




"""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 			_snake_case					(				__snake_case				,    __snake_case							):

				'''simple docstring'''




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

								UpperCAmelCase_       :Any         =   nn.Sequential(
								    nn.Linear(snake_case       ,				d_model * 4       ,				bias=snake_case  )       ,				nn.SiLU()       ,				nn.Linear(d_model * 4       ,				d_model * 4       ,				bias=snake_case  )       ,				nn.SiLU()       ,				)

								UpperCAmelCase_       :Dict         =   nn.Embedding(snake_case       ,				snake_case  )
								UpperCAmelCase_       :List[str]         =   False

								UpperCAmelCase_       :List[Any]         =   nn.Linear(snake_case       ,				snake_case       ,				bias=snake_case  )

								UpperCAmelCase_       :List[Any]         =   nn.Dropout(p=snake_case  )

								UpperCAmelCase_       :str         =   nn.ModuleList()
								for lyr_num in range(snake_case  ):
												# FiLM conditional T5 decoder
												UpperCAmelCase_       :List[str]         =   DecoderLayer(d_model=snake_case       ,				d_kv=snake_case       ,				num_heads=snake_case       ,				d_ff=snake_case       ,				dropout_rate=snake_case  )
												self.decoders.append(snake_case  )

								UpperCAmelCase_       :Optional[Any]         =   TaLayerNorm(snake_case  )

								UpperCAmelCase_       :Tuple         =   nn.Dropout(p=snake_case  )
								UpperCAmelCase_       :Optional[Any]         =   nn.Linear(snake_case       ,				snake_case       ,				bias=snake_case  )





				def 					snake_case_			(				self   :	Dict       ,				snake_case   :	List[Any]       ,				snake_case   :	Dict  ):
								UpperCAmelCase_       :List[Any]         =   torch.mul(query_input.unsqueeze(-1  )       ,				key_input.unsqueeze(-2  )  )
								return mask.unsqueeze(-3  )





				def 					snake_case_			(				self   :	List[Any]       ,				snake_case   :	int       ,				snake_case   :	Any       ,				snake_case   :	str  ):
								UpperCAmelCase_       :Any         =   decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								UpperCAmelCase_       :Dict         =   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_       :Tuple         =   self.conditioning_emb(snake_case  ).unsqueeze(1  )

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

								UpperCAmelCase_       :List[str]         =   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_       :List[str]         =   torch.broadcast_to(
								    torch.arange(snake_case       ,				device=decoder_input_tokens.device  )       ,				(batch, seq_length)       ,				)

								UpperCAmelCase_       :int         =   self.position_encoding(snake_case  )

								UpperCAmelCase_       :Union[str, Any]         =   self.continuous_inputs_projection(snake_case  )
								inputs += position_encodings
								UpperCAmelCase_       :Optional[int]         =   self.dropout(snake_case  )

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

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

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

								for lyr in self.decoders:
												UpperCAmelCase_       :List[Any]         =   lyr(
												    snake_case       ,				conditioning_emb=snake_case       ,				encoder_hidden_states=snake_case       ,				encoder_attention_mask=snake_case       ,				)[0]

								UpperCAmelCase_       :Tuple         =   self.decoder_norm(snake_case  )
								UpperCAmelCase_       :Optional[int]         =   self.post_dropout(snake_case  )

								UpperCAmelCase_       :List[str]         =   self.spec_out(snake_case  )
								return spec_out



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




				def __init__(				self   :	Optional[Any]       ,				snake_case   :	Optional[Any]       ,				snake_case   :	Tuple       ,				snake_case   :	Optional[Any]       ,				snake_case   :	List[str]       ,				snake_case   :	List[Any]       ,				snake_case   :	Any=1e-6  ):
								super().__init__()
								UpperCAmelCase_       :List[Any]         =   nn.ModuleList()

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

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

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





				def 					snake_case_			(				self   :	Union[str, Any]       ,				snake_case   :	Optional[int]       ,				snake_case   :	Any=None       ,				snake_case   :	List[str]=None       ,				snake_case   :	int=None       ,				snake_case   :	List[Any]=None       ,				snake_case   :	List[Any]=None       ,				):
								UpperCAmelCase_       :Tuple         =   self.layer[0](
								    snake_case       ,				conditioning_emb=snake_case       ,				attention_mask=snake_case       ,				)

								if encoder_hidden_states is not None:
												UpperCAmelCase_       :List[Any]         =   torch.where(encoder_attention_mask > 0       ,				0       ,				-1e10  ).to(
												    encoder_hidden_states.dtype  )

												UpperCAmelCase_       :Tuple         =   self.layer[1](
												    snake_case       ,				key_value_states=snake_case       ,				attention_mask=snake_case       ,				)

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

								return (hidden_states,)



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




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





				def 					snake_case_			(				self   :	List[str]       ,				snake_case   :	Tuple       ,				snake_case   :	Optional[int]=None       ,				snake_case   :	str=None       ,				):
								# pre_self_attention_layer_norm
								UpperCAmelCase_       :Any         =   self.layer_norm(snake_case  )

								if conditioning_emb is not None:
												UpperCAmelCase_       :Any         =   self.FiLMLayer(snake_case       ,				snake_case  )

