


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    UpperCAmelCase_	(							__a,     __a):
       '''simple docstring'''






       @register_to_config
       def __init__(     self ,					__SCREAMING_SNAKE_CASE = 128 ,					__SCREAMING_SNAKE_CASE = 256 ,					__SCREAMING_SNAKE_CASE = 2_000.0 ,					__SCREAMING_SNAKE_CASE = 768 ,					__SCREAMING_SNAKE_CASE = 12 ,					__SCREAMING_SNAKE_CASE = 12 ,					__SCREAMING_SNAKE_CASE = 64 ,					__SCREAMING_SNAKE_CASE = 2_048 ,					__SCREAMING_SNAKE_CASE = 0.1 ,					):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	str   	=		nn.Sequential(
                  nn.Linear(lowerCAmelCase_ ,					d_model * 4 ,					bias=lowerCAmelCase_						) ,					nn.SiLU() ,					nn.Linear(d_model * 4 ,					d_model * 4 ,					bias=lowerCAmelCase_						) ,					nn.SiLU() ,					)

              UpperCamelCase    :	Optional[int]   	=		nn.Embedding(lowerCAmelCase_ ,					lowerCAmelCase_						)
              UpperCamelCase    :	int   	=		False

              UpperCamelCase    :	Union[str, Any]   	=		nn.Linear(lowerCAmelCase_ ,					lowerCAmelCase_ ,					bias=lowerCAmelCase_						)

              UpperCamelCase    :	Tuple   	=		nn.Dropout(p=lowerCAmelCase_						)

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

              UpperCamelCase    :	List[str]   	=		TaLayerNorm(lowerCAmelCase_						)

              UpperCamelCase    :	Optional[int]   	=		nn.Dropout(p=lowerCAmelCase_						)
              UpperCamelCase    :	int   	=		nn.Linear(lowerCAmelCase_ ,					lowerCAmelCase_ ,					bias=lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		torch.mul(query_input.unsqueeze(-1						) ,					key_input.unsqueeze(-2						)						)
              return mask.unsqueeze(-3						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		decoder_input_tokens.shape
              assert decoder_noise_time.shape == (batch,)

              # decoder_noise_time is in [0, 1), so rescale to expected timing range.
              UpperCamelCase    :	Any   	=		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						)

              UpperCamelCase    :	List[Any]   	=		self.conditioning_emb(lowerCAmelCase_						).unsqueeze(1						)

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

              UpperCamelCase    :	List[Any]   	=		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.
              UpperCamelCase    :	Tuple   	=		torch.broadcast_to(
                  torch.arange(lowerCAmelCase_ ,					device=decoder_input_tokens.device						) ,					(batch, seq_length) ,					)

              UpperCamelCase    :	List[Any]   	=		self.position_encoding(lowerCAmelCase_						)

              UpperCamelCase    :	int   	=		self.continuous_inputs_projection(lowerCAmelCase_						)
              inputs += position_encodings
              UpperCamelCase    :	Dict   	=		self.dropout(lowerCAmelCase_						)

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

              # Translate encoding masks to encoder-decoder masks.
              UpperCamelCase    :	Any   	=		[(x, self.encoder_decoder_mask(lowerCAmelCase_ ,					lowerCAmelCase_						)) for x, y in encodings_and_masks]

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

              for lyr in self.decoders:
                     UpperCamelCase    :	Dict   	=		lyr(
                         lowerCAmelCase_ ,					conditioning_emb=lowerCAmelCase_ ,					encoder_hidden_states=lowerCAmelCase_ ,					encoder_attention_mask=lowerCAmelCase_ ,					)[0]

              UpperCamelCase    :	Tuple   	=		self.decoder_norm(lowerCAmelCase_						)
              UpperCamelCase    :	List[str]   	=		self.post_dropout(lowerCAmelCase_						)

              UpperCamelCase    :	Union[str, Any]   	=		self.spec_out(lowerCAmelCase_						)
              return spec_out




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1e-6						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	Tuple   	=		nn.ModuleList()

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

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

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.layer[0](
                  lowerCAmelCase_ ,					conditioning_emb=lowerCAmelCase_ ,					attention_mask=lowerCAmelCase_ ,					)

              if encoder_hidden_states is not None:
                     UpperCamelCase    :	Optional[int]   	=		torch.where(encoder_attention_mask > 0 ,					0 ,					-1e10						).to(
                         encoder_hidden_states.dtype						)

                     UpperCamelCase    :	Tuple   	=		self.layer[1](
                         lowerCAmelCase_ ,					key_value_states=lowerCAmelCase_ ,					attention_mask=lowerCAmelCase_ ,					)

              # Apply Film Conditional Feed Forward layer
              UpperCamelCase    :	int   	=		self.layer[-1](lowerCAmelCase_ ,					lowerCAmelCase_						)

              return (hidden_states,)




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	Optional[int]   	=		TaLayerNorm(lowerCAmelCase_						)
              UpperCamelCase    :	List[Any]   	=		TaFiLMLayer(in_features=d_model * 4 ,					out_features=lowerCAmelCase_						)
              UpperCamelCase    :	List[str]   	=		Attention(query_dim=lowerCAmelCase_ ,					heads=lowerCAmelCase_ ,					dim_head=lowerCAmelCase_ ,					out_bias=lowerCAmelCase_ ,					scale_qk=lowerCAmelCase_						)
              UpperCamelCase    :	Any   	=		nn.Dropout(lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.layer_norm(lowerCAmelCase_						)

              if conditioning_emb is not None:
                     UpperCamelCase    :	Optional[Any]   	=		self.FiLMLayer(lowerCAmelCase_ ,					lowerCAmelCase_						)

