


"""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 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):







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

        A					       = nn.Sequential(
            nn.Linear(_lowercase			,			d_model * 4			,			bias=_lowercase		)			,			nn.SiLU()			,			nn.Linear(d_model * 4			,			d_model * 4			,			bias=_lowercase		)			,			nn.SiLU()			,			)

        A					       = nn.Embedding(_lowercase			,			_lowercase		)
        A					       = False

        A					       = nn.Linear(_lowercase			,			_lowercase			,			bias=_lowercase		)

        A					       = nn.Dropout(p=_lowercase		)

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

        A					       = TaLayerNorm(_lowercase		)

        A					       = nn.Dropout(p=_lowercase		)
        A					       = nn.Linear(_lowercase			,			_lowercase			,			bias=_lowercase		)







      def __a						(							self   :    List[Any]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Any		):
        A					       = torch.mul(query_input.unsqueeze(-1		)			,			key_input.unsqueeze(-2		)		)
        return mask.unsqueeze(-3		)







      def __a						(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    List[str]			,			_lowercase   :    Any		):
        A       , A       , A					       = decoder_input_tokens.shape
        assert decoder_noise_time.shape == (batch,)

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

        A					       = self.conditioning_emb(_lowercase		).unsqueeze(1		)

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

        A					       = 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.
        A					       = torch.broadcast_to(
            torch.arange(_lowercase			,			device=decoder_input_tokens.device		)			,			(batch, seq_length)			,			)

        A					       = self.position_encoding(_lowercase		)

        A					       = self.continuous_inputs_projection(_lowercase		)
        inputs += position_encodings
        A					       = self.dropout(_lowercase		)

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

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

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

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

        A					       = self.decoder_norm(_lowercase		)
        A					       = self.post_dropout(_lowercase		)

        A					       = self.spec_out(_lowercase		)
        return spec_out



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    Optional[Any]			,			_lowercase   :    Any			,			_lowercase   :    Any			,			_lowercase   :    Dict			,			_lowercase   :    Optional[int]			,			_lowercase   :    Dict			,			_lowercase   :    Optional[Any]=1e-6		):
        super().__init__()
        A					       = nn.ModuleList()

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

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

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







      def __a						(							self   :    Union[str, Any]			,			_lowercase   :    List[Any]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=None			,			_lowercase   :    List[str]=None			,			_lowercase   :    Optional[int]=None			,			):
        A					       = self.layer[0](
            _lowercase			,			conditioning_emb=_lowercase			,			attention_mask=_lowercase			,			)

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

          A					       = self.layer[1](
              _lowercase			,			key_value_states=_lowercase			,			attention_mask=_lowercase			,			)

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

        return (hidden_states,)



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Tuple			,			_lowercase   :    str		):
        super().__init__()
        A					       = TaLayerNorm(_lowercase		)
        A					       = TaFiLMLayer(in_features=d_model * 4			,			out_features=_lowercase		)
        A					       = Attention(query_dim=_lowercase			,			heads=_lowercase			,			dim_head=_lowercase			,			out_bias=_lowercase			,			scale_qk=_lowercase		)
        A					       = nn.Dropout(_lowercase		)







      def __a						(							self   :    Any			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[str]=None			,			_lowercase   :    str=None			,			):
        # pre_self_attention_layer_norm
        A					       = self.layer_norm(_lowercase		)

        if conditioning_emb is not None:
          A					       = self.FiLMLayer(_lowercase			,			_lowercase		)

        # Self-attention block
        A					       = self.attention(_lowercase		)

        A					       = hidden_states + self.dropout(_lowercase		)

        return hidden_states



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    Optional[int]			,			_lowercase   :    Any			,			_lowercase   :    Any			,			_lowercase   :    Optional[Any]			,			_lowercase   :    List[str]			,			_lowercase   :    int		):
        super().__init__()
        A					       = Attention(query_dim=_lowercase			,			heads=_lowercase			,			dim_head=_lowercase			,			out_bias=_lowercase			,			scale_qk=_lowercase		)
        A					       = TaLayerNorm(_lowercase			,			eps=_lowercase		)
        A					       = nn.Dropout(_lowercase		)







      def __a						(							self   :    Any			,			_lowercase   :    Any			,			_lowercase   :    Any=None			,			_lowercase   :    str=None			,			):
        A					       = self.layer_norm(_lowercase		)
        A					       = self.attention(
            _lowercase			,			encoder_hidden_states=_lowercase			,			attention_mask=attention_mask.squeeze(1		)			,			)
        A					       = hidden_states + self.dropout(_lowercase		)
        return layer_output



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    Tuple			,			_lowercase   :    Dict			,			_lowercase   :    str			,			_lowercase   :    Dict			,			_lowercase   :    Any		):
        super().__init__()
        A					       = TaDenseGatedActDense(d_model=_lowercase			,			d_ff=_lowercase			,			dropout_rate=_lowercase		)
        A					       = TaFiLMLayer(in_features=d_model * 4			,			out_features=_lowercase		)
        A					       = TaLayerNorm(_lowercase			,			eps=_lowercase		)
        A					       = nn.Dropout(_lowercase		)







      def __a						(							self   :    Optional[Any]			,			_lowercase   :    List[Any]			,			_lowercase   :    Any=None		):
        A					       = self.layer_norm(_lowercase		)
        if conditioning_emb is not None:
          A					       = self.film(_lowercase			,			_lowercase		)

        A					       = self.DenseReluDense(_lowercase		)
        A					       = hidden_states + self.dropout(_lowercase		)
        return hidden_states



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    Any			,			_lowercase   :    List[Any]			,			_lowercase   :    Optional[int]			,			_lowercase   :    Union[str, Any]		):
        super().__init__()
        A					       = nn.Linear(_lowercase			,			_lowercase			,			bias=_lowercase		)
        A					       = nn.Linear(_lowercase			,			_lowercase			,			bias=_lowercase		)
        A					       = nn.Linear(_lowercase			,			_lowercase			,			bias=_lowercase		)
        A					       = nn.Dropout(_lowercase		)
        A					       = NewGELUActivation()







      def __a						(							self   :    Dict			,			_lowercase   :    Any		):
        A					       = self.act(self.wi_a(_lowercase		)		)
        A					       = self.wi_a(_lowercase		)
        A					       = hidden_gelu * hidden_linear
        A					       = self.dropout(_lowercase		)

        A					       = self.wo(_lowercase		)
        return hidden_states



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    List[Any]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Optional[Any]=1e-6		):
        super().__init__()
        A					       = nn.Parameter(torch.ones(_lowercase		)		)
        A					       = eps







      def __a						(							self   :    Any			,			_lowercase   :    List[str]		):
        # 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

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

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

        return self.weight * hidden_states



class 			lowerCamelCase__						(   nn.Module			):







      def __a						(							self   :    Optional[int]			,			_lowercase   :    torch.Tensor		):
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.0_4_4_7_1_5 * torch.pow(_lowercase			,			3.0		))		))



class 			lowerCamelCase__						(   nn.Module			):







      def __init__(							self   :    Tuple			,			_lowercase   :    Any			,			_lowercase   :    Optional[int]		):
        super().__init__()
        A					       = nn.Linear(_lowercase			,			out_features * 2			,			bias=_lowercase		)







      def __a						(							self   :    Dict			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Optional[Any]		):
        A					       = self.scale_bias(_lowercase		)
        A       , A					       = torch.chunk(_lowercase			,			2			,			-1		)
        A					       = x * (1 + scale) + shift
        return x









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





