


'''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 					lowercase_     (    a__  ,						a__   ):


    @register_to_config
    def __init__(     self  ,					a = 1_28  ,					a = 2_56  ,					a = 2000.0  ,					a = 7_68  ,					a = 12  ,					a = 12  ,					a = 64  ,					a = 20_48  ,					a = 0.1  ,					):
          super().__init__()

          UpperCamelCase__										=				nn.Sequential(
              nn.Linear(a  ,					d_model * 4  ,					bias=a    )  ,					nn.SiLU()  ,					nn.Linear(d_model * 4  ,					d_model * 4  ,					bias=a    )  ,					nn.SiLU()  ,					)

          UpperCamelCase__										=				nn.Embedding(a  ,					a    )
          UpperCamelCase__										=				False

          UpperCamelCase__										=				nn.Linear(a  ,					a  ,					bias=a    )

          UpperCamelCase__										=				nn.Dropout(p=a    )

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

          UpperCamelCase__										=				TaLayerNorm(a    )

          UpperCamelCase__										=				nn.Dropout(p=a    )
          UpperCamelCase__										=				nn.Linear(a  ,					a  ,					bias=a    )


    def    __a							(     self  ,					a  ,					a    ):
          UpperCamelCase__										=				torch.mul(query_input.unsqueeze(-1    )  ,					key_input.unsqueeze(-2    )    )
          return mask.unsqueeze(-3    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				decoder_input_tokens.shape
          assert decoder_noise_time.shape == (batch,)

          # decoder_noise_time is in [0, 1), so rescale to expected timing range.
          UpperCamelCase__										=				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__										=				self.conditioning_emb(a    ).unsqueeze(1    )

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

          UpperCamelCase__										=				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__										=				torch.broadcast_to(
              torch.arange(a  ,					device=decoder_input_tokens.device    )  ,					(batch, seq_length)  ,					)

          UpperCamelCase__										=				self.position_encoding(a    )

          UpperCamelCase__										=				self.continuous_inputs_projection(a    )
          inputs += position_encodings
          UpperCamelCase__										=				self.dropout(a    )

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

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

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

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

          UpperCamelCase__										=				self.decoder_norm(a    )
          UpperCamelCase__										=				self.post_dropout(a    )

          UpperCamelCase__										=				self.spec_out(a    )
          return spec_out

class 					lowercase_     (    nn.Module   ):


    def __init__(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a=1e-6    ):
          super().__init__()
          UpperCamelCase__										=				nn.ModuleList()

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

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

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


    def    __a							(     self  ,					a  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					):
          UpperCamelCase__										=				self.layer[0](
              a  ,					conditioning_emb=a  ,					attention_mask=a  ,					)

          if encoder_hidden_states is not None:
                UpperCamelCase__										=				torch.where(encoder_attention_mask > 0  ,					0  ,					-1e10    ).to(
                    encoder_hidden_states.dtype    )

                UpperCamelCase__										=				self.layer[1](
                    a  ,					key_value_states=a  ,					attention_mask=a  ,					)

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

          return (hidden_states,)

class 					lowercase_     (    nn.Module   ):


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


    def    __a							(     self  ,					a  ,					a=None  ,					a=None  ,					):
          # pre_self_attention_layer_norm
          UpperCamelCase__										=				self.layer_norm(a    )

          if conditioning_emb is not None:
                UpperCamelCase__										=				self.FiLMLayer(a  ,					a    )

          # Self-attention block
          UpperCamelCase__										=				self.attention(a    )

          UpperCamelCase__										=				hidden_states + self.dropout(a    )

          return hidden_states

class 					lowercase_     (    nn.Module   ):


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


    def    __a							(     self  ,					a  ,					a=None  ,					a=None  ,					):
          UpperCamelCase__										=				self.layer_norm(a    )
          UpperCamelCase__										=				self.attention(
              a  ,					encoder_hidden_states=a  ,					attention_mask=attention_mask.squeeze(1    )  ,					)
          UpperCamelCase__										=				hidden_states + self.dropout(a    )
          return layer_output

class 					lowercase_     (    nn.Module   ):


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


    def    __a							(     self  ,					a  ,					a=None    ):
          UpperCamelCase__										=				self.layer_norm(a    )
          if conditioning_emb is not None:
                UpperCamelCase__										=				self.film(a  ,					a    )

          UpperCamelCase__										=				self.DenseReluDense(a    )
          UpperCamelCase__										=				hidden_states + self.dropout(a    )
          return hidden_states

class 					lowercase_     (    nn.Module   ):


    def __init__(     self  ,					a  ,					a  ,					a    ):
          super().__init__()
          UpperCamelCase__										=				nn.Linear(a  ,					a  ,					bias=a    )
          UpperCamelCase__										=				nn.Linear(a  ,					a  ,					bias=a    )
          UpperCamelCase__										=				nn.Linear(a  ,					a  ,					bias=a    )
          UpperCamelCase__										=				nn.Dropout(a    )
          UpperCamelCase__										=				NewGELUActivation()


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				self.act(self.wi_a(a    )    )
          UpperCamelCase__										=				self.wi_a(a    )
          UpperCamelCase__										=				hidden_gelu * hidden_linear
          UpperCamelCase__										=				self.dropout(a    )

          UpperCamelCase__										=				self.wo(a    )
          return hidden_states

class 					lowercase_     (    nn.Module   ):


    def __init__(     self  ,					a  ,					a=1e-6    ):
          super().__init__()
          UpperCamelCase__										=				nn.Parameter(torch.ones(a    )    )
          UpperCamelCase__										=				eps


    def    __a							(     self  ,					a    ):
          # 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

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

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

          return self.weight * hidden_states

class 					lowercase_     (    nn.Module   ):


    def    __a							(     self  ,					a    ):
          return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.04_4715 * torch.pow(a  ,					3.0    ))    ))

class 					lowercase_     (    nn.Module   ):


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


    def    __a							(     self  ,					a  ,					a    ):
          UpperCamelCase__										=				self.scale_bias(a    )
          UpperCamelCase__					,	UpperCamelCase__										=				torch.chunk(a  ,					2  ,					-1    )
          UpperCamelCase__										=				x * (1 + scale) + shift
          return x












def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			int	):
   return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase_	) - ngram_size + 1	)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()

