"""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 					_A		(			lowerCAmelCase    ,       lowerCAmelCase      ):

      @register_to_config
      def __init__(		self						,			__lowerCAmelCase = 128						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 2_0_0_0.0						,			__lowerCAmelCase = 768						,			__lowerCAmelCase = 12						,			__lowerCAmelCase = 12						,			__lowerCAmelCase = 64						,			__lowerCAmelCase = 2048						,			__lowerCAmelCase = 0.1						,			):







            """simple docstring"""



            super().__init__()

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

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

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

            lowercase								=      nn.Dropout(p=__lowerCAmelCase		)

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

            lowercase								=      TaLayerNorm(__lowerCAmelCase		)

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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      torch.mul(query_input.unsqueeze(-1		)						,			key_input.unsqueeze(-2		)		)
            return mask.unsqueeze(-3		)







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            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(__lowerCAmelCase		).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(__lowerCAmelCase						,			device=decoder_input_tokens.device		)						,			(batch, seq_length)						,			)

            lowercase								=      self.position_encoding(__lowerCAmelCase		)

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

            # 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(__lowerCAmelCase						,			__lowerCAmelCase		)) 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(
                      __lowerCAmelCase						,			conditioning_emb=__lowerCAmelCase						,			encoder_hidden_states=__lowerCAmelCase						,			encoder_attention_mask=__lowerCAmelCase						,			)[0]

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

            lowercase								=      self.spec_out(__lowerCAmelCase		)
            return spec_out






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase=1E-6		):







            """simple docstring"""



            super().__init__()
            lowercase								=      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 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



            lowercase								=      self.layer[0](
                __lowerCAmelCase						,			conditioning_emb=__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			)

            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](
                      __lowerCAmelCase						,			key_value_states=__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			)

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

            return (hidden_states,)






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



            lowercase								=      self.layer_norm(__lowerCAmelCase		)

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

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

            lowercase								=      hidden_states + self.dropout(__lowerCAmelCase		)

            return hidden_states






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            lowercase								=      Attention(query_dim=__lowerCAmelCase						,			heads=__lowerCAmelCase						,			dim_head=__lowerCAmelCase						,			out_bias=__lowerCAmelCase						,			scale_qk=__lowerCAmelCase		)
            lowercase								=      TaLayerNorm(__lowerCAmelCase						,			eps=__lowerCAmelCase		)
            lowercase								=      nn.Dropout(__lowerCAmelCase		)







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



            lowercase								=      self.layer_norm(__lowerCAmelCase		)
            lowercase								=      self.attention(
                __lowerCAmelCase						,			encoder_hidden_states=__lowerCAmelCase						,			attention_mask=attention_mask.squeeze(1		)						,			)
            lowercase								=      hidden_states + self.dropout(__lowerCAmelCase		)
            return layer_output






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None		):







            """simple docstring"""



            lowercase								=      self.layer_norm(__lowerCAmelCase		)
            if conditioning_emb is not None:
                  lowercase								=      self.film(__lowerCAmelCase						,			__lowerCAmelCase		)

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






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            lowercase								=      nn.Linear(__lowerCAmelCase						,			__lowerCAmelCase						,			bias=__lowerCAmelCase		)
            lowercase								=      nn.Linear(__lowerCAmelCase						,			__lowerCAmelCase						,			bias=__lowerCAmelCase		)
            lowercase								=      nn.Linear(__lowerCAmelCase						,			__lowerCAmelCase						,			bias=__lowerCAmelCase		)
            lowercase								=      nn.Dropout(__lowerCAmelCase		)
            lowercase								=      NewGELUActivation()







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.act(self.wi_a(__lowerCAmelCase		)		)
            lowercase								=      self.wi_a(__lowerCAmelCase		)
            lowercase								=      hidden_gelu * hidden_linear
            lowercase								=      self.dropout(__lowerCAmelCase		)

            lowercase								=      self.wo(__lowerCAmelCase		)
            return hidden_states






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=1E-6		):







            """simple docstring"""



            super().__init__()
            lowercase								=      nn.Parameter(torch.ones(__lowerCAmelCase		)		)
            lowercase								=      eps







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      hidden_states.to(torch.floataa		).pow(2		).mean(-1						,			keepdim=__lowerCAmelCase		)
            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 					_A		(			nn.Module      ):

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.0_4_4_7_1_5 * torch.pow(__lowerCAmelCase						,			3.0		))		))






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            lowercase								=      nn.Linear(__lowerCAmelCase						,			out_features * 2						,			bias=__lowerCAmelCase		)







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.scale_bias(__lowerCAmelCase		)
            lowercase				,				lowercase								=      torch.chunk(__lowerCAmelCase						,			2						,			-1		)
            lowercase								=      x * (1 + scale) + shift
            return x




"""simple docstring"""






from copy import deepcopy





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if arr is None and size is not None:
                  lowercase								=      size
                  lowercase								=      [0] * size
            elif arr is not None:
                  self.init(__lowerCAmelCase		)
            else:
                  raise ValueError("""Either arr or size must be specified"""		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      deepcopy(__lowerCAmelCase		)
            for i in range(1						,			self.size		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        self.tree[j] += self.tree[i]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.tree[:]
            for i in range(self.size - 1						,			0						,			-1		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        arr[j] -= arr[i]
            return arr

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index + (index & (-index))

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index - (index & (-index))

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if index == 0:
                  self.tree[0] += value
                  return
            while index < self.size:
                  self.tree[index] += value
                  lowercase								=      self.next_(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            self.add(__lowerCAmelCase						,			value - self.get(__lowerCAmelCase		)		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if right == 0:
                  return 0
            lowercase								=      self.tree[0]
            right -= 1  # make right inclusive
            while right > 0:
                  result += self.tree[right]
                  lowercase								=      self.prev(__lowerCAmelCase		)
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.prefix(__lowerCAmelCase		) - self.prefix(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.query(__lowerCAmelCase						,			index + 1		)







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            value -= self.tree[0]
            if value < 0:
                  return -1

            lowercase								=      1  # Largest power of 2 <= size
            while j * 2 < self.size:
                  j *= 2

            lowercase								=      0

            while j > 0:
                  if i + j < self.size and self.tree[i + j] <= value:
                        value -= self.tree[i + j]
                        i += j
                  j //= 2
            return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()