              # Self-attention block
              UpperCamelCase    :	List[Any]   	=		self.attention(lowerCAmelCase_						)

              UpperCamelCase    :	int   	=		hidden_states + self.dropout(lowerCAmelCase_						)

              return hidden_states




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	Union[str, Any]   	=		Attention(query_dim=lowerCAmelCase_ ,					heads=lowerCAmelCase_ ,					dim_head=lowerCAmelCase_ ,					out_bias=lowerCAmelCase_ ,					scale_qk=lowerCAmelCase_						)
              UpperCamelCase    :	str   	=		TaLayerNorm(lowerCAmelCase_ ,					eps=lowerCAmelCase_						)
              UpperCamelCase    :	List[Any]   	=		nn.Dropout(lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.layer_norm(lowerCAmelCase_						)
              UpperCamelCase    :	List[str]   	=		self.attention(
                  lowerCAmelCase_ ,					encoder_hidden_states=lowerCAmelCase_ ,					attention_mask=attention_mask.squeeze(1						) ,					)
              UpperCamelCase    :	Dict   	=		hidden_states + self.dropout(lowerCAmelCase_						)
              return layer_output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	Tuple   	=		TaDenseGatedActDense(d_model=lowerCAmelCase_ ,					d_ff=lowerCAmelCase_ ,					dropout_rate=lowerCAmelCase_						)
              UpperCamelCase    :	Union[str, Any]   	=		TaFiLMLayer(in_features=d_model * 4 ,					out_features=lowerCAmelCase_						)
              UpperCamelCase    :	str   	=		TaLayerNorm(lowerCAmelCase_ ,					eps=lowerCAmelCase_						)
              UpperCamelCase    :	Optional[int]   	=		nn.Dropout(lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.layer_norm(lowerCAmelCase_						)
              if conditioning_emb is not None:
                     UpperCamelCase    :	Optional[int]   	=		self.film(lowerCAmelCase_ ,					lowerCAmelCase_						)

              UpperCamelCase    :	Optional[int]   	=		self.DenseReluDense(lowerCAmelCase_						)
              UpperCamelCase    :	List[str]   	=		hidden_states + self.dropout(lowerCAmelCase_						)
              return hidden_states




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[str]   	=		nn.Linear(lowerCAmelCase_ ,					lowerCAmelCase_ ,					bias=lowerCAmelCase_						)
              UpperCamelCase    :	Optional[int]   	=		nn.Linear(lowerCAmelCase_ ,					lowerCAmelCase_ ,					bias=lowerCAmelCase_						)
              UpperCamelCase    :	int   	=		nn.Linear(lowerCAmelCase_ ,					lowerCAmelCase_ ,					bias=lowerCAmelCase_						)
              UpperCamelCase    :	int   	=		nn.Dropout(lowerCAmelCase_						)
              UpperCamelCase    :	Optional[int]   	=		NewGELUActivation()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.act(self.wi_a(lowerCAmelCase_						)						)
              UpperCamelCase    :	str   	=		self.wi_a(lowerCAmelCase_						)
              UpperCamelCase    :	Union[str, Any]   	=		hidden_gelu * hidden_linear
              UpperCamelCase    :	Union[str, Any]   	=		self.dropout(lowerCAmelCase_						)

              UpperCamelCase    :	Dict   	=		self.wo(lowerCAmelCase_						)
              return hidden_states




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1e-6						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	Any   	=		nn.Parameter(torch.ones(lowerCAmelCase_						)						)
              UpperCamelCase    :	Optional[Any]   	=		eps






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		hidden_states.to(torch.floataa						).pow(2						).mean(-1 ,					keepdim=lowerCAmelCase_						)
              UpperCamelCase    :	List[Any]   	=		hidden_states * torch.rsqrt(variance + self.variance_epsilon						)

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

              return self.weight * hidden_states




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.044_715 * torch.pow(lowerCAmelCase_ ,					3.0						))						))




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[str]   	=		nn.Linear(lowerCAmelCase_ ,					out_features * 2 ,					bias=lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.scale_bias(lowerCAmelCase_						)
              UpperCamelCase	, UpperCamelCase    :	Optional[Any]   	=		torch.chunk(lowerCAmelCase_ ,					2 ,					-1						)
              UpperCamelCase    :	str   	=		x * (1 + scale) + shift
              return x





from __future__ import annotations


def      a	(							SCREAMING_SNAKE_CASE_      :		list[int]       ):






       """simple docstring"""





       if len(SCREAMING_SNAKE_CASE_       ) == 0:
              return array

       UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		min(SCREAMING_SNAKE_CASE_       ), max(SCREAMING_SNAKE_CASE_       )

       # Compute the variables
       UpperCamelCase    :	Union[str, Any]   	=		_max - _min + 1
       UpperCamelCase	, UpperCamelCase    :	Optional[Any]   	=		[0] * holes_range, [0] * holes_range

       # Make the sorting.
       for i in array:
              UpperCamelCase    :	Optional[int]   	=		i - _min
              UpperCamelCase    :	Any   	=		i
              holes_repeat[index] += 1

       # Makes the array back by replacing the numbers.
       UpperCamelCase    :	str   	=		0
       for i in range(SCREAMING_SNAKE_CASE_       ):
              while holes_repeat[i] > 0:
                     UpperCamelCase    :	List[Any]   	=		holes[i]
                     index += 1
                     holes_repeat[i] -= 1

    # Returns the sorted array.
       return array


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase : Any 			=				input("Enter numbers separated by comma:\n")
  __UpperCAmelCase : int 			=				[int(x) for x in user_input.split(",")]
  print(pigeon_sort(unsorted))