								# Self-attention block
								UpperCAmelCase_       :str         =   self.attention(snake_case  )

								UpperCAmelCase_       :str         =   hidden_states + self.dropout(snake_case  )

								return hidden_states



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




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





				def 					snake_case_			(				self   :	Tuple       ,				snake_case   :	List[str]       ,				snake_case   :	Union[str, Any]=None       ,				snake_case   :	int=None       ,				):
								UpperCAmelCase_       :Optional[Any]         =   self.layer_norm(snake_case  )
								UpperCAmelCase_       :List[str]         =   self.attention(
								    snake_case       ,				encoder_hidden_states=snake_case       ,				attention_mask=attention_mask.squeeze(1  )       ,				)
								UpperCAmelCase_       :List[str]         =   hidden_states + self.dropout(snake_case  )
								return layer_output



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




				def __init__(				self   :	int       ,				snake_case   :	Optional[int]       ,				snake_case   :	List[str]       ,				snake_case   :	Optional[Any]       ,				snake_case   :	Tuple  ):
								super().__init__()
								UpperCAmelCase_       :Optional[int]         =   TaDenseGatedActDense(d_model=snake_case       ,				d_ff=snake_case       ,				dropout_rate=snake_case  )
								UpperCAmelCase_       :Dict         =   TaFiLMLayer(in_features=d_model * 4       ,				out_features=snake_case  )
								UpperCAmelCase_       :List[str]         =   TaLayerNorm(snake_case       ,				eps=snake_case  )
								UpperCAmelCase_       :Any         =   nn.Dropout(snake_case  )





				def 					snake_case_			(				self   :	List[Any]       ,				snake_case   :	Dict       ,				snake_case   :	List[str]=None  ):
								UpperCAmelCase_       :str         =   self.layer_norm(snake_case  )
								if conditioning_emb is not None:
												UpperCAmelCase_       :Optional[int]         =   self.film(snake_case       ,				snake_case  )

								UpperCAmelCase_       :List[Any]         =   self.DenseReluDense(snake_case  )
								UpperCAmelCase_       :List[Any]         =   hidden_states + self.dropout(snake_case  )
								return hidden_states



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




				def __init__(				self   :	List[str]       ,				snake_case   :	Dict       ,				snake_case   :	int       ,				snake_case   :	List[str]  ):
								super().__init__()
								UpperCAmelCase_       :Dict         =   nn.Linear(snake_case       ,				snake_case       ,				bias=snake_case  )
								UpperCAmelCase_       :str         =   nn.Linear(snake_case       ,				snake_case       ,				bias=snake_case  )
								UpperCAmelCase_       :Any         =   nn.Linear(snake_case       ,				snake_case       ,				bias=snake_case  )
								UpperCAmelCase_       :str         =   nn.Dropout(snake_case  )
								UpperCAmelCase_       :str         =   NewGELUActivation()





				def 					snake_case_			(				self   :	Union[str, Any]       ,				snake_case   :	List[str]  ):
								UpperCAmelCase_       :List[Any]         =   self.act(self.wi_a(snake_case  )  )
								UpperCAmelCase_       :Optional[int]         =   self.wi_a(snake_case  )
								UpperCAmelCase_       :str         =   hidden_gelu * hidden_linear
								UpperCAmelCase_       :int         =   self.dropout(snake_case  )

								UpperCAmelCase_       :Dict         =   self.wo(snake_case  )
								return hidden_states



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




				def __init__(				self   :	List[str]       ,				snake_case   :	Union[str, Any]       ,				snake_case   :	Tuple=1e-6  ):
								super().__init__()
								UpperCAmelCase_       :int         =   nn.Parameter(torch.ones(snake_case  )  )
								UpperCAmelCase_       :Union[str, Any]         =   eps





				def 					snake_case_			(				self   :	Tuple       ,				snake_case   :	Union[str, Any]  ):
								# 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_       :str         =   hidden_states.to(torch.floataa  ).pow(2  ).mean(-1       ,				keepdim=snake_case  )
								UpperCAmelCase_       :Optional[int]         =   hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

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

								return self.weight * hidden_states



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




				def 					snake_case_			(				self   :	int       ,				snake_case   :	torch.Tensor  ):
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044_715 * torch.pow(snake_case       ,				3.0  ))  ))



class 			_snake_case					(				nn.Module							):

				'''simple docstring'''




				def __init__(				self   :	Optional[Any]       ,				snake_case   :	Optional[Any]       ,				snake_case   :	List[str]  ):
								super().__init__()
								UpperCAmelCase_       :Optional[Any]         =   nn.Linear(snake_case       ,				out_features * 2       ,				bias=snake_case  )





				def 					snake_case_			(				self   :	List[str]       ,				snake_case   :	Any       ,				snake_case   :	List[str]  ):
								UpperCAmelCase_       :Optional[Any]         =   self.scale_bias(snake_case  )
								UpperCAmelCase_       :List[str]         =   torch.chunk(snake_case       ,				2       ,				-1  )
								UpperCAmelCase_       :Tuple         =   x * (1 + scale) + shift
								return x




from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




