'''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 _SCREAMING_SNAKE_CASE(							lowercase_						,						lowercase_					):




						@register_to_config
						def __init__(    self :       str							,							UpperCamelCase_ :       int = 1_28							,							UpperCamelCase_ :       int = 2_56							,							UpperCamelCase_ :       float = 2000.0							,							UpperCamelCase_ :       int = 7_68							,							UpperCamelCase_ :       int = 12							,							UpperCamelCase_ :       int = 12							,							UpperCamelCase_ :       int = 64							,							UpperCamelCase_ :       int = 20_48							,							UpperCamelCase_ :       float = 0.1							,							)     ->				List[Any]:
													super().__init__()

													SCREAMING_SNAKE_CASE__     :List[str]						    = nn.Sequential(
													    nn.Linear(UpperCamelCase_							,							d_model * 4							,							bias=UpperCamelCase_      )							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=UpperCamelCase_      )							,							nn.SiLU()							,							)

													SCREAMING_SNAKE_CASE__     :List[str]						    = nn.Embedding(UpperCamelCase_							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = False

													SCREAMING_SNAKE_CASE__     :Tuple						    = nn.Linear(UpperCamelCase_							,							UpperCamelCase_							,							bias=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = nn.Dropout(p=UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :List[Any]						    = TaLayerNorm(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = nn.Dropout(p=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = nn.Linear(UpperCamelCase_							,							UpperCamelCase_							,							bias=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       List[str]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.mul(query_input.unsqueeze(-1      )							,							key_input.unsqueeze(-2      )      )
													return mask.unsqueeze(-3      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       List[Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = decoder_input_tokens.shape
													assert decoder_noise_time.shape == (batch,)

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

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.conditioning_emb(UpperCamelCase_      ).unsqueeze(1      )

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

													SCREAMING_SNAKE_CASE__     :Optional[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.
													SCREAMING_SNAKE_CASE__     :Dict						    = torch.broadcast_to(
													    torch.arange(UpperCamelCase_							,							device=decoder_input_tokens.device      )							,							(batch, seq_length)							,							)

													SCREAMING_SNAKE_CASE__     :int						    = self.position_encoding(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.continuous_inputs_projection(UpperCamelCase_      )
													inputs += position_encodings
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.dropout(UpperCamelCase_      )

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

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

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

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

													SCREAMING_SNAKE_CASE__     :str						    = self.decoder_norm(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.post_dropout(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = self.spec_out(UpperCamelCase_      )
													return spec_out




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Any=1e-6      )     ->				Any:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = nn.ModuleList()

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

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

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




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Union[str, Any]=None							,							UpperCamelCase_ :       Optional[Any]=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       str=None							,							)     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Dict						    = self.layer[0](
													    UpperCamelCase_							,							conditioning_emb=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							)

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

																				SCREAMING_SNAKE_CASE__     :Dict						    = self.layer[1](
																				    UpperCamelCase_							,							key_value_states=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							)

													# Apply Film Conditional Feed Forward layer
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.layer[-1](UpperCamelCase_							,							UpperCamelCase_      )

													return (hidden_states,)




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       Tuple							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Union[str, Any]      )     ->				Dict:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = TaLayerNorm(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = TaFiLMLayer(in_features=d_model * 4							,							out_features=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = Attention(query_dim=UpperCamelCase_							,							heads=UpperCamelCase_							,							dim_head=UpperCamelCase_							,							out_bias=UpperCamelCase_							,							scale_qk=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = nn.Dropout(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Optional[int]=None							,							)     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :int						    = self.layer_norm(UpperCamelCase_      )

													if conditioning_emb is not None:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = self.FiLMLayer(UpperCamelCase_							,							UpperCamelCase_      )

													# Self-attention block
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.attention(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_states + self.dropout(UpperCamelCase_      )

													return hidden_states




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       List[Any]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Optional[Any]      )     ->				Optional[Any]:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :str						    = Attention(query_dim=UpperCamelCase_							,							heads=UpperCamelCase_							,							dim_head=UpperCamelCase_							,							out_bias=UpperCamelCase_							,							scale_qk=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = TaLayerNorm(UpperCamelCase_							,							eps=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = nn.Dropout(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Dict=None							,							)     ->				str:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.layer_norm(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.attention(
													    UpperCamelCase_							,							encoder_hidden_states=UpperCamelCase_							,							attention_mask=attention_mask.squeeze(1      )							,							)
													SCREAMING_SNAKE_CASE__     :List[str]						    = hidden_states + self.dropout(UpperCamelCase_      )
													return layer_output




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       str							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       str      )     ->				str:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :List[Any]						    = TaDenseGatedActDense(d_model=UpperCamelCase_							,							d_ff=UpperCamelCase_							,							dropout_rate=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = TaFiLMLayer(in_features=d_model * 4							,							out_features=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = TaLayerNorm(UpperCamelCase_							,							eps=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = nn.Dropout(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[Any]=None      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.layer_norm(UpperCamelCase_      )
													if conditioning_emb is not None:
																				SCREAMING_SNAKE_CASE__     :Any						    = self.film(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = self.DenseReluDense(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = hidden_states + self.dropout(UpperCamelCase_      )
													return hidden_states




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       int							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       int      )     ->				int:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :Tuple						    = nn.Linear(UpperCamelCase_							,							UpperCamelCase_							,							bias=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = nn.Linear(UpperCamelCase_							,							UpperCamelCase_							,							bias=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = nn.Linear(UpperCamelCase_							,							UpperCamelCase_							,							bias=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = nn.Dropout(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = NewGELUActivation()




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :int						    = self.act(self.wi_a(UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.wi_a(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = hidden_gelu * hidden_linear
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.dropout(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = self.wo(UpperCamelCase_      )
													return hidden_states




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Any=1e-6      )     ->				str:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :List[str]						    = nn.Parameter(torch.ones(UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = eps




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       List[str]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = hidden_states.to(torch.floataa      ).pow(2      ).mean(-1							,							keepdim=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :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]:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = hidden_states.to(self.weight.dtype      )

													return self.weight * hidden_states




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       torch.Tensor      )     ->				Union[str, Any]:
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.04_4715 * torch.pow(UpperCamelCase_							,							3.0      ))      ))




class _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       Optional[Any]							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       List[Any]      )     ->				Tuple:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :Dict						    = nn.Linear(UpperCamelCase_							,							out_features * 2							,							bias=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       int      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scale_bias(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.chunk(UpperCamelCase_							,							2							,							-1      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = x * (1 + scale) + shift
													return x














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


__a   :							List[Any]						= logging.get_logger(__name__)

# TODO: upload to AWS
__a   :							Union[str, Any]						= {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"retribert"




     def __init__(					self							:      Any			,						UpperCamelCase_							:      int=30_522			,						UpperCamelCase_							:      Optional[int]=768			,						UpperCamelCase_							:      Union[str, Any]=8			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      List[Any]=3_072			,						UpperCamelCase_							:      Tuple="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      List[str]=512			,						UpperCamelCase_							:      Optional[int]=2			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      List[str]=1e-12			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=128			,						UpperCamelCase_							:      Union[str, Any]=0			,						**UpperCamelCase_							:      List[str]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         __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       =						share_encoders
         __A       =						projection_dim




