
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     (      snake_case_			,       snake_case_						):



	@register_to_config
	def __init__(    self						,  lowercase = 128						,  lowercase = 256						,  lowercase = 2_0_0_0.0						,  lowercase = 768						,  lowercase = 12						,  lowercase = 12						,  lowercase = 64						,  lowercase = 2048						,  lowercase = 0.1						,  )				->       str:
					super().__init__()

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

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

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

					lowerCamelCase_						=			nn.Dropout(p=lowercase )

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

					lowerCamelCase_						=			TaLayerNorm(lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Optional[int]:
					lowerCamelCase_						=			torch.mul(query_input.unsqueeze(-1 )						,  key_input.unsqueeze(-2 ) )
					return mask.unsqueeze(-3 )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       int:
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

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

					lowerCamelCase_						=			self.conditioning_emb(lowercase ).unsqueeze(1 )

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

					lowerCamelCase_						=			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.
					lowerCamelCase_						=			torch.broadcast_to(
					    torch.arange(lowercase						,  device=decoder_input_tokens.device )						,  (batch, seq_length)						,  )

					lowerCamelCase_						=			self.position_encoding(lowercase )

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

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

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

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

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

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

					lowerCamelCase_						=			self.spec_out(lowercase )
					return spec_out




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase=1e-6 )				->       Tuple:
					super().__init__()
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  )				->       List[Any]:
					lowerCamelCase_						=			self.layer[0](
					    lowercase						,  conditioning_emb=lowercase						,  attention_mask=lowercase						,  )

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

									lowerCamelCase_						=			self.layer[1](
									    lowercase						,  key_value_states=lowercase						,  attention_mask=lowercase						,  )

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

					return (hidden_states,)




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Tuple:
					super().__init__()
					lowerCamelCase_						=			TaLayerNorm(lowercase )
					lowerCamelCase_						=			TaFiLMLayer(in_features=d_model * 4						,  out_features=lowercase )
					lowerCamelCase_						=			Attention(query_dim=lowercase						,  heads=lowercase						,  dim_head=lowercase						,  out_bias=lowercase						,  scale_qk=lowercase )
					lowerCamelCase_						=			nn.Dropout(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=None						,  lowercase=None						,  )				->       Optional[int]:
					# pre_self_attention_layer_norm
					lowerCamelCase_						=			self.layer_norm(lowercase )

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

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

					lowerCamelCase_						=			hidden_states + self.dropout(lowercase )

					return hidden_states




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       List[str]:
					super().__init__()
					lowerCamelCase_						=			Attention(query_dim=lowercase						,  heads=lowercase						,  dim_head=lowercase						,  out_bias=lowercase						,  scale_qk=lowercase )
					lowerCamelCase_						=			TaLayerNorm(lowercase						,  eps=lowercase )
					lowerCamelCase_						=			nn.Dropout(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=None						,  lowercase=None						,  )				->       Dict:
					lowerCamelCase_						=			self.layer_norm(lowercase )
					lowerCamelCase_						=			self.attention(
					    lowercase						,  encoder_hidden_states=lowercase						,  attention_mask=attention_mask.squeeze(1 )						,  )
					lowerCamelCase_						=			hidden_states + self.dropout(lowercase )
					return layer_output




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Tuple:
					super().__init__()
					lowerCamelCase_						=			TaDenseGatedActDense(d_model=lowercase						,  d_ff=lowercase						,  dropout_rate=lowercase )
					lowerCamelCase_						=			TaFiLMLayer(in_features=d_model * 4						,  out_features=lowercase )
					lowerCamelCase_						=			TaLayerNorm(lowercase						,  eps=lowercase )
					lowerCamelCase_						=			nn.Dropout(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=None )				->       Optional[Any]:
					lowerCamelCase_						=			self.layer_norm(lowercase )
					if conditioning_emb is not None:
									lowerCamelCase_						=			self.film(lowercase						,  lowercase )

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




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase )				->       List[Any]:
					super().__init__()
					lowerCamelCase_						=			nn.Linear(lowercase						,  lowercase						,  bias=lowercase )
					lowerCamelCase_						=			nn.Linear(lowercase						,  lowercase						,  bias=lowercase )
					lowerCamelCase_						=			nn.Linear(lowercase						,  lowercase						,  bias=lowercase )
					lowerCamelCase_						=			nn.Dropout(lowercase )
					lowerCamelCase_						=			NewGELUActivation()



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			self.act(self.wi_a(lowercase ) )
					lowerCamelCase_						=			self.wi_a(lowercase )
					lowerCamelCase_						=			hidden_gelu * hidden_linear
					lowerCamelCase_						=			self.dropout(lowercase )

					lowerCamelCase_						=			self.wo(lowercase )
					return hidden_states




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase=1e-6 )				->       Tuple:
					super().__init__()
					lowerCamelCase_						=			nn.Parameter(torch.ones(lowercase ) )
					lowerCamelCase_						=			eps



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       int:
					# 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

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

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

					return self.weight * hidden_states




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def     SCREAMING_SNAKE_CASE_(    self						,  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     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase )				->       Union[str, Any]:
					super().__init__()
					lowerCamelCase_						=			nn.Linear(lowercase						,  out_features * 2						,  bias=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       str:
					lowerCamelCase_						=			self.scale_bias(lowercase )
					lowerCamelCase_	,    lowerCamelCase_						=			torch.chunk(lowercase						,  2						,  -1 )
					lowerCamelCase_						=			x * (1 + scale) + shift
					return x




def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[0 for i in range(r + 1    )]
				# nc0 = 1
				lowerCamelCase_						=			1
				for i in range(1	, n + 1    ):
								# to compute current row from previous row.
								lowerCamelCase_						=			min(lowerCamelCase__	, lowerCamelCase__    )
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]





print(binomial_coefficient(n=1_0, r=5))


