_  , out_bias=snake_case_  , scale_qk=snake_case_   )
   lowercase									=TaLayerNorm(snake_case_  , eps=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=None  , ):
   lowercase									=self.layer_norm(snake_case_   )
   lowercase									=self.attention(
       snake_case_  , encoder_hidden_states=snake_case_  , attention_mask=attention_mask.squeeze(1   )  , )
   lowercase									=hidden_states + self.dropout(snake_case_   )
   return layer_output

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=TaDenseGatedActDense(d_model=snake_case_  , d_ff=snake_case_  , dropout_rate=snake_case_   )
   lowercase									=TaFiLMLayer(in_features=d_model * 4  , out_features=snake_case_   )
   lowercase									=TaLayerNorm(snake_case_  , eps=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None   ):
   lowercase									=self.layer_norm(snake_case_   )
   if conditioning_emb is not None:
     lowercase									=self.film(snake_case_  , snake_case_   )

   lowercase									=self.DenseReluDense(snake_case_   )
   lowercase									=hidden_states + self.dropout(snake_case_   )
   return hidden_states

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )
   lowercase									=nn.Linear(snake_case_  , snake_case_  , bias=snake_case_   )
   lowercase									=nn.Dropout(snake_case_   )
   lowercase									=NewGELUActivation()







 def 						_A(       self  , snake_case_   ):
   lowercase									=self.act(self.wi_a(snake_case_   )   )
   lowercase									=self.wi_a(snake_case_   )
   lowercase									=hidden_gelu * hidden_linear
   lowercase									=self.dropout(snake_case_   )

   lowercase									=self.wo(snake_case_   )
   return hidden_states

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_=1E-6   ):
   super().__init__()
   lowercase									=nn.Parameter(torch.ones(snake_case_   )   )
   lowercase									=eps







 def 						_A(       self  , snake_case_   ):
   # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
   # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
   # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
   # half-precision inputs is done in fp32

   lowercase									=hidden_states.to(torch.floataa   ).pow(2   ).mean(-1  , keepdim=snake_case_   )
   lowercase									=hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

   # convert into half-precision if necessary
   if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     lowercase									=hidden_states.to(self.weight.dtype   )

   return self.weight * hidden_states

class 						__magic_name__   (							nn.Module ):







 def 						_A(       self  , snake_case_   ):
   return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_47_15 * torch.pow(snake_case_  , 3.0   ))   ))

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_  , snake_case_   ):
   super().__init__()
   lowercase									=nn.Linear(snake_case_  , out_features * 2  , bias=snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_   ):
   lowercase									=self.scale_bias(snake_case_   )
   lowercase							, lowercase									=torch.chunk(snake_case_  , 2  , -1   )
   lowercase									=x * (1 + scale) + shift
   return x








import itertools
import string
from collections.abc import Generator, Iterable





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       iter(__snake_case							)
     while True:
          __lowerCAmelCase             =       tuple(itertools.islice(__snake_case       ,			__snake_case							)							)
          if not chunk:
               return
          yield chunk





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       "".join([c.upper() for c in dirty if c in string.ascii_letters]							)
     __lowerCAmelCase             =       ""

     if len(__snake_case							) < 2:
          return dirty

     for i in range(len(__snake_case							) - 1							):
          clean += dirty[i]

          if dirty[i] == dirty[i + 1]:
               clean += "X"

     clean += dirty[-1]

     if len(__snake_case							) & 1:
          clean += "X"

     return clean





def    __lowerCAmelCase   (    __snake_case							):
     # I and J are used interchangeably to allow
     # us to use a 5x5 table (25 letters)
     __lowerCAmelCase             =       "ABCDEFGHIKLMNOPQRSTUVWXYZ"
     # we're using a list instead of a '2d' array because it makes the math
     # for setting up the table and doing the actual encoding/decoding simpler
     __lowerCAmelCase             =       []

     # copy key chars into the table if they are in `alphabet` ignoring duplicates
     for char in key.upper():
          if char not in table and char in alphabet:
               table.append(__snake_case							)

    # fill the rest of the table in with the remaining alphabet chars
     for char in alphabet:
          if char not in table:
               table.append(__snake_case							)

     return table





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       prepare_input(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
          elif cola == cola:
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
          else:  # rectangle
               ciphertext += table[rowa * 5 + cola]
               ciphertext += table[rowa * 5 + cola]

     return ciphertext





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               plaintext += table[rowa * 5 + (cola - 1) % 5]
               plaintext += table[rowa * 5 + (cola - 1) % 5]
          elif cola == cola:
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
          else:  # rectangle
               plaintext += table[rowa * 5 + cola]
               plaintext += table[rowa * 5 + cola]

     return plaintext






